From 41b446957a8a401ea2647c5d0920f7f677e384f6 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Thu, 1 Dec 2022 13:31:00 -0800 Subject: [PATCH 01/31] overhauling whole project to use new libraries --- .gitmodules | 6 + libs/architectures/poetry.lock | 201 ++- libs/architectures/pyproject.toml | 4 +- libs/export/deepclean/export.py | 65 - libs/export/poetry.lock | 228 --- libs/export/poetry.toml | 2 - libs/export/pyproject.toml | 23 - libs/export/tests/test_export.py | 64 - libs/gwftools/poetry.toml | 2 - libs/gwftools/pyproject.toml | 20 - libs/signal/deepclean/signal/__init__.py | 3 - libs/signal/deepclean/signal/op.py | 58 - libs/signal/deepclean/signal/scaler.py | 23 - libs/signal/poetry.lock | 223 --- libs/signal/tests/test_filter.py | 71 - libs/signal/tests/test_op.py | 58 - libs/trainer/deepclean/trainer/analysis.py | 39 +- libs/trainer/deepclean/trainer/criterion.py | 277 +-- libs/trainer/deepclean/trainer/dataloader.py | 345 ---- libs/trainer/deepclean/trainer/trainer.py | 305 +--- libs/trainer/deepclean/trainer/utils.py | 206 +++ libs/trainer/deepclean/trainer/viz.py | 84 - libs/trainer/deepclean/trainer/wrapper.py | 13 +- libs/trainer/poetry.lock | 1412 +++++++++++++-- libs/trainer/poetry.toml | 2 - libs/trainer/pyproject.toml | 23 +- .../deepclean/gwftools/__init__.py | 0 .../deepclean/gwftools/channels.py | 0 .../deepclean/gwftools/frames.py | 0 .../deepclean/gwftools/io.py | 0 .../filter.py => utils/deepclean/utils.py} | 30 +- libs/{gwftools => utils}/poetry.lock | 0 libs/{signal => utils}/pyproject.toml | 7 +- .../tests/test_channels.py | 0 libs/{gwftools => utils}/tests/test_frames.py | 0 libs/{gwftools => utils}/tests/test_io.py | 0 ml4gw | 1 + mldatafind | 1 + projects/sandbox/train/poetry.lock | 1583 +++++++++++++++-- projects/sandbox/train/poetry.toml | 2 - projects/sandbox/train/pyproject.toml | 15 +- 41 files changed, 3296 insertions(+), 2100 deletions(-) delete mode 100644 libs/export/deepclean/export.py delete mode 100644 libs/export/poetry.lock delete mode 100644 libs/export/poetry.toml delete mode 100644 libs/export/pyproject.toml delete mode 100644 libs/export/tests/test_export.py delete mode 100644 libs/gwftools/poetry.toml delete mode 100644 libs/gwftools/pyproject.toml delete mode 100644 libs/signal/deepclean/signal/__init__.py delete mode 100644 libs/signal/deepclean/signal/op.py delete mode 100644 libs/signal/deepclean/signal/scaler.py delete mode 100644 libs/signal/poetry.lock delete mode 100644 libs/signal/tests/test_filter.py delete mode 100644 libs/signal/tests/test_op.py delete mode 100644 libs/trainer/deepclean/trainer/dataloader.py create mode 100644 libs/trainer/deepclean/trainer/utils.py delete mode 100644 libs/trainer/deepclean/trainer/viz.py delete mode 100644 libs/trainer/poetry.toml rename libs/{gwftools => utils}/deepclean/gwftools/__init__.py (100%) rename libs/{gwftools => utils}/deepclean/gwftools/channels.py (100%) rename libs/{gwftools => utils}/deepclean/gwftools/frames.py (100%) rename libs/{gwftools => utils}/deepclean/gwftools/io.py (100%) rename libs/{signal/deepclean/signal/filter.py => utils/deepclean/utils.py} (81%) rename libs/{gwftools => utils}/poetry.lock (100%) rename libs/{signal => utils}/pyproject.toml (66%) rename libs/{gwftools => utils}/tests/test_channels.py (100%) rename libs/{gwftools => utils}/tests/test_frames.py (100%) rename libs/{gwftools => utils}/tests/test_io.py (100%) create mode 160000 ml4gw create mode 160000 mldatafind delete mode 100644 projects/sandbox/train/poetry.toml diff --git a/.gitmodules b/.gitmodules index a63ede3..657674b 100644 --- a/.gitmodules +++ b/.gitmodules @@ -1,3 +1,9 @@ [submodule "hermes"] path = hermes url = git@github.com:ML4GW/hermes.git +[submodule "ml4gw"] + path = ml4gw + url = git@github.com:ML4GW/ml4gw.git +[submodule "mldatafind"] + path = mldatafind + url = git@github.com:ML4GW/mldatafind.git diff --git a/libs/architectures/poetry.lock b/libs/architectures/poetry.lock index c327f7a..11f5699 100644 --- a/libs/architectures/poetry.lock +++ b/libs/architectures/poetry.lock @@ -1,6 +1,6 @@ [[package]] name = "atomicwrites" -version = "1.4.0" +version = "1.4.1" description = "Atomic file writes." category = "dev" optional = false @@ -8,25 +8,25 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "attrs" -version = "21.4.0" +version = "22.1.0" description = "Classes Without Boilerplate" category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = ">=3.5" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] +docs = ["furo", "sphinx", "zope-interface", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "cloudpickle"] +tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] [[package]] name = "colorama" -version = "0.4.4" +version = "0.4.6" description = "Cross-platform colored terminal text." category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" [[package]] name = "iniconfig" @@ -36,6 +36,54 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "nvidia-cublas-cu11" +version = "11.10.3.66" +description = "CUBLAS native runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-nvrtc-cu11" +version = "11.7.99" +description = "NVRTC native runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-runtime-cu11" +version = "11.7.99" +description = "CUDA Runtime native Libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cudnn-cu11" +version = "8.5.0.96" +description = "cuDNN runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + [[package]] name = "packaging" version = "21.3" @@ -69,14 +117,14 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" [[package]] name = "pyparsing" -version = "3.0.7" -description = "Python parsing module" +version = "3.0.9" +description = "pyparsing module - Classes and methods to define and execute parsing grammars" category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.6.8" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pytest" @@ -99,6 +147,19 @@ toml = "*" [package.extras] testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +[[package]] +name = "setuptools" +version = "65.6.3" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +category = "main" 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a/libs/export/tests/test_export.py b/libs/export/tests/test_export.py deleted file mode 100644 index 37401c0..0000000 --- a/libs/export/tests/test_export.py +++ /dev/null @@ -1,64 +0,0 @@ -import numpy as np -import pytest -import torch - -from deepclean.export import PrePostDeepClean - - -def test_pre_post_deepclean(): - deepclean = torch.nn.Sequential( - torch.nn.Conv1d(4, 2, 4), torch.nn.ConvTranspose1d(2, 1, 4) - ) - - # models without a num_witnesses attribute - # will raise a ValueError - with pytest.raises(ValueError): - prepost = PrePostDeepClean(deepclean) - - deepclean.num_witnesses = 4 - prepost = PrePostDeepClean(deepclean) - - # make sure that parameters are included in the state dict - assert len(list(deepclean.state_dict().keys())) == 4 - assert len(list(prepost.state_dict().keys())) == 8 - - # make sure that before fitting the - # prepost model's behavior is the same - # as standalone deepclean - x = torch.randn(8, 4, 100) - assert (deepclean(x) == prepost(x)).numpy().all() - - X = np.stack([np.arange(100) + i for i in range(6)]) - y = np.arange(100) - - # X has too many channels right now - with pytest.raises(ValueError): - prepost.fit(X, y) - - X = X[:4] - prepost.fit(X, y) - - # make sure that the parameters didn't get - # changed to vanilla tensors during fitting - assert len(list(prepost.state_dict().keys())) == 8 - - # make sure the fit parameter values are correct - # TODO: should we do something more explicit here? - assert np.isclose( - prepost.input_shift.numpy()[:, 0], X.mean(axis=1), rtol=1e-6 - ).all() - assert np.isclose( - prepost.input_scale.numpy()[:, 0], X.std(axis=1), rtol=1e-6 - ).all() - assert prepost.output_shift.numpy() == y.mean() - assert prepost.output_scale.numpy() == y.std() - - # now run the pre/post steps manually for comparison - expected = (x - prepost.input_shift) / prepost.input_scale - expected = deepclean(expected) - expected = prepost.output_scale * expected + prepost.output_shift - - # ensure that the preprost model performs all steps - with torch.no_grad(): - output = prepost(x).numpy() - assert np.isclose(output, expected.numpy(), rtol=1e-6).all() diff --git a/libs/gwftools/poetry.toml b/libs/gwftools/poetry.toml deleted file mode 100644 index 59c390d..0000000 --- a/libs/gwftools/poetry.toml +++ /dev/null @@ -1,2 +0,0 @@ -[virtualenvs] -create = false diff --git a/libs/gwftools/pyproject.toml b/libs/gwftools/pyproject.toml deleted file mode 100644 index f10f1b4..0000000 --- a/libs/gwftools/pyproject.toml +++ /dev/null @@ -1,20 +0,0 @@ -[tool.poetry] -name = "deepclean.gwftools" -version = "0.0.1" -description = "Utilites for managing gwf file conventions" -authors = ["Alec Gunny "] -# readme = "README.md" -packages = [ - {include = "deepclean"} -] - -[tool.poetry.dependencies] -python = "^3.8,<3.11" -"deepclean.base" = {path = "../base", develop = true} - -[tool.poetry.dev-dependencies] -pytest = "^6.2" - -[build-system] -requires = ["poetry>=1.2"] -build-backend = "poetry.masonry.api" diff --git a/libs/signal/deepclean/signal/__init__.py b/libs/signal/deepclean/signal/__init__.py deleted file mode 100644 index d5a9134..0000000 --- a/libs/signal/deepclean/signal/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .filter import BandpassFilter -from .op import Pipeline -from .scaler import StandardScaler diff --git a/libs/signal/deepclean/signal/op.py b/libs/signal/deepclean/signal/op.py deleted file mode 100644 index 883a163..0000000 --- a/libs/signal/deepclean/signal/op.py +++ /dev/null @@ -1,58 +0,0 @@ -import pickle -from typing import List - - -class Op: - def __call__(self, x, **kwargs): - return x - - def fit(self, X, **kwargs): - return - - def __rshift__(self, other): - if isinstance(other, Pipeline): - pipeline = Pipeline([self] + other.ops) - elif isinstance(other, Op): - pipeline = Pipeline([self, other]) - else: - raise TypeError( - "Can't pipe output of Op {} to type {}".format( - self.__class__.__name__, type(other) - ) - ) - return pipeline - - def write(self, fname): - with open(fname, "wb") as f: - pickle.dump(self, f) - - @classmethod - def load(self, fname): - with open(fname, "rb") as f: - return pickle.load(f) - - -class Pipeline(Op): - def __init__(self, ops: List[Op]): - self.ops = ops - - def __call__(self, x, **kwargs): - for op in self.ops: - x = op(x, **kwargs) - return x - - def fit(self, X, **kwargs): - for op in self.ops: - op.fit(X, **kwargs) - X = op(X) - - def __rshift__(self, other): - if isinstance(other, Pipeline): - pipeline = Pipeline(self.ops + other.ops) - elif isinstance(other, Op): - pipeline = Pipeline(self.ops + [other]) - else: - raise TypeError( - "Can't pipe output of Pipeline to type {}".format(type(other)) - ) - return pipeline diff --git a/libs/signal/deepclean/signal/scaler.py b/libs/signal/deepclean/signal/scaler.py deleted file mode 100644 index 8769481..0000000 --- a/libs/signal/deepclean/signal/scaler.py +++ /dev/null @@ -1,23 +0,0 @@ -from deepclean.signal.op import Op - - 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iterable_type([65])) - with pytest.raises(ValueError): - normalize_frequencies(iterable_type([55]), 65) - - # low > high raises error - with pytest.raises(ValueError): - normalize_frequencies(65, 55) - - # mismatched lengths raises error - with pytest.raises(ValueError): - normalize_frequencies(iterable_type([55, 75]), iterable_type([65])) - - # iterable with one low > high raises error - with pytest.raises(ValueError): - normalize_frequencies(iterable_type([65, 75]), iterable_type([55, 85])) - - # iterable with overlapping bands raises error - with pytest.raises(ValueError): - normalize_frequencies(iterable_type([55, 60]), iterable_type([65, 70])) - - -def validate_at_precision(y, expected, precision): - err = np.abs(y - expected) / np.abs(expected) - assert np.percentile(err, 99) < precision - - -@pytest.mark.parametrize("sample_rate", [512, 4096]) -def test_bandpass_filter(sample_rate): - # set up a signal with two frequency components - x = np.linspace(0, 16, sample_rate * 16) - y_60 = np.sin(60 * 2 * np.pi * x) - y = y_60 + np.sin(80 * 2 * np.pi * x) - - # only look at the middle of the signal to - # ignore filtering edge effects - slc = slice(sample_rate * 2, sample_rate * 14) - - # bandpass one of the frequency components and - # ensure that roughly only one is left - bandpass = BandpassFilter(55, 65, sample_rate) - y_hat = bandpass(y) - validate_at_precision(y_hat[slc], y_60[slc], 1e-5) - - # keep both components and ensure that - # they're both still present - bandpass = BandpassFilter([55, 75], [65, 85], sample_rate) - y_hat = bandpass(y) - validate_at_precision(y_hat[slc], y[slc], 1e-5) diff --git a/libs/signal/tests/test_op.py b/libs/signal/tests/test_op.py deleted file mode 100644 index 3fb36d3..0000000 --- a/libs/signal/tests/test_op.py +++ /dev/null @@ -1,58 +0,0 @@ -import os - -import pytest - -from deepclean.signal.op import Op, Pipeline - - -class AddOne(Op): - def __call__(self, x): - return x + 1 - - -class ParamOp(Op): - def __init__(self, value): - self.value = value - - def __call__(self, x): - return x + self.value - - -@pytest.fixture(scope="session") -def fname(): - yield "op.pkl" - os.remove("op.pkl") - - -def test_op(fname): - op = AddOne() - assert op(4) == 5 - - op.write(fname) - op = AddOne.load(fname) - assert op(4) == 5 - - op2 = ParamOp(3) - assert op2(4) == 7 - - op2.write(fname) - op2 = ParamOp.load(fname) - assert op2(4) == 7 - - pipeline = op >> op2 - assert pipeline(4) == 8 - - pipeline.write(fname) - pipeline = Pipeline.load(fname) - assert pipeline(4) == 8 - - op3 = AddOne() - pipeline2 = pipeline >> op3 - assert pipeline2(4) == 9 - - pipeline2.write(fname) - pipeline2 = Pipeline.load(fname) - assert pipeline2(4) == 9 - - pipeline3 = op3 >> pipeline2 - assert pipeline3(4) == 10 diff --git a/libs/trainer/deepclean/trainer/analysis.py b/libs/trainer/deepclean/trainer/analysis.py index 225ab05..55c1d85 100644 --- a/libs/trainer/deepclean/trainer/analysis.py +++ b/libs/trainer/deepclean/trainer/analysis.py @@ -1,19 +1,46 @@ -from typing import Iterable, Tuple +from pathlib import Path +from typing import Iterable, Optional, Sequence +import h5py import numpy as np import torch -from deepclean.trainer.criterion import TorchWelch +from deepclean.trainer.utils import DataGenerator + + +def write_analysis(group, name, **data): + subgroup = group.create_group(name) + for key, value in data.items(): + subgroup.create_dataset(key, data=value) def analyze_model( - dataset: Iterable[Tuple[torch.Tensor, torch.Tensor]], + output_directory: Path, + nn: torch.nn.Module, + welch: torch.nn.Module, + train_dataset: DataGenerator, + valid_dataset: Optional[DataGenerator] = None, + percentiles: Sequence[float] = [5, 25, 50, 75, 95], +) -> None: + with h5py.File(output_directory / "history.h5", "a") as f: + grads, coheres = analyze_dataset(train_dataset, nn, welch, percentiles) + group = f.create_group("analysis") + group.attrs["percentiles"] = percentiles + write_analysis(group, "train", gradient=grads, coherence=coheres) + + if valid_dataset is not None: + grads, coheres = analyze_dataset( + valid_dataset, nn, welch, percentiles + ) + write_analysis(group, "valid", gradient=grads, coherence=coheres) + + +def analyze_dataset( + dataset: DataGenerator, nn: torch.nn.Module, - sample_rate: float, - fftlength: float = 2, + welch: torch.nn.Module, percentiles: Iterable[float] = [5, 25, 50, 75, 95], ): - welch = TorchWelch(sample_rate, fftlength, average="median", fast=True) coherences, gradients = [], [] for i, (x, y) in enumerate(dataset): if i == 0: diff --git a/libs/trainer/deepclean/trainer/criterion.py b/libs/trainer/deepclean/trainer/criterion.py index defa191..4d1f090 100644 --- a/libs/trainer/deepclean/trainer/criterion.py +++ b/libs/trainer/deepclean/trainer/criterion.py @@ -2,257 +2,12 @@ import numpy as np import torch -import torch.nn as nn -from scipy.signal.spectral import _median_bias -from deepclean.signal.filter import normalize_frequencies +from deepclean.utils import Frequency, normalize_frequencies +from ml4gw.transforms import SpectralDensity -def _validate_shapes(x: torch.Tensor, y: torch.Tensor): - # acceptable combinations of shapes: - # x: time, y: time - # x: channel x time, y: time OR channel x time - # x: batch x channel x time, y: batch x channel x time OR batch x time - if x.shape[-1] != y.shape[-1]: - raise ValueError( - "Time dimensions of x and y tensors must " - "be the same, found {} and {}".format(x.shape[-1], y.shape[-1]) - ) - elif x.ndim == 1 and not y.ndim == 1: - raise ValueError( - "Can't compute cross spectral density of " - "1D tensor x with {}D tensor y".format(y.ndim) - ) - elif x.ndim > 1 and y.ndim == x.ndim: - if not y.shape == x.shape: - raise ValueError( - "If x and y tensors have the same number " - "of dimensions, shapes must fully match. " - "Found shapes {} and {}".format(x.shape, y.shape) - ) - elif x.ndim > 1 and y.ndim != (x.ndim - 1): - raise ValueError( - "Can't compute cross spectral density of " - "tensors with shapes {} and {}".format(x.shape, y.shape) - ) - elif x.ndim > 2 and y.shape[0] != x.shape[0]: - raise ValueError( - "If x is a 3D tensor and y is a 2D tensor, " - "0th batch dimensions must match, but found " - "values {} and {}".format(x.shape[0], y.shape[0]) - ) - - -def _median(x, axis): - return torch.quantile(x, q=0.5, axis=axis) - - -def _fast_csd( - x: torch.Tensor, - nperseg: int, - nstride: int, - window: torch.Tensor, - scale: torch.Tensor, - average: str, - y: Optional[torch.Tensor] = None, -): - # for fast implementation, use the built-in stft - # module along the last dimension and let torch - # handle the heavy lifting under the hood - if x.ndim > 2: - # stft only works on 2D input, so roll the - # channel dimension out along the batch - batch_size = x.shape[0] - num_channels = x.shape[1] - x = x.reshape(-1, x.shape[-1]) - - if y is not None and y.ndim == 3: - y = y.reshape(-1, x.shape[-1]) - else: - batch_size = None - - x = x - x.mean(axis=-1, keepdims=True) - fft = torch.stft( - x, - n_fft=nperseg, - hop_length=nstride, - window=window, - normalized=False, - center=False, - return_complex=True, - ) - if y is not None: - y = y - y.mean(axis=-1, keepdims=True) - y_fft = torch.stft( - y, - n_fft=nperseg, - hop_length=nstride, - window=window, - normalized=False, - center=False, - return_complex=True, - ) - if batch_size is not None and fft.shape[0] > y_fft.shape[0]: - nfreq = nperseg // 2 + 1 - fft = fft.reshape(batch_size, num_channels, nfreq, -1) - y_fft = y_fft[:, None] - - fft = torch.conj(fft) * y_fft - else: - fft = fft.abs() ** 2 - - stop = None if nperseg % 2 else -1 - if x.ndim == 1: - fft[1:stop] *= 2 - elif fft.ndim < 4: - fft[:, 1:stop] *= 2 - else: - fft[:, :, 1:stop] *= 2 - fft *= scale - - if average == "mean": - fft = fft.mean(axis=-1) - else: - bias = _median_bias(fft.shape[-1]) - if y is not None: - real_median = _median(fft.real, -1) - imag_median = 1j * _median(fft.imag, -1) - fft = real_median + imag_median - else: - fft = _median(fft, -1) - fft /= bias - - if fft.ndim == 2 and batch_size is not None: - fft = fft.reshape(batch_size, num_channels, -1) - return fft - - -class TorchWelch(nn.Module): - def __init__( - self, - sample_rate: float, - fftlength: float, - overlap: Optional[float] = None, - average: str = "mean", - device: str = "cpu", - fast: bool = False, - ): - if overlap is None: - overlap = fftlength / 2 - elif overlap >= fftlength: - raise ValueError( - "Can't have overlap {} longer than fftlength {}".format( - overlap, fftlength - ) - ) - - super().__init__() - - self.nperseg = int(fftlength * sample_rate) - self.nstride = self.nperseg - int(overlap * sample_rate) - - # do we allow for arbitrary windows? - self.window = torch.hann_window(self.nperseg).to(device) - - # scale corresponds to "density" normalization, worth - # considering adding this as a kwarg and changing this calc - self.scale = 1.0 / (sample_rate * (self.window**2).sum()) - - if average not in ("mean", "median"): - raise ValueError( - f'average must be "mean" or "median", got {average} instead' - ) - self.average = average - self.device = device - self.fast = fast - - def forward(self, x: torch.Tensor, y: Optional[torch.Tensor] = None): - if x.shape[-1] < self.nperseg: - raise ValueError( - "Number of samples {} in input x is insufficient " - "for number of fft samples {}".format( - x.shape[-1], self.nperseg - ) - ) - elif x.ndim > 3: - raise ValueError( - f"Can't perform welch transform on tensor with shape {x.shape}" - ) - - if y is not None: - _validate_shapes(x, y) - - if self.fast: - return _fast_csd( - x, - y=y, - nperseg=self.nperseg, - nstride=self.nstride, - window=self.window, - scale=self.scale, - average=self.average, - ) - - if y is not None: - raise NotImplementedError - - # for non-fast implementation, we need to unfold - # the tensor along the time dimension ourselves - # to detrend each segment individually, so start - # by converting x to a 4D tensor so we can use - # torch's Unfold op - if x.ndim == 1: - reshape = [] - x = x[None, None, None, :] - elif x.ndim == 2: - reshape = [len(x)] - x = x[None, :, None, :] - elif x.ndim == 3: - reshape = list(x.shape[:-1]) - x = x[:, :, None, :] - - # calculate the number of segments and trim x along - # the time dimensions so that we can unfold it exactly - num_segments = (x.shape[-1] - self.nperseg) // self.nstride + 1 - stop = (num_segments - 1) * self.nstride + self.nperseg - x = x[..., :stop] - - # unfold x into overlapping segments and detrend and window - # each one individually before computing the rfft. Unfold - # will produce a batch x (num_channels * num_segments) x nperseg - # shaped tensor - unfold_op = torch.nn.Unfold( - (1, num_segments), dilation=(1, self.nstride) - ) - x = unfold_op(x) - x = x - x.mean(axis=-1, keepdims=True) - x *= self.window - - # after the fft, we'll have a - # batch x (num_channels * num_segments) x nfreq - # sized tensor - fft = torch.fft.rfft(x, axis=-1).abs() ** 2 - - if self.nperseg % 2: - fft[..., 1:] *= 2 - else: - fft[..., 1:-1] *= 2 - fft *= self.scale - - # unfold the batch and channel dimensions back - # out if there were any to begin with, putting - # the segment dimension as the second to last - reshape += [num_segments, -1] - fft = fft.reshape(*reshape) - - if self.average == "mean": - return fft.mean(axis=-2) - else: - bias = _median_bias(num_segments) - return _median(fft, -2) / bias - - -class PSDLoss(nn.Module): +class PSDLoss(torch.nn.Module): """Compute the power spectrum density (PSD) loss, defined as the average over frequency of the PSD ratio""" @@ -260,10 +15,10 @@ def __init__( self, sample_rate: float, fftlength: float, - overlap: float, asd: bool = False, - freq_low: Union[float, List[float], None] = None, - freq_high: Union[float, List[float], None] = None, + overlap: Optional[float] = None, + freq_low: Union[Frequency, None] = None, + freq_high: Union[Frequency, None] = None, device: str = "cpu", ): super().__init__() @@ -300,20 +55,20 @@ def __init__( ) ) - self.welch = TorchWelch( + self.welch = SpectralDensity( sample_rate, fftlength, overlap, - average="median", + average="mean", device=device, fast=fast, ) self.asd = asd - def forward(self, pred, target): + def forward(self, y_hat: torch.Tensor, y: torch.Tensor) -> torch.Tensor: # Calculate the PSD of the residual and the target - psd_res = self.welch(target - pred) - psd_target = self.welch(target) + psd_res = self.welch(y - y_hat) + psd_target = self.welch(y) if self.mask is not None: # mask out these arrays rather than the ratio since @@ -322,18 +77,18 @@ def forward(self, pred, target): # if they've been filtered beforehand. This leads # to nan values that propogate back through the gradient psd_res = torch.masked_select(psd_res, self.mask) - psd_res = psd_res.reshape(len(pred), -1) + psd_res = psd_res.reshape(len(y), -1) psd_target = torch.masked_select(psd_target, self.mask) - psd_target = psd_target.reshape(len(pred), -1) + psd_target = psd_target.reshape(len(y), -1) ratio = psd_res / psd_target if self.asd: - ratio = ratio ** (0.5) + ratio = ratio**0.5 return ratio.mean() -class CompositePSDLoss(nn.Module): +class CompositePSDLoss(torch.nn.Module): """PSD + MSE Loss with weight""" def __init__( @@ -368,7 +123,7 @@ def __init__( asd=asd, device=device, ) - self.mse_loss = nn.MSELoss() + self.mse_loss = torch.nn.MSELoss() def forward(self, pred, target): if self.alpha == 0: diff --git a/libs/trainer/deepclean/trainer/dataloader.py b/libs/trainer/deepclean/trainer/dataloader.py deleted file mode 100644 index e5dbbed..0000000 --- a/libs/trainer/deepclean/trainer/dataloader.py +++ /dev/null @@ -1,345 +0,0 @@ -import logging -from typing import Tuple - -import numpy as np -import torch - - -class ChunkedTimeSeriesDataset(torch.utils.data.IterableDataset): - def __init__( - self, - X: np.ndarray, - y: np.ndarray, - kernel_length: float, - kernel_stride: float, - sample_rate: float, - batch_size: int, - chunk_length: float = 0, - num_chunks: int = 1, - shuffle: bool = True, - device: torch.device = "cpu", - ) -> None: - assert X.shape[-1] == y.shape[-1] - - self.kernel_size = int(kernel_length * sample_rate) - self.stride_size = int(kernel_stride * sample_rate) - - self.batch_size = batch_size - self.shuffle = shuffle - self.num_chunks = num_chunks - - X = torch.Tensor(X).to(device) - y = torch.Tensor(y).to(device) - - if chunk_length == 0: - # use the data as-is and slice on the fly - self.X = X - self.y = y - elif chunk_length == -1: - # a chunk length of -1 means we unroll the entire - # dataset up front. Slice out some of the time - # series to ensure that we can fit an integer number - # of kernels in the unrolled array - remainder = (X.shape[-1] - self.kernel_size) % self.stride_size - if remainder > 0: - X = X[:, :-remainder] - y = y[:-remainder] - self.X = self.unfold(X) - self.y = self.unfold(y) - - elif chunk_length > 0: - # break the data up into `chunk_length` chunks that - # will be unrolled and concatenated at iteration time - if chunk_length < 1: - # a chunk_length between 0 and 1 indicates a fraction - # of the total amount of time covered by the data - chunk_length = chunk_length * X.shape[-1] / sample_rate - - # make sure both that the chunk_length can accomodate - # an entire kernel and an entire batch TODO: is this - # repetitive, the first check should be a subset of the - # cases checked by the second, correct? - samples_per_chunk = int(chunk_length * num_chunks * sample_rate) - kernels_per_chunk = self.get_num_kernels(samples_per_chunk) - if chunk_length < kernel_length: - raise ValueError( - "Chunk length {} must be greater than kernel " - "length {}".format(chunk_length, kernel_length) - ) - elif kernels_per_chunk < batch_size: - raise ValueError( - "Not enough kernels in {}s long chunks to " - "create batches of size {}".format( - chunk_length * num_chunks, batch_size - ) - ) - - # split up the dataset into chunks, slicing off - # any data which might prevent us from creating - # an integer number of kernels in each chunk - split_idx, remainder = self.get_split_idx( - X.shape[-1], int(chunk_length * sample_rate) - ) - if remainder > 0: - X = X[:, :-remainder] - y = y[:-remainder] - - self.X = torch.split(X, split_idx, dim=-1) - self.y = torch.split(y, split_idx, dim=-1) - else: - raise ValueError( - "'chunk_length' must be either -1 or a " - "float value greater than or equal to 0, " - "found value {}".format(chunk_length) - ) - - self.chunk_length = chunk_length - - def get_split_idx( - self, num_samples: int, samples_per_chunk: int - ) -> Tuple[Tuple[int, ...], int]: - # first figure out how to make chunks with - # roughly the desired length that have - # an even number of kernels in each chunk - remainder_per_chunk = ( - samples_per_chunk - self.kernel_size - ) % self.stride_size - samples_per_chunk = samples_per_chunk - remainder_per_chunk - num_chunks = (num_samples - 1) // samples_per_chunk + 1 - lengths = (samples_per_chunk,) * (num_chunks - 1) - - # make sure that the last chunk will have an even - # number of kernels, even if that means sloughing - # off a little bit of data from the edge - last_length = num_samples - samples_per_chunk * (num_chunks - 1) - remainder = (last_length - self.kernel_size) % self.stride_size - last_length -= remainder - - # however, if we wont' have enough data for a - # full kernel in this case, then just cut off - # all the data in the last chunk - # TODO: should we try to reallocate some of - # this data to the existing chunks, or do we - # assume that the user has tuned their - # chunk_length to precisely match their - # memory capacity? - if last_length >= self.kernel_size: - lengths = lengths + (last_length,) - else: - remainder = last_length + remainder - return lengths, remainder - - def get_num_kernels(self, num_samples: int) -> int: - """ - Don't need any ceil in this function since we - take care beforehand to make sure all tensors - have an even number of kernels in them - """ - - return (num_samples - self.kernel_size) // self.stride_size + 1 - - @property - def num_kernels(self) -> int: - """The total number of kernels in the dataset""" - - if self.chunk_length == -1: - return len(self.X) - elif self.chunk_length == 0: - return self.get_num_kernels(self.X.shape[-1]) - else: - return sum([self.get_num_kernels(x.shape[-1]) for x in self.X]) - - def __len__(self) -> int: - """The number of batches of kernels in the dataset""" - - return (self.num_kernels - 1) // self.batch_size + 1 - - def unfold(self, chunk: torch.Tensor) -> torch.Tensor: - if len(chunk.shape) == 1: - chunk = chunk[None, None, None] - num_channels = None - elif len(chunk.shape) == 2: - chunk = chunk[None, :, None] - num_channels = chunk.shape[1] - else: - raise ValueError( - "Can't unfold timeseries chunk with shape {}".format( - chunk.shape - ) - ) - - num_kernels = self.get_num_kernels(chunk.shape[-1]) - unfold = torch.nn.Unfold( - kernel_size=(1, num_kernels), dilation=(1, self.stride_size) - ) - chunk = unfold(chunk) - if num_channels is not None: - chunk = chunk.reshape(num_channels, num_kernels, -1) - chunk = chunk.transpose(1, 0) - else: - chunk = chunk[0] - return chunk - - def grab_next_chunks(self, remainder_X=None, remainder_y=None): - start = self._chunk_idx * self.num_chunks - if start >= len(self.X): - raise StopIteration - - stop = (self._chunk_idx + 1) * self.num_chunks - chunk_indices = self.idx[start:stop] - - xs = [self.unfold(self.X[i]) for i in chunk_indices] - ys = [self.unfold(self.y[i]) for i in chunk_indices] - - if remainder_X is not None: - xs.insert(0, remainder_X) - ys.insert(0, remainder_y) - - X = torch.cat(xs, axis=0) - y = torch.cat(ys, axis=0) - - logging.debug( - "Loaded chunks of shape {} and {}".format(X.shape, y.shape) - ) - - if self.shuffle: - idx = torch.randperm(X.shape[0]) - X = X[idx] - y = y[idx] - self._chunk_idx += 1 - - return X, y - - def __iter__(self): - if self.chunk_length <= 0: - idx = self.num_kernels - else: - idx = len(self.X) - self._chunk_idx = 0 - self.chunk_X = self.chunk_y = None - - if self.shuffle: - self.idx = torch.randperm(idx) - else: - self.idx = torch.arange(idx) - self._batch_idx = 0 - - return self - - def __next__(self): - if self.chunk_length <= 0: - start = self._batch_idx * self.batch_size - if start >= self.idx.shape[0]: - raise StopIteration - - stop = (self._batch_idx + 1) * self.batch_size - idx = self.idx[start:stop] - self._batch_idx += 1 - if self.chunk_length == -1: - return self.X[idx], self.y[idx] - else: - idx = idx * self.stride_size - X = torch.stack( - [self.X[:, i : i + self.kernel_size] for i in idx] - ) - y = torch.stack( - [self.y[i : i + self.kernel_size] for i in idx] - ) - return X, y - - start = self._batch_idx * self.batch_size - stop = (self._batch_idx + 1) * self.batch_size - if self.chunk_X is None or stop >= len(self.chunk_X): - if self.chunk_X is not None and start < len(self.chunk_X): - remainder_X = self.chunk_X[start:] - remainder_y = self.chunk_y[start:] - else: - remainder_X = remainder_y = None - - try: - self.chunk_X, self.chunk_y = self.grab_next_chunks( - remainder_X=remainder_X, remainder_y=remainder_y - ) - except StopIteration: - if self.chunk_X is None or remainder_X is None: - raise - - self.chunk_X = self.chunk_y = None - return remainder_X, remainder_y - - start = self._batch_idx = 0 - stop = self.batch_size - - stop = (self._batch_idx + 1) * self.batch_size - X = self.chunk_X[start:stop] - y = self.chunk_y[start:stop] - self._batch_idx += 1 - return X, y - - -# def ChunkedFrameFileDataset(ChunkedTimeSeriesDataset): -# def __init__( -# self, -# fnames: Union[str, List[str]], -# channels: List[str], -# kernel_length: float, -# kernel_stride: float, -# sample_rate: float, -# batch_size: int, -# chunk_length: float, -# num_chunks: int, -# shuffle: bool = True, -# device: torch.device = "cpu", -# ) -> None: -# if isinstance(fnames, str): -# fnames = [fnames] - -# X, y = [], [] -# for fname in fnames: -# if fname.split(".")[-1] == "gwf": -# data = self.read_gwf(fname, channels, sample_rate) -# elif fname.split(".")[-1] in ["h5", "hdf5"]: -# data = self.read_hdf5(fname, channels, sample_rate) -# else: -# raise ValueError(f"No parser to read file {fname}.") - -# y.append(data[channels[0]]) -# X.append(np.stack([data[chan] for chan in sorted(channels[1:])])) - -# X = np.concatenate(X, axis=-1) -# y = np.concatenate(y, axis=-1) -# super().__init__( -# X, -# y, -# kernel_length=kernel_length, -# kernel_stride=kernel_stride, -# sample_rate=sample_rate, -# batch_size=batch_size, -# chunk_length=chunk_length, -# num_chunks=num_chunks, -# shuffle=shuffle, -# device=device, -# ) - -# def read_hdf5(self, fname, channels, sample_rate): -# with h5py.File(self.fname, "r") as f: -# data = {} -# for chan, x in f.items(): -# try: -# if x.attrs["sample_rate"] != sample_rate: -# raise ValueError( -# "Channel {} has sample rate {}Hz, expected " -# "sample rate of {}Hz".format( -# chan, x.attrs["sample_rate"], sample_rate -# ) -# ) -# except KeyError: -# continue - -# if len(data) < len(channels): -# raise ValueError( -# "Filename {} missing channels {}".format( -# fname, list(set(channels) - set(data)) -# ) -# ) -# return data diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 0120658..156a8ff 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -1,114 +1,34 @@ import logging -import os -import time +from functools import partial from pathlib import Path from typing import Callable, Optional, Tuple -import h5py import numpy as np import torch -from deepclean.export import PrePostDeepClean -from deepclean.signal import BandpassFilter, StandardScaler -from deepclean.signal.filter import FREQUENCY -from deepclean.trainer import ChunkedTimeSeriesDataset, CompositePSDLoss +from deepclean.trainer import CompositePSDLoss from deepclean.trainer.analysis import analyze_model +from deepclean.trainer.utils import Checkpointer, Trainer +from deepclean.utils import BandpassFilter, Frequency +from ml4gw.dataloading import InMemoryDataset +from ml4gw.transforms import ChannelWiseScaler, SpectralDensity torch.set_default_tensor_type(torch.FloatTensor) -def train_for_one_epoch( - model: torch.nn.Module, - optimizer: torch.optim.Optimizer, - criterion: torch.nn.Module, - train_data: ChunkedTimeSeriesDataset, - valid_data: Optional[ChunkedTimeSeriesDataset] = None, - profiler: Optional[torch.profiler.profile] = None, - scaler: Optional[torch.cuda.amp.GradScaler] = None, -): - """Run a single epoch of training""" - - train_loss = 0 - samples_seen = 0 - start_time = time.time() - model.train() - for witnesses, strain in train_data: - optimizer.zero_grad(set_to_none=True) # reset gradient - # do forward step in mixed precision - # if a gradient scaler got passed - with torch.autocast("cuda", enabled=scaler is not None): - noise_prediction = model(witnesses) - loss = criterion(noise_prediction, strain) - - train_loss += loss.item() * len(witnesses) - samples_seen += len(witnesses) - - if scaler is not None: - scaler.scale(loss).backward() - scaler.step(optimizer) - scaler.update() - else: - loss.backward() - optimizer.step() - - if profiler is not None: - profiler.step() - logging.debug(f"{samples_seen}/{train_data.num_kernels}") - - if profiler is not None: - profiler.stop() - - end_time = time.time() - duration = end_time - start_time - throughput = samples_seen / duration - train_loss /= samples_seen - - logging.info( - "Duration {:0.2f}s, Throughput {:0.1f} samples/s".format( - duration, throughput - ) - ) - msg = f"Train Loss: {train_loss:.4e}" - - # Evaluate performance on validation set if given - if valid_data is not None: - valid_loss = 0 - samples_seen = 0 - - model.eval() - with torch.no_grad(): - for witnesses, strain in valid_data: - noise_prediction = model(witnesses) - loss = criterion(noise_prediction, strain) - - valid_loss += loss.item() * len(witnesses) - samples_seen += len(witnesses) - - valid_loss /= samples_seen - msg += f", Valid Loss: {valid_loss:.4e}" - else: - valid_loss = None - - logging.info(msg) - return train_loss, valid_loss, duration, throughput - - def train( architecture: Callable, - output_directory: str, + output_directory: Path, # data params X: np.ndarray, y: np.ndarray, kernel_length: float, kernel_stride: float, sample_rate: float, - chunk_length: float = 0, - num_chunks: int = 1, valid_data: Optional[Tuple[np.ndarray, np.ndarray]] = None, # preproc params - # TODO: make optional - freq_low: FREQUENCY = 55.0, - freq_high: FREQUENCY = 65.0, + freq_low: Frequency = 55.0, + freq_high: Frequency = 65.0, filter_order: int = 8, # optimization params batch_size: int = 32, @@ -127,7 +47,7 @@ def train( device: Optional[str] = None, profile: bool = False, use_amp: bool = False, -) -> float: +) -> None: """Train a DeepClean model on in-memory data Args: @@ -241,82 +161,64 @@ def train( if not (0 <= alpha <= 1): raise ValueError("Alpha value must be between 0 and 1") - os.makedirs(output_directory, exist_ok=True) - output_directory = Path(output_directory) - - # TODO: is there more complicated device - # logic we want to do or should "cpu" just - # be the default? + output_directory.mkdir(parents=True, exist_ok=True) device = device or "cpu" - # Create pipelines for preprocessing both - # witness and strain data. Witness data - # just gets normalized on a channel-wise - # basis, strain data gets normalized then - # bandpass filtered to ensure only the - # target frequency range gets optimized - logging.info("Preprocessing") - witness_scaler = StandardScaler() - strain_scaler = StandardScaler() - bandpass = BandpassFilter( - freq_low=freq_low, - freq_high=freq_high, - sample_rate=sample_rate, - order=filter_order, - ) + # scale both the inputs and outputs to 0 mean unit variance + input_scaler = ChannelWiseScaler(len(X)).to(device) + output_scaler = ChannelWiseScaler().to(device) - # fit the pipelines to the data (i.e. compute - # the mean and standard deviation across - # the datasets) and save them for later use - witness_pipeline = witness_scaler - witness_pipeline.fit(X) + # fit the means and standard deviations _before_ filtering + input_scaler.fit(X) + output_scaler.fit(y) - strain_pipeline = strain_scaler >> bandpass - strain_pipeline.fit(y) + # bandpass the strain up front so that we only + # deal with the frequency ranges we care about + strain_filter = BandpassFilter( + freq_low, freq_high, sample_rate, filter_order + ) + y = strain_filter(y) - # use these preprocessed arrays to - # instantiate iterable datasets - train_data = ChunkedTimeSeriesDataset( - witness_pipeline(X), - strain_pipeline(y), - kernel_length=kernel_length, - kernel_stride=kernel_stride, - sample_rate=sample_rate, + train_data = InMemoryDataset( + X, + y=y, + kernel_size=int(sample_rate * kernel_length), batch_size=batch_size, - chunk_length=chunk_length, - num_chunks=num_chunks, - shuffle=False, + stride=int(sample_rate * kernel_stride), + coincident=True, + shuffle=True, device=device, ) + train_data.X = input_scaler(train_data.X) + train_data.y = output_scaler(train_data.y) if valid_data is not None: valid_X, valid_y = valid_data - valid_data = ChunkedTimeSeriesDataset( - witness_pipeline(valid_X), - strain_pipeline(valid_y), - kernel_length=kernel_length, - kernel_stride=kernel_stride, - sample_rate=sample_rate, - batch_size=batch_size * 2, - chunk_length=-1, + valid_y = strain_filter(valid_y) + + valid_data = InMemoryDataset( + valid_X, + y=valid_y, + kernel_size=int(sample_rate * kernel_length), + batch_size=batch_size, + stride=int(sample_rate * kernel_stride), + coincident=True, shuffle=False, device=device, ) + valid_data.X = input_scaler(valid_data.X) + valid_data.y = output_scaler(valid_data.y) # Creating model, loss function, optimizer and lr scheduler logging.info("Building and initializing model") - model = architecture(len(X)) - model.to(device) - + model = architecture(len(X)).to(device) if init_weights is not None: # allow us to easily point to the best weights # from another run of this same function if init_weights.is_dir(): init_weights = init_weights / "weights.pt" - logging.debug( - f"Initializing model weights from checkpoint '{init_weights}'" - ) + logging.info(f"Initializing model from checkpoint '{init_weights}'") model.load_state_dict(torch.load(init_weights)) logging.info(model) @@ -325,38 +227,24 @@ def train( alpha, sample_rate, fftlength=fftlength, - overlap=None, + overlap=overlap, asd=True, device=device, freq_low=freq_low, freq_high=freq_high, ) - optimizer = torch.optim.Adam( - model.parameters(), lr=lr, weight_decay=weight_decay + trainer = Trainer(criterion, model, lr, weight_decay, use_amp) + checkpointer = Checkpointer( + output_directory, + trainer.optimizer, + patience=patience, + decay_factor=factor, + min_lr=lr * factor**2, + early_stop=early_stop, + checkpoint_every=5, ) - if patience is not None: - lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( - optimizer, - patience=patience, - factor=factor, - threshold=0.0001, - min_lr=lr * factor**2, - verbose=True, - ) - - # start training - scaler = None - if use_amp and device.startswith("cuda"): - scaler = torch.cuda.amp.GradScaler() - elif use_amp: - logging.warning("'use_amp' flag set but no cuda device, ignoring") torch.backends.cudnn.benchmark = True - best_valid_loss = float("inf") - since_last_improvement = 0 - weights_path = output_directory / "weights.pt" - history = {"train_loss": [], "valid_loss": []} - logging.info("Beginning training loop") for epoch in range(max_epochs): if epoch == 0 and profile: @@ -371,85 +259,24 @@ def train( profiler = None logging.info(f"=== Epoch {epoch + 1}/{max_epochs} ===") - train_loss, valid_loss, duration, throughput = train_for_one_epoch( - model, - optimizer, - criterion, - train_data, - valid_data, - profiler, - scaler, - ) - history["train_loss"].append(train_loss) - - # do some house cleaning with our - # validation loss if we have one - if valid_loss is not None: - history["valid_loss"].append(valid_loss) - - # update our learning rate scheduler if we - # indicated a schedule with `patience` - if patience is not None: - lr_scheduler.step(valid_loss) - - # save this version of the model weights if - # we achieved a new best loss, otherwise check - # to see if we need to early stop based on - # plateauing validation loss - if valid_loss < best_valid_loss: - logging.debug( - "Achieved new lowest validation loss, " - "saving model weights" - ) - best_valid_loss = valid_loss - - torch.save(model.state_dict(), weights_path) - since_last_improvement = 0 - else: - since_last_improvement += 1 - if since_last_improvement >= early_stop: - logging.info( - "No improvement in validation loss in {} " - "epochs, halting training early".format(early_stop) - ) - break - else: - # if we don't have validation data, just - # always keep the last version of the model - torch.save(model.state_dict(), weights_path) + train_loss, valid_loss = trainer(train_data, valid_data, profiler) + stop = checkpointer(train_loss, valid_loss, model) + if stop: + break # load in the best version of the model from training + weights_path = output_directory / "weights.pt" model.load_state_dict(torch.load(weights_path)) # generate some analyses of our model logging.info("Performing post-hoc analysis on trained model") - gradients, coherences = analyze_model(train_data, model, sample_rate) - history.update( - { - "percentiles": [5, 25, 50, 75, 95], - "train_gradients": gradients, - "train_coherences": coherences, - } + welch = SpectralDensity( + sample_rate, fftlength, average="median", fast=True ) - if valid_data is not None: - # we'll actually need gradients on the validation data, - # so we can't be as liberal with the memory. Reset its - # batch size to keep things within reason - valid_data.batch_size = train_data.batch_size - gradients, coherences = analyze_model(valid_data, model, sample_rate) - history.update( - { - "valid_gradients": gradients, - "valid_coherences": coherences, - } - ) - with h5py.File(output_directory / "train_results.h5", "w") as f: - for key, value in history.items(): - f.create_dataset(key, data=value) + analyze_model(output_directory, model, welch, train_data, valid_data) # now create a version of the model which # has the pre- and postprocessing built in - nn = PrePostDeepClean(model) - nn.fit(X, y) - torch.save(nn.state_dict(), weights_path) - # return history + output_scaler.forward = partial(output_scaler.forward, reverse=True) + model = torch.nn.Sequential(input_scaler, model, output_scaler) + torch.save(model.state_dict(), weights_path) diff --git a/libs/trainer/deepclean/trainer/utils.py b/libs/trainer/deepclean/trainer/utils.py new file mode 100644 index 0000000..fd02945 --- /dev/null +++ b/libs/trainer/deepclean/trainer/utils.py @@ -0,0 +1,206 @@ +import logging +import time +from dataclasses import dataclass +from functools import wraps +from pathlib import Path +from typing import Callable, Iterable, Optional, Tuple + +import h5py +import torch + +DataGenerator = Iterable[Tuple[torch.nn.Module, torch.nn.Module]] + + +@dataclass +class EpochTracker: + profiler: Optional[torch.profiler.profile] = None + + def __post_init__(self): + self.samples_seen = 0 + self.loss = 0 + self.start_time = time.time() + if self.profiler is not None: + self.profiler.start() + + def update(self, loss, num_samples) -> None: + if self.profiler is not None: + self.profiler.step() + + if loss is not None: + self.loss += loss + self.samples_seen += num_samples + + def stop(self) -> Tuple[float, float, float]: + if self.profiler is not None: + self.profiler.stop() + + end_time = time.time() + duration = self.start_time - end_time + throughput = self.samples_seen / duration + loss = self.loss / self.samples_seen + return loss, duration, throughput + + +def backward( + loss: torch.Tensor, + optimizer: torch.optim.Optimizer, + scaler: Optional[torch.cuda.amp.GradScaler] = None, +) -> Optional[float]: + if scaler is not None: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + optimizer.step() + + isnan = torch.isnan(loss).item() + if isnan and scaler is None: + raise ValueError("Encountered nan loss") + elif isnan: + return None + return loss.item() + + +def backwardize(forward_fn: Callable) -> Callable: + @wraps(forward_fn) + def step(obj, X: torch.Tensor, y: torch.Tensor) -> Optional[float]: + obj.optimizer.zero_grad(set_to_none=True) # reset gradient + + # do forward step in mixed precision + # if a gradient scaler got passed + with torch.autocast("cuda", enabled=obj.scaler is not None): + loss = forward_fn(X, y) + return backward(loss, obj.optimizer, obj.scaler) + + return step + + +class Trainer: + def __init__( + self, + model: torch.nn.Module, + criterion: Callable, + learning_rate: float, + weight_decay: float, + use_amp: bool = False, + ) -> None: + self.model = model + self.criterion = criterion + + self.optimizer = torch.optim.Adam( + model.parameters(), lr=learning_rate, weight_decay=weight_decay + ) + self.scaler = torch.cuda.amp.GradScaler() if use_amp else None + + def forward(self, X, y): + noise_prediction = self.model(X) + loss = self.criterion(noise_prediction, y) + return loss + + @backwardize + def step(self, X, y): + return self.forward(X, y) + + def __call__( + self, + train_data: DataGenerator, + valid_data: Optional[DataGenerator] = None, + profiler: Optional[torch.profiler.profile] = None, + ) -> Tuple[float, Optional[float]]: + + self.model.train() + tracker = EpochTracker(profiler) + for witnesses, strain in train_data: + loss = self.step(witnesses, strain) + tracker.update(loss, len(witnesses)) + train_loss, duration, throughput = tracker.stop() + + logging.info( + f"Duration {duration:0.2f}s, " + f"Throughput {throughput:0.1f} samples/s" + ) + msg = f"Train Loss: {train_loss:.4e}" + + # Evaluate performance on validation set if given + if valid_data is not None: + tracker = EpochTracker() + self.model.eval() + with torch.no_grad(): + for witnesses, strain in valid_data: + loss = self.forward(witnesses, strain) + tracker.update(loss, len(witnesses)) + valid_loss, _, __ = tracker.stop() + msg += f", Valid Loss: {valid_loss:.4e}" + else: + valid_loss = None + logging.info(msg) + + return train_loss, valid_loss + + +@dataclass +class Checkpointer: + output_directory: Path + optimizer: torch.optim.Optimizer + patience: Optional[int] = None + decay_factor: float = 0.1 + min_lr: Optional[float] = None + early_stop: Optional[int] = None + checkpoint_every: Optional[int] = None + + def __post_init__(self): + if self.patience is not None: + self.lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau( + self.optimizer, + patience=self.patience, + factor=self.decay_factor, + threshold=0.0001, + min_lr=self.min_lr, + verbose=True, + ) + self._i = 0 + self.best = float("inf") + self.since_last = 0 + self.history = {"train_loss": [], "valid_loss": []} + + if self.checkpoint_every is not None: + self.checkpoint_dir = self.output_directory / "checkpoints" + self.checkpoint_dir.mkdir(parents=True, exist_ok=True) + else: + self.checkpoint_dir = None + + def __call__(self, train_loss, valid_loss, model) -> bool: + self._i += 1 + if ( + self.checkpoint_every is not None + and not self._i % self.checkpoint_every + ): + fname = "epoch_" + str(self._i).zfill(4) + fname = self.checkpoint_dir / fname + torch.save(model.state_dict, fname) + + self.history["train_loss"].append(train_loss) + stop = False + if valid_loss is not None: + self.history["valid_loss"].append(valid_loss) + + if self.patience is not None: + self.lr_scheduler.step(valid_loss) + + if valid_loss <= self.best: + self.best = valid_loss + self.since_last = 0 + + fname = self.output_directory / "weights.pt" + torch.save(model.state_dict(), fname) + elif self.early_stop is not None: + self.since_last += 1 + if self.since_last >= self.early_stop: + stop = True + + with h5py.File(self.output_directory / "history.h5", "w") as f: + group = f.create_group("loss") + for key, value in self.history.items(): + group.create_dataset(key, data=value) + return stop diff --git a/libs/trainer/deepclean/trainer/viz.py b/libs/trainer/deepclean/trainer/viz.py deleted file mode 100644 index c46d662..0000000 --- a/libs/trainer/deepclean/trainer/viz.py +++ /dev/null @@ -1,84 +0,0 @@ -import os -from typing import TYPE_CHECKING, Callable, List, Optional, Tuple - -import matplotlib.pyplot as plt -import numpy as np -import torch - -if TYPE_CHECKING: - from deepclean.trainer.dataloader import ChunkedTimeSeriesDataset - - -def plot_data_asds( - dataloader: "ChunkedTimeSeriesDataset", - sample_rate: float, - write_dir: str, - welch: Callable, - channels: List[str], - x_range: Optional[Tuple[float]] = None, - model: Optional[torch.nn.Module] = None, -): - os.makedirs(write_dir, exist_ok=True) - - X_asd, y_asd, res_asd, N = 0, 0, 0, 0 - for X, y in dataloader: - # compute predictions using X - # if we have a model to do it with - if model is not None: - model.eval() - with torch.no_grad(): - pred = model(X) - - # now compute the PSD of the residual - residual = y - pred - residual_welch = welch(residual).mean(axis=0) - else: - residual_welch = None - - # compute PSDs of each of the X witness channels in X - batch_size = X.shape[0] - X = X.view(-1, X.shape[-1]) - X = welch(X) - X = X.view(batch_size, -1, X.shape[-1]) - X = X.mean(axis=0) - - # compute the PSD of the strain channel - y = welch(y).mean(axis=0) - - # now update the averages in an online fashion - factor = len(X) / (N + len(X)) - X_asd -= (X_asd - X) * factor - y_asd -= (y_asd - y) * factor - - if model is not None: - res_asd -= (res_asd - residual_welch) * factor - N += len(X) - - # move these asds back to the CPU/numpy - X_asd = np.sqrt(X_asd.cpu().numpy()) - y_asd = np.sqrt(y_asd.cpu().numpy()) - if model is not None: - res_asd = np.sqrt(res_asd.cpu().numpy()) - - asds = np.concatenate([y_asd[None], X_asd], axis=0) - freqs = np.linspace(0, sample_rate / 2, X_asd.shape[-1]) - - if x_range is not None: - x_min, x_max = x_range - mask = (x_min < freqs) & (freqs < x_max) - else: - mask = np.ones_like(freqs, dtype=np.bool) - - for channel, asd in zip(channels, asds): - fig, ax = plt.subplots() - ax.set_xlabel("Frequency [Hz]") - ax.set_ylabel("ASD [Hz$^{-\\frac{1}{2}}$]") - ax.set_yscale("log") - ax.set_title(str(channel)) - - ax.plot(freqs[mask], asd[mask]) - if model is not None and channel == channels[0]: - ax.plot(freqs[mask], res_asd[mask], label="Cleaned") - ax.legend() - fig.savefig(os.path.join(write_dir, f"{channel}.png")) - plt.close(fig) diff --git a/libs/trainer/deepclean/trainer/wrapper.py b/libs/trainer/deepclean/trainer/wrapper.py index fb0197f..e644ca8 100644 --- a/libs/trainer/deepclean/trainer/wrapper.py +++ b/libs/trainer/deepclean/trainer/wrapper.py @@ -1,9 +1,10 @@ import inspect +from typeo import scriptify +from typeo.doc_utils import parse_doc, parse_help + from deepclean.architectures import get_arch_fns from deepclean.trainer.trainer import train -from hermes.typeo import typeo -from hermes.typeo.typeo import _parse_doc, _parse_help def _configure_wrapper(f, wrapper): @@ -31,13 +32,13 @@ def _configure_wrapper(f, wrapper): wrapper.__signature__ = inspect.Signature(parameters=parameters) wrapper.__name__ = f.__name__ - _, train_args = _parse_doc(train) - f_doc, f_args = _parse_doc(f) + _, train_args = parse_doc(train) + f_doc, f_args = parse_doc(f) wrapper_args = "" for p in parameters: for args in [f_args, train_args]: - doc_str = _parse_help(args, p.name) + doc_str = parse_help(args, p.name) if doc_str: break else: @@ -141,4 +142,4 @@ def wrapper(*args, **kwargs): # all training and architecture arguments. Each # network architecture will be exposed as a # subcommand with its own arguments - return typeo(wrapper, **arch_fns) + return scriptify(wrapper, **arch_fns) diff --git a/libs/trainer/poetry.lock b/libs/trainer/poetry.lock index 61860ca..6e2ea2b 100644 --- a/libs/trainer/poetry.lock +++ b/libs/trainer/poetry.lock @@ -1,19 +1,23 @@ [[package]] name = "astropy" -version = "4.3.1" +version = "5.1.1" description = "Astronomy and astrophysics core library" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] -numpy = ">=1.17" -pyerfa = ">=1.7.3" +numpy = ">=1.18" +packaging = ">=19.0" +pyerfa = ">=2.0" +PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.1)", "certifi", "dask", "h5py", "beautifulsoup4", "html5lib", "bleach", "PyYAML (>=3.13)", "pandas", "sortedcontainers", "pytz", "jplephem", "matplotlib (>=3.0,!=3.4.0)", "mpmath", "asdf (>=2.6)", "bottleneck", "ipython (>=4.2)", "pytest"] -docs = ["sphinx (<4)", "sphinx-astropy (>=1.3)", "pytest", "PyYAML (>=3.13)", "scipy (>=1.1)", "matplotlib (>=3.1,!=3.4.0)", "sphinx-changelog (>=1.1.0)", "packaging"] -test = ["pytest-astropy (>=0.8)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "packaging"] +all = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)"] +docs = ["sphinx (<4)", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (<3.1)"] +recommended = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "pooch"] [[package]] name = "atomicwrites" @@ -27,102 +31,356 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" name = "attrs" version = "22.1.0" description = "Classes Without Boilerplate" -category = "dev" +category = "main" optional = false python-versions = ">=3.5" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] +dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] +docs = ["furo", "sphinx", "zope-interface", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "cloudpickle"] tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +[[package]] +name = "bilby" +version = "1.4.0" +description = "A user-friendly Bayesian inference library" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +attrs = "*" +"bilby.cython" = ">=0.3.0" +corner = "*" +dill = "*" +dynesty = ">=2" +emcee = "*" +h5py = "*" +matplotlib = ">=2.1" +numpy = "*" +pandas = "*" +scipy = ">=1.5" +tqdm = "*" + +[package.extras] +all = ["cpnest 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+python-versions = "*" + +[package.dependencies] +pycparser = "*" + +[[package]] +name = "charset-normalizer" +version = "2.1.1" +description = "The Real First Universal Charset Detector. 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["h5py", "scipy"] +tests = ["pytest", "pytest-cov", "coverage"] [[package]] -name = "hermes.typeo" -version = "0.1.5" -description = "Utilities for running functions as scripts" +name = "fonttools" +version = "4.38.0" +description = "Tools to manipulate font files" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] +graphite = ["lz4 (>=1.7.4.2)"] +interpolatable = ["scipy", "munkres"] +lxml = ["lxml (>=4.0,<5)"] +pathops = ["skia-pathops (>=0.5.0)"] +plot = ["matplotlib"] +repacker = ["uharfbuzz (>=0.23.0)"] +symfont = ["sympy"] +type1 = ["xattr"] +ufo = ["fs (>=2.2.0,<3)"] +unicode = ["unicodedata2 (>=14.0.0)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] + +[[package]] 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"A python package for gravitational-wave astrophysics" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +astropy = ">=4.0" +dqsegdb2 = "*" +gwdatafind = ">=1.1.0" +gwosc = ">=0.5.3" +h5py = ">=2.8.0" +ligo-segments = ">=1.0.0" +ligotimegps = ">=1.2.1" +matplotlib = ">=3.3.0" +numpy = ">=1.16" +python-dateutil = "*" +scipy = ">=1.2.0" +tqdm = ">=4.10.0" + +[package.extras] +conda = ["python-framel (>=8.40.1)", "python-nds2-client", "python-ldas-tools-framecpp"] +dev = ["ciecplib", "maya", "pandas", "psycopg2", "pymysql", "pyrxp", "sqlalchemy", "uproot (>=3.11)", "uproot3", "astropy (<5.1a0)", "lalsuite", "lscsoft-glue", "pycbc (>=1.13.4)", "python-ligo-lw (>=1.7.0)"] +docs = ["numpydoc (>=0.8.0)", "requests", "sphinx (>=4.0.0)", "sphinx-automodapi", "sphinx-material (>=0.0.32)", "sphinx-panels (>=0.6.0)", "sphinxcontrib-programoutput"] +test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "pytest-cov (>=2.4.0)", "pytest-socket", "pytest-xdist", "requests-mock"] + +[[package]] +name = "h5py" +version = "3.7.0" +description = "Read and write HDF5 files from Python" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +numpy = ">=1.14.5" + +[[package]] +name = "idna" +version = "3.4" +description = "Internationalized Domain Names in Applications (IDNA)" +category = "main" +optional = false +python-versions = ">=3.5" + +[[package]] +name = "igwn-auth-utils" +version = "0.3.1" +description = "Authorisation utilities for IGWN" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +cryptography = ">=2.3" +requests = ">=2.14" +safe-netrc = ">=1.0.0" +scitokens = ">=1.7.0" + +[package.extras] +test = ["safe-netrc (>=1.0.0)", "requests-mock", "requests (>=2.14)", "pytest-cov", "pytest (>=3.9.1)"] +lint = ["flake8-bandit", "flake8 (>=3.7.0)"] +docs = ["sphinx-immaterial-igwn", "sphinx (>=4.0.0)"] [[package]] name = "iniconfig" @@ -132,9 +390,75 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "kiwisolver" +version = "1.4.4" +description = "A fast implementation of the Cassowary constraint solver" +category = "main" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "ligo-segments" +version = "1.4.0" +description = "Representations of semi-open intervals" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[[package]] +name = "ligotimegps" +version = "2.0.1" +description = "A pure-python version of lal.LIGOTimeGPS" +category = "main" +optional = false +python-versions = ">=3.4" + +[[package]] +name = "matplotlib" +version = "3.6.2" +description = "Python plotting package" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +contourpy = ">=1.0.1" +cycler = ">=0.10" +fonttools = ">=4.22.0" +kiwisolver = ">=1.0.1" +numpy = ">=1.19" +packaging = ">=20.0" +pillow = ">=6.2.0" +pyparsing = ">=2.2.1" +python-dateutil = ">=2.7" +setuptools_scm = ">=7" + +[[package]] +name = "ml4gw" +version = "0.0.1" +description = "Tools for building training torch models on gravitational wave data" +category = "main" +optional = false +python-versions = ">=3.8,<3.11" +develop = true + +[package.dependencies] +bilby = "^1.1" +gwpy = "^2.1" +torch = "^1.10" +torchtyping = "^0.1" + +[package.source] +type = "directory" +url = "../../ml4gw" + [[package]] name = "numpy" -version = "1.22.4" +version = "1.23.5" description = "NumPy is the fundamental package for array computing with Python." category = "main" optional = false @@ -142,14 +466,45 @@ python-versions = ">=3.8" [[package]] name = "packaging" -version = "20.9" +version = "21.3" description = "Core utilities for Python packages" category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +python-versions = ">=3.6" + +[package.dependencies] +pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" + +[[package]] +name = "pandas" +version = "1.5.2" +description = "Powerful data structures for data analysis, time series, and statistics" +category = "main" +optional = false +python-versions = ">=3.8" [package.dependencies] -pyparsing = ">=2.0.2" +numpy = [ + {version = ">=1.20.3", markers = "python_version < \"3.10\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, +] +python-dateutil = ">=2.8.1" +pytz = ">=2020.1" + +[package.extras] +test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] + +[[package]] +name = "pillow" +version = "9.3.0" +description = "Python Imaging Library (Fork)" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] name = "pluggy" @@ -171,6 +526,14 @@ category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +[[package]] +name = "pycparser" +version = "2.21" +description = "C parser in Python" +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + [[package]] name = "pyerfa" version = "2.0.0.1" @@ -186,6 +549,20 @@ numpy = ">=1.17" docs = ["sphinx-astropy (>=1.3)"] test = ["pytest", "pytest-doctestplus (>=0.7)"] +[[package]] +name = "pyjwt" +version = "2.6.0" +description = "JSON Web Token implementation in Python" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +crypto = ["cryptography (>=3.4.0)"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface"] +tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] + [[package]] name = "pyparsing" version = "3.0.9" @@ -218,16 +595,132 @@ toml = "*" [package.extras] testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +[[package]] +name = "python-dateutil" +version = "2.8.2" +description = "Extensions to the standard Python datetime module" +category = "main" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" + +[package.dependencies] +six = ">=1.5" + +[[package]] +name = "pytz" +version = "2022.6" +description = "World timezone definitions, modern and historical" +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "pyyaml" +version = "6.0" +description = "YAML parser and emitter for Python" +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "requests" +version = "2.28.1" +description = "Python HTTP for Humans." +category = "main" +optional = false +python-versions = ">=3.7, <4" + +[package.dependencies] +certifi = ">=2017.4.17" +charset-normalizer = ">=2,<3" +idna = ">=2.5,<4" +urllib3 = ">=1.21.1,<1.27" + +[package.extras] +socks = ["PySocks (>=1.5.6,!=1.5.7)"] +use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] + +[[package]] +name = "safe-netrc" +version = "1.0.1" +description = "Safe netrc file parser" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +test = ["pytest"] + [[package]] name = "scipy" -version = "1.7.3" -description = "SciPy: Scientific Library for Python" +version = "1.9.3" +description = "Fundamental algorithms for scientific computing in Python" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = ">=1.18.5,<1.26.0" + +[package.extras] +test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-panels (>=0.5.2)", "matplotlib (>2)", "numpydoc", "sphinx-tabs"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8"] + +[[package]] +name = "scitokens" +version = "1.7.4" +description = "SciToken reference implementation library" category = "main" optional = false -python-versions = ">=3.7,<3.11" +python-versions = ">=3.5" [package.dependencies] -numpy = ">=1.16.5,<1.23.0" +cryptography = "*" +PyJWT = ">=1.6.1" +setuptools = "*" + +[package.extras] +docs = ["sphinx"] + +[[package]] +name = "setuptools" +version = "65.6.3" +description = "Easily download, build, install, upgrade, and uninstall Python packages" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"] +testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] + +[[package]] +name = "setuptools-scm" +version = "7.0.5" +description = "the blessed package to manage your versions by scm tags" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +packaging = ">=20.0" +setuptools = "*" +tomli = ">=1.0.0" +typing-extensions = "*" + +[package.extras] +test = ["pytest (>=6.2)", "virtualenv (>20)"] +toml = ["setuptools (>=42)"] + +[[package]] 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a/libs/trainer/pyproject.toml b/libs/trainer/pyproject.toml index 39be73d..a804566 100644 --- a/libs/trainer/pyproject.toml +++ b/libs/trainer/pyproject.toml @@ -9,22 +9,19 @@ packages = [ ] [tool.poetry.dependencies] -python = "^3.8,<3.10" -torch = "^1.0" +python = "^3.8,<3.11" +torch = {version = "^1.10", source = "torch"} +typeo = {git = "https://github.com/ML4GW/typeo.git", branch = "main"} +ml4gw = {path = "../../ml4gw", develop = true} -# include signal processing libraries -# for preprocessing steps -"deepclean.signal" = {path = "../signal", develop = true} - -# include network architecture libraries -# if we want to build a wrapper that exposes -# architecture-specific args +"deepclean.utils" = {path = "../utils", develop = true} "deepclean.architectures" = {path = "../architectures", develop = true} -"deepclean.export" = {path = "../export", develop = true} -"deepclean.base" = {path = "../base", develop = true} - -"hermes.typeo" = "^0.1.5" +[[tool.poetry.source]] +name = "torch" +url = "https://download.pytorch.org/whl/cu116" +secondary = true +default = false [tool.poetry.dev-dependencies] pytest = "^6.2" diff --git a/libs/gwftools/deepclean/gwftools/__init__.py b/libs/utils/deepclean/gwftools/__init__.py similarity index 100% rename from libs/gwftools/deepclean/gwftools/__init__.py rename to libs/utils/deepclean/gwftools/__init__.py diff --git a/libs/gwftools/deepclean/gwftools/channels.py b/libs/utils/deepclean/gwftools/channels.py similarity index 100% rename from libs/gwftools/deepclean/gwftools/channels.py rename to libs/utils/deepclean/gwftools/channels.py diff --git a/libs/gwftools/deepclean/gwftools/frames.py b/libs/utils/deepclean/gwftools/frames.py similarity index 100% rename from libs/gwftools/deepclean/gwftools/frames.py rename to libs/utils/deepclean/gwftools/frames.py diff --git a/libs/gwftools/deepclean/gwftools/io.py b/libs/utils/deepclean/gwftools/io.py similarity index 100% rename from libs/gwftools/deepclean/gwftools/io.py rename to libs/utils/deepclean/gwftools/io.py diff --git a/libs/signal/deepclean/signal/filter.py b/libs/utils/deepclean/utils.py similarity index 81% rename from libs/signal/deepclean/signal/filter.py rename to libs/utils/deepclean/utils.py index 3265702..67c3e5e 100644 --- a/libs/signal/deepclean/signal/filter.py +++ b/libs/utils/deepclean/utils.py @@ -1,14 +1,28 @@ -from typing import Iterable, List, Tuple, Union +from typing import List, Sequence, Tuple, Union from scipy import signal -from deepclean.signal.op import Op +ChannelList = Union[str, Sequence[str]] +Frequency = Union[float, Sequence[float]] -FREQUENCY = Union[float, Iterable[float]] + +def get_channels(channels: ChannelList) -> List[str]: + if isinstance(channels, str) or len(channels) == 1: + if len(channels) == 1: + channels = channels[0] + + try: + with open(channels, "r") as f: + channels = [i for i in f.read().splitlines() if i] + except FileNotFoundError: + raise FileNotFoundError(f"No channel file {channels} exists") + + channels = list(channels) + return channels[:1] + sorted(channels[1:]) def normalize_frequencies( - freq_low: FREQUENCY, freq_high: FREQUENCY + freq_low: Frequency, freq_high: Frequency ) -> Tuple[List[float], List[float]]: """Standardize frequency bands to sorted lists @@ -98,11 +112,11 @@ def normalize_frequencies( return freq_low, freq_high -class BandpassFilter(Op): +class BandpassFilter: def __init__( self, - freq_low: FREQUENCY, - freq_high: FREQUENCY, + freq_low: Frequency, + freq_high: Frequency, sample_rate: float, order: int = 8, ) -> None: @@ -121,7 +135,7 @@ def __init__( ) ) - def __call__(self, x, **kwargs): + def __call__(self, x): output = 0.0 for sos in self.sos: output += signal.sosfiltfilt(sos, x, axis=-1) diff --git a/libs/gwftools/poetry.lock b/libs/utils/poetry.lock similarity index 100% rename from libs/gwftools/poetry.lock rename to libs/utils/poetry.lock diff --git a/libs/signal/pyproject.toml b/libs/utils/pyproject.toml similarity index 66% rename from libs/signal/pyproject.toml rename to libs/utils/pyproject.toml index 1984a92..363c3f3 100644 --- a/libs/signal/pyproject.toml +++ b/libs/utils/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] -name = "deepclean.signal" +name = "deepclean.utils" version = "0.0.1" -description = "DeepClean signal processing utilities" +description = "General deepclean utilities" authors = ["Alec Gunny "] # readme = "README.md" packages = [ @@ -10,7 +10,6 @@ packages = [ [tool.poetry.dependencies] python = "^3.8,<3.11" -numpy = "^1.21" scipy = "^1.7" [tool.poetry.dev-dependencies] @@ -18,4 +17,4 @@ pytest = "^6.2" [build-system] requires = ["poetry>=1.2"] -build-backend = "poetry.masonry.api" +build-backend = "poetry.core.masonry.api" diff --git a/libs/gwftools/tests/test_channels.py b/libs/utils/tests/test_channels.py similarity index 100% rename from libs/gwftools/tests/test_channels.py rename to libs/utils/tests/test_channels.py diff --git a/libs/gwftools/tests/test_frames.py b/libs/utils/tests/test_frames.py similarity index 100% rename from libs/gwftools/tests/test_frames.py rename to libs/utils/tests/test_frames.py diff --git a/libs/gwftools/tests/test_io.py b/libs/utils/tests/test_io.py similarity index 100% rename from libs/gwftools/tests/test_io.py rename to libs/utils/tests/test_io.py diff --git a/ml4gw b/ml4gw new file mode 160000 index 0000000..ce861a1 --- /dev/null +++ b/ml4gw @@ -0,0 +1 @@ +Subproject commit ce861a1645b6c52fbadebf46b03f8ce634981355 diff --git a/mldatafind b/mldatafind new file mode 160000 index 0000000..a820212 --- /dev/null +++ b/mldatafind @@ -0,0 +1 @@ +Subproject commit a8202128ce23167db331423f912b99e262ad829e diff --git a/projects/sandbox/train/poetry.lock b/projects/sandbox/train/poetry.lock index 93e8f80..e9c47e7 100644 --- a/projects/sandbox/train/poetry.lock +++ b/projects/sandbox/train/poetry.lock @@ -1,19 +1,23 @@ [[package]] name = "astropy" -version = "4.3.1" +version = "5.1.1" description = "Astronomy and astrophysics core library" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] -numpy = ">=1.17" -pyerfa = ">=1.7.3" +numpy = ">=1.18" +packaging = ">=19.0" +pyerfa = ">=2.0" +PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.1)", "certifi", "dask", "h5py", "beautifulsoup4", "html5lib", "bleach", "PyYAML (>=3.13)", "pandas", "sortedcontainers", "pytz", "jplephem", "matplotlib (>=3.0,!=3.4.0)", "mpmath", "asdf (>=2.6)", "bottleneck", "ipython (>=4.2)", "pytest"] -docs = ["sphinx (<4)", "sphinx-astropy (>=1.3)", "pytest", "PyYAML (>=3.13)", "scipy (>=1.1)", "matplotlib (>=3.1,!=3.4.0)", "sphinx-changelog (>=1.1.0)", "packaging"] -test = ["pytest-astropy (>=0.8)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "packaging"] +all = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", 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Open, modern and actively maintained alternative to Chardet." +category = "main" +optional = false +python-versions = ">=3.6.0" + +[package.extras] +unicode_backport = ["unicodedata2"] + +[[package]] +name = "ciecplib" +version = "0.7.1" +description = "A python client for SAML/ECP authentication" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +cryptography = ">=36.0.0" +requests = "*" +requests-ecp = "*" + +[package.extras] +docs = ["sphinx", "sphinx-argparse", "sphinx-automodapi", "sphinx-rtd-theme", "sphinx-tabs"] +kerberos = ["requests-ecp"] +manpages = ["argparse-manpage"] +tests = ["pytest (>=3.9.0)", "pytest-cov", "requests-mock"] + [[package]] name = "colorama" -version = "0.4.5" +version = "0.4.6" description = "Cross-platform colored terminal text." -category = "dev" +category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" [[package]] -name = "deepclean.architectures" -version = "0.0.1" -description = "DeepClean neural network architecture zoo" +name = "contourpy" +version = "1.0.6" +description = "Python library for calculating contours of 2D quadrilateral grids" category = "main" optional = false -python-versions = "^3.8,<3.10" -develop = true +python-versions = ">=3.7" [package.dependencies] -torch = "^1.0" +numpy = ">=1.16" -[package.source] -type = "directory" -url = "../../../libs/architectures" +[package.extras] +bokeh = ["bokeh", "selenium"] +docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] +test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] +test-minimal = ["pytest"] +test-no-codebase = ["pytest", "matplotlib", "pillow"] [[package]] -name = "deepclean.base" -version = "0.0.1" -description = "Dummy base library for constraining versions" +name = "corner" +version = "2.2.1" +description = "Make some beautiful corner plots" category = "main" optional = false -python-versions = "^3.8,<3.11" -develop = true +python-versions = ">=3.6" [package.dependencies] -astropy = "<5.0" -numpy = "<1.23" -packaging = "<21.0" -scipy = "<1.8" +matplotlib = ">=2.1" -[package.source] -type = "directory" -url = "../../../libs/base" +[package.extras] +arviz = ["arviz (>=0.9)"] +dev = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml", "arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme", "pre-commit", "flake8", "pep517", "twine"] +docs = ["arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme"] +test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] -name = "deepclean.export" -version = "0.0.1" -description = "Model export utilities" +name = "cryptography" +version = "38.0.4" +description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "main" optional = false -python-versions = "^3.8,<3.10" -develop = true +python-versions = ">=3.6" [package.dependencies] -torch = "^1.0" +cffi = ">=1.12" -[package.source] -type = "directory" -url = "../../../libs/export" +[package.extras] +docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1)", "sphinx-rtd-theme"] +docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] +pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"] +sdist = ["setuptools-rust (>=0.11.4)"] +ssh = ["bcrypt (>=3.1.5)"] +test = ["pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] + +[[package]] +name = "cycler" +version = "0.11.0" +description = "Composable style cycles" +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "decorator" +version = "5.1.1" +description = "Decorators for Humans" +category = "main" +optional = false +python-versions = ">=3.5" [[package]] -name = "deepclean.gwftools" +name = "deepclean-architectures" version = "0.0.1" -description = "Utilites for managing gwf file conventions" +description = "DeepClean neural network architecture zoo" category = "main" optional = false python-versions = "^3.8,<3.11" develop = true [package.dependencies] -"deepclean.base" = {path = "../base", develop = true} +torch = "^1.10" [package.source] type = "directory" -url = "../../../libs/gwftools" +url = "../../../libs/architectures" [[package]] -name = "deepclean.logging" +name = "deepclean-logging" version = "0.0.1" description = "Utilities for standardizing logging practices" category = "main" @@ -126,53 +236,222 @@ type = "directory" url = "../../../libs/logging" [[package]] -name = "deepclean.signal" +name = "deepclean-trainer" version = "0.0.1" -description = "DeepClean signal processing utilities" +description = "DeepClean training helper functions" category = "main" optional = false python-versions = "^3.8,<3.11" develop = true [package.dependencies] -numpy = "^1.21" -scipy = "^1.7" +"deepclean.architectures" = {path = "../architectures", develop = true} +"deepclean.utils" = {path = "../utils", develop = true} +ml4gw = {path = "../../ml4gw", develop = true} +torch = "^1.10" +typeo = {git = "https://github.com/ML4GW/typeo.git", branch = "main"} [package.source] type = "directory" -url = "../../../libs/signal" +url = "../../../libs/trainer" [[package]] -name = "deepclean.trainer" +name = "deepclean-utils" version = "0.0.1" -description = "DeepClean training helper functions" +description = "General deepclean utilities" category = "main" optional = false -python-versions = "^3.8,<3.10" +python-versions = "^3.8,<3.11" develop = true [package.dependencies] -"deepclean.architectures" = {path = "../architectures", develop = true} -"deepclean.base" = {path = "../base", develop = true} -"deepclean.export" = {path = "../export", develop = true} -"deepclean.signal" = {path = "../signal", develop = true} -"hermes.typeo" = "^0.1.5" -torch = "^1.0" +scipy = "^1.7" [package.source] type = "directory" -url = "../../../libs/trainer" +url = "../../../libs/utils" [[package]] -name = "hermes.typeo" -version = "0.1.5" -description = "Utilities for running functions as scripts" +name = "dill" +version = "0.3.6" +description = "serialize all of python" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +graph = ["objgraph (>=1.7.2)"] + +[[package]] +name = "dqsegdb2" +version = "1.1.3" +description = "Simplified python interface to DQSEGDB" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +igwn-auth-utils = ">=0.3.1" +ligo-segments = ">=1.0.0" + +[package.extras] +test = ["requests-mock (>=1.5.0)", "pytest-cov (>=2.5.1)", "pytest (>=2.9.2)"] +lint = ["flake8-bandit", "flake8 (>=3.7.0)"] +docs = ["sphinx-immaterial", "sphinx-automodapi", "sphinx"] + +[[package]] +name = "dynesty" +version = "2.0.2" +description = "A dynamic nested sampling package for computing Bayesian posteriors and evidences." +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "emcee" +version = "3.1.3" +description = "The Python ensemble sampling toolkit for MCMC" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +numpy = "*" + +[package.extras] +extras = ["h5py", "scipy"] +tests = ["pytest", "pytest-cov", "coverage"] + +[[package]] +name = "fonttools" +version = "4.38.0" +description = "Tools to manipulate font files" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] +graphite = ["lz4 (>=1.7.4.2)"] +interpolatable = ["scipy", "munkres"] +lxml = ["lxml (>=4.0,<5)"] +pathops = ["skia-pathops (>=0.5.0)"] +plot = ["matplotlib"] +repacker = ["uharfbuzz (>=0.23.0)"] +symfont = ["sympy"] +type1 = ["xattr"] +ufo = ["fs (>=2.2.0,<3)"] +unicode = ["unicodedata2 (>=14.0.0)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] + +[[package]] +name = "gssapi" +version = "1.8.2" +description = "Python GSSAPI Wrapper" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +decorator = "*" + +[[package]] +name = "gwdatafind" +version = "1.1.3" +description = "The GWDataFind data discovery client" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +igwn-auth-utils = ">=0.3.1" +ligo-segments = "*" + +[package.extras] +docs = ["numpydoc", "sphinx (>=4.4.0)", "sphinx-argparse", "sphinx-automodapi", "sphinx-rtd-theme"] +lint = ["flake8", "flake8-bandit", "flake8-docstrings", "radon"] +test = ["pytest (>=2.8.0)", "pytest-cov", "requests-mock"] + +[[package]] +name = "gwosc" +version = "0.6.1" +description = "A python interface to the GW Open Science data archive" +category = "main" +optional = false +python-versions = ">=3.5" + +[package.dependencies] +requests = ">=1.0.0" + +[package.extras] +test = ["requests-mock (>=1.5.0)", "pytest-socket", "pytest-cov", "pytest (>=2.7.0)"] +docs = ["sphinx-rtd-theme", "sphinx-automodapi", "sphinx"] + +[[package]] +name = "gwpy" +version = "2.1.5" +description = "A python package for gravitational-wave astrophysics" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +astropy = ">=4.0" +dqsegdb2 = "*" +gwdatafind = ">=1.1.0" +gwosc = ">=0.5.3" +h5py = ">=2.8.0" +ligo-segments = ">=1.0.0" +ligotimegps = ">=1.2.1" +matplotlib = ">=3.3.0" +numpy = ">=1.16" +python-dateutil = "*" +scipy = ">=1.2.0" +tqdm = ">=4.10.0" + +[package.extras] +conda = ["python-framel (>=8.40.1)", "python-nds2-client", "python-ldas-tools-framecpp"] +dev = ["ciecplib", "maya", "pandas", "psycopg2", "pymysql", "pyrxp", "sqlalchemy", "uproot (>=3.11)", "uproot3", "astropy (<5.1a0)", "lalsuite", "lscsoft-glue", "pycbc (>=1.13.4)", "python-ligo-lw (>=1.7.0)"] +docs = ["numpydoc (>=0.8.0)", "requests", "sphinx (>=4.0.0)", "sphinx-automodapi", "sphinx-material (>=0.0.32)", "sphinx-panels (>=0.6.0)", "sphinxcontrib-programoutput"] +test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "pytest-cov (>=2.4.0)", "pytest-socket", "pytest-xdist", "requests-mock"] + +[[package]] +name = "h5py" +version = "3.7.0" +description = "Read and write HDF5 files from Python" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +numpy = ">=1.14.5" + +[[package]] +name = "idna" +version = "3.4" +description = "Internationalized Domain Names in Applications (IDNA)" category = "main" optional = false -python-versions = ">=3.8,<4.0" +python-versions = ">=3.5" + +[[package]] +name = "igwn-auth-utils" +version = "0.3.1" +description = "Authorisation utilities for IGWN" +category = "main" +optional = false +python-versions = ">=3.6" [package.dependencies] -toml = ">=0.10.2,<0.11.0" +cryptography = ">=2.3" +requests = ">=2.14" +safe-netrc = ">=1.0.0" +scitokens = ">=1.7.0" + +[package.extras] +test = ["safe-netrc (>=1.0.0)", "requests-mock", "requests (>=2.14)", "pytest-cov", "pytest (>=3.9.1)"] +lint = ["flake8-bandit", "flake8 (>=3.7.0)"] +docs = ["sphinx-immaterial-igwn", "sphinx (>=4.0.0)"] [[package]] name = "iniconfig" @@ -182,9 +461,108 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "kiwisolver" +version = "1.4.4" +description = "A fast implementation of the Cassowary constraint solver" +category = "main" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "ligo-segments" +version = "1.4.0" +description = "Representations of semi-open intervals" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +six = "*" + +[[package]] +name = "ligotimegps" +version = "2.0.1" +description = "A pure-python version of lal.LIGOTimeGPS" +category = "main" +optional = false +python-versions = ">=3.4" + +[[package]] +name = "lxml" +version = "4.9.1" +description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*" + +[package.extras] +cssselect = ["cssselect (>=0.7)"] +html5 = ["html5lib"] +htmlsoup = ["beautifulsoup4"] +source = ["Cython (>=0.29.7)"] + +[[package]] +name = "matplotlib" +version = "3.6.2" +description = "Python plotting package" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +contourpy = ">=1.0.1" +cycler = ">=0.10" +fonttools = ">=4.22.0" +kiwisolver = ">=1.0.1" +numpy = ">=1.19" +packaging = ">=20.0" +pillow = ">=6.2.0" +pyparsing = ">=2.2.1" +python-dateutil = ">=2.7" +setuptools_scm = ">=7" + +[[package]] +name = "ml4gw" +version = "0.0.1" +description = "Tools for building training torch models on gravitational wave data" +category = "main" +optional = false +python-versions = ">=3.8,<3.11" +develop = true + +[package.dependencies] +bilby = "^1.1" +gwpy = "^2.1" +torch = "^1.10" +torchtyping = "^0.1" + +[package.source] +type = "directory" +url = "../../../ml4gw" + +[[package]] +name = "mldatafind" +version = "0.1.0" +description = "Tools for gravitational wave data discovery" +category = "main" +optional = false +python-versions = ">=3.8,<3.11" +develop = true + +[package.dependencies] +ciecplib = "^0.7.1" +numpy = ">=1.22.0,<1.22.4" +requests-gssapi = "^1.2.3" +scipy = "<1.8" + +[package.source] +type = "directory" +url = "../../../mldatafind" + [[package]] name = "numpy" -version = "1.22.4" +version = "1.22.3" description = "NumPy is the fundamental package for array computing with Python." category = "main" optional = false @@ -192,14 +570,45 @@ python-versions = ">=3.8" [[package]] name = "packaging" -version = "20.9" +version = "21.3" description = "Core utilities for Python packages" category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +python-versions = ">=3.6" [package.dependencies] -pyparsing = ">=2.0.2" +pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" + +[[package]] +name = "pandas" +version = "1.5.2" +description = "Powerful data structures for data analysis, time series, and statistics" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = [ + {version = ">=1.20.3", markers = "python_version < \"3.10\""}, + {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, +] +python-dateutil = ">=2.8.1" +pytz = ">=2020.1" + +[package.extras] +test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] + +[[package]] +name = "pillow" +version = "9.3.0" +description = "Python Imaging Library (Fork)" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] name = "pluggy" @@ -221,6 +630,14 @@ category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +[[package]] +name = "pycparser" +version = "2.21" +description = "C parser in Python" +category = "main" +optional = false +python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" + [[package]] name = "pyerfa" version = "2.0.0.1" @@ -236,6 +653,20 @@ numpy = ">=1.17" docs = ["sphinx-astropy (>=1.3)"] test = ["pytest", "pytest-doctestplus (>=0.7)"] +[[package]] +name = "pyjwt" +version = "2.6.0" +description = "JSON Web Token implementation in Python" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +crypto = ["cryptography (>=3.4.0)"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface"] +tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] + [[package]] name = "pyparsing" version = "3.0.9" @@ -268,6 +699,91 @@ toml = "*" [package.extras] testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +[[package]] +name = "python-dateutil" +version = "2.8.2" +description = "Extensions to the standard Python datetime module" +category = "main" +optional = false +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" + +[package.dependencies] +six = ">=1.5" + +[[package]] +name = "pytz" +version = "2022.6" +description = "World timezone definitions, modern and historical" +category = "main" +optional = false +python-versions = "*" + 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"sha256:47cc05d99aaa09c9e72ed5809b60e7ba354e64b59c9c173ac3018642d8bb41fc"}, + {file = "urllib3-1.26.13.tar.gz", hash = "sha256:c083dd0dce68dbfbe1129d5271cb90f9447dea7d52097c6e0126120c521ddea8"}, +] diff --git a/projects/sandbox/train/poetry.toml b/projects/sandbox/train/poetry.toml deleted file mode 100644 index 59c390d..0000000 --- a/projects/sandbox/train/poetry.toml +++ /dev/null @@ -1,2 +0,0 @@ -[virtualenvs] -create = false diff --git a/projects/sandbox/train/pyproject.toml b/projects/sandbox/train/pyproject.toml index 120063c..d3105ac 100644 --- a/projects/sandbox/train/pyproject.toml +++ b/projects/sandbox/train/pyproject.toml @@ -1,7 +1,7 @@ [tool.poetry] name = "train" version = "0.0.1" -description = "Training DeepClean on archival NDS2 data" +description = "Training DeepClean on a single dataset" authors = ["Alec Gunny "] readme = "README.md" @@ -9,15 +9,22 @@ readme = "README.md" train = "train:main" [tool.poetry.dependencies] -python = "^3.8,<3.10" +python = "^3.8,<3.11" +mldatafind = {path = "../../../mldatafind", develop = true} +gwpy = "^2.1" -"deepclean.gwftools" = {path = "../../../libs/gwftools", develop = true} "deepclean.logging" = {path = "../../../libs/logging", develop = true} "deepclean.trainer" = {path = "../../../libs/trainer", develop = true, extras=["wrapper"]} +[[tool.poetry.source]] +name = "torch" +url = "https://download.pytorch.org/whl/cu116" +secondary = true +default = false + [tool.poetry.dev-dependencies] pytest = "^6.2" [build-system] requires = ["poetry>=1.2"] -build-backend = "poetry.masonry.api" +build-backend = "poetry.core.masonry.api" From abb1f5cc7784f0d5b4317ec44cc849826be04ddb Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 6 Dec 2022 16:58:24 -0800 Subject: [PATCH 02/31] simplifying train project --- mldatafind | 2 +- projects/sandbox/train/train.py | 69 +++++++++------------------------ 2 files changed, 19 insertions(+), 52 deletions(-) diff --git a/mldatafind b/mldatafind index a820212..2e5b166 160000 --- a/mldatafind +++ b/mldatafind @@ -1 +1 @@ -Subproject commit a8202128ce23167db331423f912b99e262ad829e +Subproject commit 2e5b166f53c1e2fde7c02708f73eacd15ec57d5f diff --git a/projects/sandbox/train/train.py b/projects/sandbox/train/train.py index b6218f1..a5dcd74 100644 --- a/projects/sandbox/train/train.py +++ b/projects/sandbox/train/train.py @@ -4,28 +4,20 @@ import numpy as np from deepclean.gwftools.channels import ChannelList, get_channels -from deepclean.gwftools.io import find as find_data from deepclean.logging import configure_logging from deepclean.trainer.wrapper import trainify +from mldatafind.io import read_timeseries -# note that this function decorator acts both to -# wrap this function such that the outputs of it -# (i.e. the training and possible validation data) -# get passed as inputs to deepclean.trainer.trainer.train, -# as well as to expose these arguments _as well_ as those -# from deepclean.trainer.trainer.train to command line -# execution and parsing @trainify def main( + output_directory: Path, + data_directory: Path, channels: ChannelList, t0: float, duration: float, sample_rate: float, - output_directory: Path, - data_path: Optional[Path] = None, valid_frac: Optional[float] = None, - force_download: bool = False, verbose: bool = False, **kwargs, ): @@ -36,22 +28,10 @@ def main( specifed by its start time and length, optionally reserving some fraction of the data for validation. - If a path to existing data is specified, an attempt will - be made to pull the approriate data from that file. - For each of the specified channels, if the corresponding - data is either missing, has the incorrect initial timestamp, - or is insufficiently long, a `ValueError` will be raised. - - Otherwise, the specified data will be fetched from the - NDS2 server. If a path to data is still specified but - does not exist, the appropriate directory will be - created and the downloaded data will be written to a file - in that directory. If the specified data path doesn't - adhere to GW file standards, it will be assumed that the - path is a directory and the data will be written to a - GW-formatted file in that directory with prefix `"deepclean_train"`. - Args: + data_directory: + Directory containing files with the requisite + stretch of data in HDF5 format. channels: Either a list of channels to use during training, or a path to a file containing these channels separated @@ -71,23 +51,9 @@ def main( them have a duration shorter than this value. sample_rate: Rate at which to resample witness and strain timeseries. - data_path: - Path to existing data stored as an HDF5 file. If this path - exists and is a file, an attempt will be made to load data - from this path. If this path exists but is a directory, - a filename to which the downloaded data will be written - will be generated in this directory of the form - `"deepclean_train-{t0}-{duration}.h5"`. Otherwise if the - path does not exist, whether it refers to a file explicitly - or a directory to which to form a filename as above will - be inferred by whether the name of the file adheres to - GW file naming conventions. valid_frac: Fraction of training data to reserve for validation, split chronologically - force_download: - Whether to re-download data even if `data_path` is an - existing file. verbose: Indicates whether to log at DEBUG or INFO level @@ -102,19 +68,20 @@ def main( output_directory.mkdir(parents=True, exist_ok=True) configure_logging(output_directory / "train.log", verbose) + channels = get_channels(channels) - data = find_data( - channels, - t0, - duration, - sample_rate, - data_path=data_path, - force_download=force_download, - ) + tf = t0 + duration - # create the input and target arrays from the collected data - X = np.stack([data[channel] for channel in channels[1:]]) - y = data[channels[0]] + X = read_timeseries(data_directory, channels, t0, tf, array_like=True) + if X.shape[-1] != (duration * sample_rate): + inferred_sample_rate = X.shape[-1] / duration + raise ValueError( + "Data found in directory {} contains data " + "with sample rate {}, expected sample rate {}".format( + data_directory, inferred_sample_rate, sample_rate + ) + ) + y, X = np.split(X, [1], axis=0) # if we didn't specify to create any validation data, # return these arrays as-is From 25b5ef8772db7442a7e4982f692bd0b8e5e58f33 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Thu, 23 Feb 2023 11:19:52 -0800 Subject: [PATCH 03/31] start of major overhaul --- hermes | 2 +- .../architectures/convolutional_autoencoder.py | 2 +- libs/trainer/deepclean/trainer/__init__.py | 1 - libs/trainer/deepclean/trainer/criterion.py | 7 ++----- libs/trainer/deepclean/trainer/trainer.py | 11 +++++++++-- libs/trainer/deepclean/trainer/utils.py | 12 ++++++------ ml4gw | 2 +- mldatafind | 2 +- projects/sandbox/pyproject.toml | 13 ++++++------- projects/sandbox/train/train.py | 4 ++-- 10 files changed, 29 insertions(+), 27 deletions(-) diff --git a/hermes b/hermes index 30df0c7..12e19f1 160000 --- a/hermes +++ b/hermes @@ -1 +1 @@ -Subproject commit 30df0c7a364b43d99cd0d403eedbed96d03ac7a9 +Subproject commit 12e19f1887616ebb025846128e08e99e7338619e diff --git a/libs/architectures/deepclean/architectures/convolutional_autoencoder.py b/libs/architectures/deepclean/architectures/convolutional_autoencoder.py index c5c2f93..577b562 100644 --- a/libs/architectures/deepclean/architectures/convolutional_autoencoder.py +++ b/libs/architectures/deepclean/architectures/convolutional_autoencoder.py @@ -13,7 +13,7 @@ def __init__( padding: int, transpose: bool, output_padding: Optional[int] = None, - activation: nn.Module = nn.Tanh, + activation: nn.Module = nn.ELU, ) -> None: super().__init__() diff --git a/libs/trainer/deepclean/trainer/__init__.py b/libs/trainer/deepclean/trainer/__init__.py index baa6585..7e77968 100644 --- a/libs/trainer/deepclean/trainer/__init__.py +++ b/libs/trainer/deepclean/trainer/__init__.py @@ -1,2 +1 @@ from .criterion import CompositePSDLoss -from .dataloader import ChunkedTimeSeriesDataset diff --git a/libs/trainer/deepclean/trainer/criterion.py b/libs/trainer/deepclean/trainer/criterion.py index 4d1f090..7b3dd45 100644 --- a/libs/trainer/deepclean/trainer/criterion.py +++ b/libs/trainer/deepclean/trainer/criterion.py @@ -19,7 +19,6 @@ def __init__( overlap: Optional[float] = None, freq_low: Union[Frequency, None] = None, freq_high: Union[Frequency, None] = None, - device: str = "cpu", ): super().__init__() @@ -40,7 +39,8 @@ def __init__( # implementation of the welch transform if not mask[:2].any(): fast = True - self.mask = torch.tensor(mask, dtype=torch.bool).to(device) + mask = torch.tensor(mask, dtype=torch.bool) + self.register_buffer("mask", mask) elif freq_low is None and freq_high is None: # no frequencies were specified, so ignore the mask self.mask = None @@ -60,7 +60,6 @@ def __init__( fftlength, overlap, average="mean", - device=device, fast=fast, ) self.asd = asd @@ -100,7 +99,6 @@ def __init__( asd: bool = False, freq_low: Union[float, List[float], None] = None, freq_high: Union[float, List[float], None] = None, - device: str = "cpu", ): super().__init__() if not 0 <= alpha <= 1: @@ -121,7 +119,6 @@ def __init__( fftlength=fftlength, overlap=overlap, asd=asd, - device=device, ) self.mse_loss = torch.nn.MSELoss() diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 156a8ff..5f9c330 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -229,11 +229,14 @@ def train( fftlength=fftlength, overlap=overlap, asd=True, - device=device, freq_low=freq_low, freq_high=freq_high, ) - trainer = Trainer(criterion, model, lr, weight_decay, use_amp) + if alpha > 0: + criterion.psd_loss.to(device) + criterion.psd_loss.welch.to(device) + + trainer = Trainer(model, criterion, lr, weight_decay, use_amp) checkpointer = Checkpointer( output_directory, trainer.optimizer, @@ -273,6 +276,10 @@ def train( welch = SpectralDensity( sample_rate, fftlength, average="median", fast=True ) + + # reset batch sizes to be more manageable since + # gradient computation is analysis is expensive + valid_data.batch_size = train_data.batch_size = 64 analyze_model(output_directory, model, welch, train_data, valid_data) # now create a version of the model which diff --git a/libs/trainer/deepclean/trainer/utils.py b/libs/trainer/deepclean/trainer/utils.py index fd02945..d9c04f3 100644 --- a/libs/trainer/deepclean/trainer/utils.py +++ b/libs/trainer/deepclean/trainer/utils.py @@ -27,7 +27,7 @@ def update(self, loss, num_samples) -> None: self.profiler.step() if loss is not None: - self.loss += loss + self.loss += loss * num_samples self.samples_seen += num_samples def stop(self) -> Tuple[float, float, float]: @@ -35,7 +35,7 @@ def stop(self) -> Tuple[float, float, float]: self.profiler.stop() end_time = time.time() - duration = self.start_time - end_time + duration = end_time - self.start_time throughput = self.samples_seen / duration loss = self.loss / self.samples_seen return loss, duration, throughput @@ -59,7 +59,7 @@ def backward( raise ValueError("Encountered nan loss") elif isnan: return None - return loss.item() + return loss def backwardize(forward_fn: Callable) -> Callable: @@ -70,7 +70,7 @@ def step(obj, X: torch.Tensor, y: torch.Tensor) -> Optional[float]: # do forward step in mixed precision # if a gradient scaler got passed with torch.autocast("cuda", enabled=obj.scaler is not None): - loss = forward_fn(X, y) + loss = forward_fn(obj, X, y) return backward(loss, obj.optimizer, obj.scaler) return step @@ -112,7 +112,7 @@ def __call__( self.model.train() tracker = EpochTracker(profiler) for witnesses, strain in train_data: - loss = self.step(witnesses, strain) + loss = self.step(witnesses, strain).item() tracker.update(loss, len(witnesses)) train_loss, duration, throughput = tracker.stop() @@ -128,7 +128,7 @@ def __call__( self.model.eval() with torch.no_grad(): for witnesses, strain in valid_data: - loss = self.forward(witnesses, strain) + loss = self.forward(witnesses, strain).item() tracker.update(loss, len(witnesses)) valid_loss, _, __ = tracker.stop() msg += f", Valid Loss: {valid_loss:.4e}" diff --git a/ml4gw b/ml4gw index ce861a1..0fc31b1 160000 --- a/ml4gw +++ b/ml4gw @@ -1 +1 @@ -Subproject commit ce861a1645b6c52fbadebf46b03f8ce634981355 +Subproject commit 0fc31b1f7563a0e266ddd93c9e3cb32b8aac0eb8 diff --git a/mldatafind b/mldatafind index 2e5b166..3e71f58 160000 --- a/mldatafind +++ b/mldatafind @@ -1 +1 @@ -Subproject commit 2e5b166f53c1e2fde7c02708f73eacd15ec57d5f +Subproject commit 3e71f581bb92ce84e69cb3f547d8fc3ff25530a8 diff --git a/projects/sandbox/pyproject.toml b/projects/sandbox/pyproject.toml index 15c5de1..f980c7c 100644 --- a/projects/sandbox/pyproject.toml +++ b/projects/sandbox/pyproject.toml @@ -34,15 +34,15 @@ channels = [ sample_rate = 4096 verbose = false -train_t0 = 1243278913 +train_t0 = 1251335314 train_duration = 4096 infer_kernel_length = 1 infer_batch_size = 64 inference_sampling_rate = 512 -test_t0 = 1243283009 -test_duration = 1024 +test_t0 = 1251339410 +test_duration = 16384 max_latency = 0.5 freq_low = 55 @@ -55,7 +55,7 @@ autoencoder = {} [tool.typeo.scripts.train] # paths output_directory = "${PROJECT_DIR}" -data_path = "${DATA_DIR}" +data_directory = "${DATA_DIR}" # data parameters channels = "${base.channels}" @@ -66,8 +66,7 @@ valid_frac = 0.25 # data loading parameters sample_rate = "${base.sample_rate}" kernel_length = 8 -kernel_stride = 0.25 -chunk_length = 0 +kernel_stride = 0.0625 # preprocessing parameters freq_low = "${base.freq_low}" @@ -75,7 +74,7 @@ freq_high = "${base.freq_high}" filter_order = 8 # optimization parameters -batch_size = 32 +batch_size = 256 max_epochs = 500 lr = 3.2e-2 weight_decay = 1e-5 diff --git a/projects/sandbox/train/train.py b/projects/sandbox/train/train.py index a5dcd74..a7402d9 100644 --- a/projects/sandbox/train/train.py +++ b/projects/sandbox/train/train.py @@ -72,7 +72,7 @@ def main( channels = get_channels(channels) tf = t0 + duration - X = read_timeseries(data_directory, channels, t0, tf, array_like=True) + X, _ = read_timeseries(data_directory, channels, t0, tf, array_like=True) if X.shape[-1] != (duration * sample_rate): inferred_sample_rate = X.shape[-1] / duration raise ValueError( @@ -81,7 +81,7 @@ def main( data_directory, inferred_sample_rate, sample_rate ) ) - y, X = np.split(X, [1], axis=0) + [y], X = np.split(X, [1], axis=0) # if we didn't specify to create any validation data, # return these arrays as-is From 78eb5e33e921d0ae97d0717504173ab4ee3afd5c Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Fri, 24 Feb 2023 16:13:48 -0800 Subject: [PATCH 04/31] getting new training running --- .gitmodules | 3 + .../deepclean/architectures/__init__.py | 140 +- libs/logging/deepclean/logging.py | 58 +- libs/trainer/deepclean/trainer/__init__.py | 1 + libs/trainer/deepclean/trainer/trainer.py | 16 +- libs/trainer/deepclean/trainer/utils.py | 7 +- libs/trainer/deepclean/trainer/wrapper.py | 145 -- libs/trainer/poetry.lock | 1181 +++++++++------- libs/trainer/pyproject.toml | 13 +- libs/trainer/tests/test_wrapper.py | 71 - projects/sandbox/pyproject.toml | 14 +- projects/sandbox/train/poetry.lock | 1189 ++++++++++------- projects/sandbox/train/pyproject.toml | 14 +- projects/sandbox/train/train.py | 150 ++- typeo | 1 + 15 files changed, 1627 insertions(+), 1376 deletions(-) delete mode 100644 libs/trainer/deepclean/trainer/wrapper.py delete mode 100644 libs/trainer/tests/test_wrapper.py create mode 160000 typeo diff --git a/.gitmodules b/.gitmodules index 657674b..3b74d1d 100644 --- a/.gitmodules +++ b/.gitmodules @@ -7,3 +7,6 @@ [submodule "mldatafind"] path = mldatafind url = git@github.com:ML4GW/mldatafind.git +[submodule "typeo"] + path = typeo + url = git@github.com:ML4GW/typeo.git diff --git a/libs/architectures/deepclean/architectures/__init__.py b/libs/architectures/deepclean/architectures/__init__.py index e1b4417..b33103f 100644 --- a/libs/architectures/deepclean/architectures/__init__.py +++ b/libs/architectures/deepclean/architectures/__init__.py @@ -2,138 +2,18 @@ from .convolutional_autoencoder import DeepCleanAE -architectures = {"autoencoder": DeepCleanAE} +def _wrap_arch(arch): + def func(*args, **kwargs): + def f(num_channels): + return arch(num_channels, *args, **kwargs) -def get_arch_fns(fn, fn_kwargs={}): - """Create functions for network architectures + return f - For each network architecture, create a function which - exposes architecture parameters as arguments and returns - the output of the passed function `fn` called with - the keyword arguments `fn_kwargs` and an argument - `architecture` which is itself a function that takes - as input the input shape to the network, and returns - the compiled architecture. + params = inspect.signature(arch).parameters + params = list(params.values())[1:] + func.__signature__ = inspect.Signature(params) + return func - As an example: - ```python - import argparse - from deepclean.architectures import get_arch_fns - def train(architecture, learning_rate, batch_size): - network = architecture(input_shape=21) - # do some training here - return - - # instantiate train_kwargs now, then update - # in-place later so that each arch_fn calls - # `train` with some command line arguments - train_kwargs = {} - arch_fns = get_arch_fns(train, train_kwargs) - - if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--learning-rate", type=float) - parser.add_argument("--batch-size", type=int) - parser.add_argument("--arch", choices=tuple(arch_fns), type=str) - args = vars(parser.parse_args()) - - arch = args.pop("arch") - fn = arch_fns[arch] - train_kwargs.update(args) - fn() - ``` - - The intended use case for this is for more complex - model architectures which may require different - sets of arguments, so that they can be simply - implemented with the same training function. - """ - - arch_fns = {} - for name, arch in architectures.items(): - - def arch_fn(**arch_kwargs): - # create a function which only takes the input - # shape as an argument and instantiates a network - # based on the architecture with that shape and - # the remaining kwargs - def get_arch(input_shape): - return arch(input_shape, **arch_kwargs) - - # pass the function to `fn` as a kwarg, - # then run `fn` with all the passed kwargs. - fn_kwargs["architecture"] = get_arch - return fn(**fn_kwargs) - - # now add all the architecture parameters other - # than the first, which is assumed to be some - # form of input shape, to the `arch_fn` we - # just created via the __signature__ attribute - params = [] - for i, param in enumerate(inspect.signature(arch).parameters.values()): - if i > 0: - params.append(param) - - arch_fn.__signature__ = inspect.Signature(parameters=params) - arch_fn.__name__ = name - arch_fns[name] = arch_fn - return arch_fns - - -def architecturize(f): - """ - Wrap a function so that if it's called without - any arguments, it will parse arguments from the - command line with a network architecture name - as a positional parameter with its own subparsers. - - For example, a script that looks like - - ```python - from typing import Callable - from training_library import do_some_training - from deepclean.architectures import architecturize - - - @architecturize - def my_func(architecture: Callable, learning_rate: float, batch_size: int): - num_witness_channels = 8 - network = architecture(num_witness_channels) - do_some_training(network, learning_rate, batch_size) - - - if __name__ == "__main__": - my_func() - ``` - - can be executed from the command line like - - ```console - python my_script.py --learning-rate 1e-3 --batch-size 32 autoencoder - ``` - - and the wrapper will take care of mapping `"autoencoder"` to the - corresponding architecture function which maps from an input - shape to an initialized `torch.nn.Module`. - """ - - # doing the unthinkable and putting this import - # here until I decide what I really want to do - # with this function - from hermes.typeo import typeo - - f_kwargs = {} - arch_fns = get_arch_fns(f, f_kwargs) - - def wrapper(**kwargs): - f_kwargs.update(kwargs) - - f_params = inspect.signature(f).parameters.values() - parameters = [p for p in f_params if p.name != "architecture"] - wrapper.__signature__ = inspect.Signature(parameters) - wrapper.__name__ = f.__name__ - wrapper.__doc__ = f.__doc__ - - return typeo(wrapper, **arch_fns) +architectures = {"autoencoder": _wrap_arch(DeepCleanAE)} diff --git a/libs/logging/deepclean/logging.py b/libs/logging/deepclean/logging.py index 6725a97..82600fc 100644 --- a/libs/logging/deepclean/logging.py +++ b/libs/logging/deepclean/logging.py @@ -1,21 +1,47 @@ import logging +import os import sys from typing import Optional -def configure_logging( - filename: Optional[str] = None, verbose: bool = False -) -> None: - log_format = "%(asctime)s - %(name)s - %(levelname)s - %(message)s" - logging.basicConfig( - format=log_format, - level=logging.DEBUG if verbose else logging.INFO, - stream=sys.stdout, - ) - - logger = logging.getLogger() - if filename is not None: - formatter = logging.Formatter(log_format) - handler = logging.FileHandler(filename=filename, mode="w") - handler.setFormatter(formatter) - logger.addHandler(handler) +class Logger: + def __init__(self): + self._logger = logging.getLogger() + self._formatter = logging.Formatter( + "%(asctime)s - %(name)s - %(levelname)s - %(message)s" + ) + self._cache = [] + + def set_logger( + self, name: str, filename: Optional[str] = None, verbose: bool = False + ) -> None: + self._logger = logging.getLogger(name) + if name in self._cache: + return self._logger + self._cache.append(name) + + level = logging.DEBUG if verbose else logging.INFO + self._logger.setLevel(level) + + use_stdout = os.getenv("DEEPCLEAN_LOG_STDOUT", "True") + if eval(use_stdout): + handler = logging.StreamHandler(stream=sys.stdout) + handler.setFormatter(self._formatter) + handler.setLevel(level) + self._logger.addHandler(handler) + if filename is not None: + handler = logging.FileHandler(filename=filename, mode="w") + handler.setFormatter(self._formatter) + handler.setLevel(level) + self._logger.addHandler(handler) + return self._logger + + def __getattr__(self, name): + logger = object.__getattribute__(self, "_logger") + try: + return logger.__getattribute__(name) + except AttributeError as e: + raise AttributeError from e + + +logger = Logger() diff --git a/libs/trainer/deepclean/trainer/__init__.py b/libs/trainer/deepclean/trainer/__init__.py index 7e77968..628f7d3 100644 --- a/libs/trainer/deepclean/trainer/__init__.py +++ b/libs/trainer/deepclean/trainer/__init__.py @@ -1 +1,2 @@ from .criterion import CompositePSDLoss +from .trainer import train diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 5f9c330..2c4129b 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -1,4 +1,3 @@ -import logging from functools import partial from pathlib import Path from typing import Callable, Optional, Tuple @@ -6,6 +5,7 @@ import numpy as np import torch +from deepclean.logging import logger from deepclean.trainer import CompositePSDLoss from deepclean.trainer.analysis import analyze_model from deepclean.trainer.utils import Checkpointer, Trainer @@ -210,7 +210,7 @@ def train( valid_data.y = output_scaler(valid_data.y) # Creating model, loss function, optimizer and lr scheduler - logging.info("Building and initializing model") + logger.info("Building and initializing model") model = architecture(len(X)).to(device) if init_weights is not None: # allow us to easily point to the best weights @@ -218,11 +218,11 @@ def train( if init_weights.is_dir(): init_weights = init_weights / "weights.pt" - logging.info(f"Initializing model from checkpoint '{init_weights}'") + logger.info(f"Initializing model from checkpoint '{init_weights}'") model.load_state_dict(torch.load(init_weights)) - logging.info(model) + logger.info(model) - logging.info("Initializing loss and optimizer") + logger.info("Initializing loss and optimizer") criterion = CompositePSDLoss( alpha, sample_rate, @@ -248,7 +248,7 @@ def train( ) torch.backends.cudnn.benchmark = True - logging.info("Beginning training loop") + logger.info("Beginning training loop") for epoch in range(max_epochs): if epoch == 0 and profile: profiler = torch.profiler.profile( @@ -261,7 +261,7 @@ def train( else: profiler = None - logging.info(f"=== Epoch {epoch + 1}/{max_epochs} ===") + logger.info(f"=== Epoch {epoch + 1}/{max_epochs} ===") train_loss, valid_loss = trainer(train_data, valid_data, profiler) stop = checkpointer(train_loss, valid_loss, model) if stop: @@ -272,7 +272,7 @@ def train( model.load_state_dict(torch.load(weights_path)) # generate some analyses of our model - logging.info("Performing post-hoc analysis on trained model") + logger.info("Performing post-hoc analysis on trained model") welch = SpectralDensity( sample_rate, fftlength, average="median", fast=True ) diff --git a/libs/trainer/deepclean/trainer/utils.py b/libs/trainer/deepclean/trainer/utils.py index d9c04f3..81fba03 100644 --- a/libs/trainer/deepclean/trainer/utils.py +++ b/libs/trainer/deepclean/trainer/utils.py @@ -1,4 +1,3 @@ -import logging import time from dataclasses import dataclass from functools import wraps @@ -8,6 +7,8 @@ import h5py import torch +from deepclean.logging import logger + DataGenerator = Iterable[Tuple[torch.nn.Module, torch.nn.Module]] @@ -116,7 +117,7 @@ def __call__( tracker.update(loss, len(witnesses)) train_loss, duration, throughput = tracker.stop() - logging.info( + logger.info( f"Duration {duration:0.2f}s, " f"Throughput {throughput:0.1f} samples/s" ) @@ -134,7 +135,7 @@ def __call__( msg += f", Valid Loss: {valid_loss:.4e}" else: valid_loss = None - logging.info(msg) + logger.info(msg) return train_loss, valid_loss diff --git a/libs/trainer/deepclean/trainer/wrapper.py b/libs/trainer/deepclean/trainer/wrapper.py deleted file mode 100644 index e644ca8..0000000 --- a/libs/trainer/deepclean/trainer/wrapper.py +++ /dev/null @@ -1,145 +0,0 @@ -import inspect - -from typeo import scriptify -from typeo.doc_utils import parse_doc, parse_help - -from deepclean.architectures import get_arch_fns -from deepclean.trainer.trainer import train - - -def _configure_wrapper(f, wrapper): - f_sig = inspect.signature(f) - train_sig = inspect.signature(train) - - parameters = [] - optional_parameters = [] - for p in f_sig.parameters.values(): - if p.name == "kwargs": - continue - elif p.default is inspect.Parameter.empty: - parameters.append(p) - else: - optional_parameters.append(p) - - for param in train_sig.parameters.values(): - if ( - param.name not in ("X", "y", "valid_data", "architecture") - and param.name not in f_sig.parameters - ): - parameters.append(param) - parameters = parameters + optional_parameters - - wrapper.__signature__ = inspect.Signature(parameters=parameters) - wrapper.__name__ = f.__name__ - - _, train_args = parse_doc(train) - f_doc, f_args = parse_doc(f) - - wrapper_args = "" - for p in parameters: - for args in [f_args, train_args]: - doc_str = parse_help(args, p.name) - if doc_str: - break - else: - continue - - wrapper_args += "\n" + " " * 8 + p.name + ":\n" - wrapper_args += " " * 12 + doc_str - wrapper.__doc__ = f_doc + "\n" + " " * 4 + "Args:\n" + wrapper_args - - -def trainify(f): - """Turn a data-generating function into a command line trainer - - Wraps the function `f`, which is assumed to generate training - and validation data, so that this data gets passed to - `deepclean.trainer.trainer.train`, but in such a way that - `f` can be called from the command line with all of the arguments - to `deepclean.trainer.trainer.train`, with the network architecture - as a positional parameter and its arguments as additional parameters - after that. - """ - - # initialize our training kwargs now and use them to - # create the wrapper network functions. Populate these - # args later within our wrapper function in-place - train_kwargs = {} - arch_fns = get_arch_fns(train, train_kwargs) - train_signature = inspect.signature(train) - - def wrapper(*args, **kwargs): - # use the passed function `f` to generate data - data = f(*args, **kwargs) - - # pass any args passed to this wrapper that - # `train` needs into the `train_kwargs` dictionary - for p, v in zip(inspect.signature(f).parameters, args): - if p in train_signature.parameters: - train_kwargs[p] = v - - # do the same for any kwargs that were passed here - for k, v in kwargs.items(): - if k in train_signature.parameters: - train_kwargs[k] = v - - # parse out the data that was returned from - # `f`, checking if validation data was included - if len(data) == 2: - X, y = data - valid_X = valid_y = None - elif len(data) == 4: - X, y, valid_X, valid_y = data - else: - raise ValueError( - "Can't process {} elements returned by function {}".format( - len(data), f.__name__ - ) - ) - - # add in the parsed data to our training kwargs - train_kwargs["X"] = X - train_kwargs["y"] = y - if valid_X is not None: - train_kwargs["valid_data"] = (valid_X, valid_y) - - # allow wrapper functionality to be utilized if - # `f` is called with an "arch" parameter - if "arch" in kwargs: - try: - arch_fn = arch_fns[kwargs["arch"]] - except KeyError: - raise ValueError( - "No network architecture named " + kwargs["arch"] - ) - - # grab any architecture-specific parameters from - # the arguments passed to `f` - arch_kwargs = {} - arch_sig = inspect.signature(arch_fn) - for k, v in kwargs.items(): - if k in arch_sig.parameters: - arch_kwargs[k] = v - - # run the architecture function, which will implicitly - # call deepclean.trainer.trainer.train under the - # hood with the arguments we populated into - # `train_kwargs` - result = arch_fn(**arch_kwargs) - else: - # otherwise just return the data, equivalent - # to running `f` without any wrapper functionality - result = data - - return result - - # create the appropriate signature, name, and documentation - # for the wrapper function we just created - _configure_wrapper(f, wrapper) - - # wrap this wrapper using typeo, so that it - # can be called from the command line exposing - # all training and architecture arguments. Each - # network architecture will be exposed as a - # subcommand with its own arguments - return scriptify(wrapper, **arch_fns) diff --git a/libs/trainer/poetry.lock b/libs/trainer/poetry.lock index 6e2ea2b..be871b9 100644 --- a/libs/trainer/poetry.lock +++ b/libs/trainer/poetry.lock @@ -1,23 +1,23 @@ [[package]] name = "astropy" -version = "5.1.1" +version = "5.2.1" description = "Astronomy and astrophysics core library" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -numpy = ">=1.18" +numpy = ">=1.20" packaging = ">=19.0" pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)"] -docs = ["sphinx (<4)", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (<3.1)"] -recommended = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] +docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "towncrier (<22.12.0)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] +recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] -test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "pooch"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] [[package]] name = "atomicwrites" @@ -29,21 +29,23 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope-interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope-interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] [[package]] name = "bilby" -version = "1.4.0" +version = "1.4.1" description = "A user-friendly Bayesian inference library" category = "main" optional = false @@ -81,7 +83,7 @@ numpy = "*" [[package]] name = "certifi" -version = "2022.9.24" +version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -100,14 +102,11 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "2.1.1" +version = "3.0.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" optional = false -python-versions = ">=3.6.0" - -[package.extras] -unicode_backport = ["unicodedata2"] +python-versions = "*" [[package]] name = "colorama" @@ -119,21 +118,21 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7 [[package]] name = "contourpy" -version = "1.0.6" +version = "1.0.7" description = "Python library for calculating contours of 2D quadrilateral grids" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] numpy = ">=1.16" [package.extras] -bokeh = ["bokeh", "selenium"] -docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] -test-minimal = ["pytest"] -test-no-codebase = ["pytest", "matplotlib", "pillow"] +bokeh = ["bokeh", "chromedriver", "selenium"] +docs = ["furo", "sphinx-copybutton"] +mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] +test = ["matplotlib", "pillow", "pytest"] +test-no-images = ["pytest"] [[package]] name = "corner" @@ -154,7 +153,7 @@ test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] name = "cryptography" -version = "38.0.4" +version = "39.0.1" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "main" optional = false @@ -164,12 +163,14 @@ python-versions = ">=3.6" cffi = ">=1.12" [package.extras] -docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1)", "sphinx-rtd-theme"] +docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"] +pep8test = ["black", "ruff", "mypy", "types-pytz", "types-requests", "check-manifest"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test-randomorder = ["pytest-randomly"] +tox = ["tox"] [[package]] name = "cycler" @@ -179,22 +180,6 @@ category = "main" optional = false python-versions = ">=3.6" -[[package]] -name = "deepclean-architectures" -version = "0.0.1" -description = "DeepClean neural network architecture zoo" -category = "main" -optional = false -python-versions = "^3.8,<3.11" -develop = true - -[package.dependencies] -torch = "^1.10" - -[package.source] -type = "directory" -url = "../architectures" - [[package]] name = "deepclean-utils" version = "0.0.1" @@ -235,13 +220,13 @@ igwn-auth-utils = ">=0.3.1" ligo-segments = ">=1.0.0" [package.extras] -test = ["requests-mock (>=1.5.0)", "pytest-cov (>=2.5.1)", "pytest (>=2.9.2)"] -lint = ["flake8-bandit", "flake8 (>=3.7.0)"] -docs = ["sphinx-immaterial", "sphinx-automodapi", "sphinx"] +docs = ["sphinx", "sphinx-automodapi", "sphinx-immaterial"] +lint = ["flake8 (>=3.7.0)", "flake8-bandit"] +test = ["pytest (>=2.9.2)", "pytest-cov (>=2.5.1)", "requests-mock (>=1.5.0)"] [[package]] name = "dynesty" -version = "2.0.2" +version = "2.1.0" description = "A dynamic nested sampling package for computing Bayesian posteriors and evidences." category = "main" optional = false @@ -249,7 +234,7 @@ python-versions = "*" [[package]] name = "emcee" -version = "3.1.3" +version = "3.1.4" description = "The Python ensemble sampling toolkit for MCMC" category = "main" optional = false @@ -313,8 +298,8 @@ python-versions = ">=3.5" requests = ">=1.0.0" [package.extras] -test = ["requests-mock (>=1.5.0)", "pytest-socket", "pytest-cov", "pytest (>=2.7.0)"] -docs = ["sphinx-rtd-theme", "sphinx-automodapi", "sphinx"] +docs = ["sphinx", "sphinx-automodapi", "sphinx-rtd-theme"] +test = ["pytest (>=2.7.0)", "pytest-cov", "pytest-socket", "requests-mock (>=1.5.0)"] [[package]] name = "gwpy" @@ -346,7 +331,7 @@ test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "p [[package]] name = "h5py" -version = "3.7.0" +version = "3.8.0" description = "Read and write HDF5 files from Python" category = "main" optional = false @@ -365,7 +350,7 @@ python-versions = ">=3.5" [[package]] name = "igwn-auth-utils" -version = "0.3.1" +version = "0.4.0" description = "Authorisation utilities for IGWN" category = "main" optional = false @@ -378,17 +363,32 @@ safe-netrc = ">=1.0.0" scitokens = ">=1.7.0" [package.extras] -test = ["safe-netrc (>=1.0.0)", "requests-mock", "requests (>=2.14)", "pytest-cov", "pytest (>=3.9.1)"] -lint = ["flake8-bandit", "flake8 (>=3.7.0)"] -docs = ["sphinx-immaterial-igwn", "sphinx (>=4.0.0)"] +docs = ["sphinx (>=4.0.0)", "sphinx-immaterial-igwn"] +lint = ["flake8 (>=3.7.0)", "flake8-bandit"] +test = ["pytest (>=3.9.1)", "pytest-cov", "requests (>=2.14)", "requests-mock", "safe-netrc (>=1.0.0)"] + +[[package]] +name = "importlib-resources" +version = "5.12.0" +description = "Read resources from Python packages" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} + +[package.extras] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] [[package]] name = "iniconfig" -version = "1.1.1" -description = "iniconfig: brain-dead simple config-ini parsing" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.7" [[package]] name = "kiwisolver" @@ -419,7 +419,7 @@ python-versions = ">=3.4" [[package]] name = "matplotlib" -version = "3.6.2" +version = "3.7.0" description = "Python plotting package" category = "main" optional = false @@ -429,18 +429,19 @@ python-versions = ">=3.8" contourpy = ">=1.0.1" cycler = ">=0.10" fonttools = ">=4.22.0" +importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""} kiwisolver = ">=1.0.1" -numpy = ">=1.19" +numpy = ">=1.20" packaging = ">=20.0" pillow = ">=6.2.0" -pyparsing = ">=2.2.1" +pyparsing = ">=2.3.1" python-dateutil = ">=2.7" setuptools_scm = ">=7" [[package]] name = "ml4gw" -version = "0.0.1" -description = "Tools for building training torch models on gravitational wave data" +version = "0.1.1" +description = "Tools for training torch models on gravitational wave data" category = "main" optional = false python-versions = ">=3.8,<3.11" @@ -458,26 +459,71 @@ url = "../../ml4gw" [[package]] name = "numpy" -version = "1.23.5" -description = "NumPy is the fundamental package for array computing with Python." +version = "1.24.2" +description = "Fundamental package for array computing in Python" category = "main" optional = false python-versions = ">=3.8" [[package]] -name = "packaging" -version = "21.3" -description = "Core utilities for Python packages" +name = "nvidia-cublas-cu11" +version = "11.10.3.66" +description = "CUBLAS native runtime libraries" category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3" [package.dependencies] -pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-nvrtc-cu11" +version = "11.7.99" +description = "NVRTC native runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-runtime-cu11" +version = "11.7.99" +description = "CUDA Runtime native Libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cudnn-cu11" +version = "8.5.0.96" +description = "cuDNN runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "packaging" +version = "23.0" +description = "Core utilities for Python packages" +category = "main" +optional = false +python-versions = ">=3.7" [[package]] name = "pandas" -version = "1.5.2" +version = "1.5.3" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false @@ -496,14 +542,14 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] [[package]] name = "pillow" -version = "9.3.0" +version = "9.4.0" description = "Python Imaging Library (Fork)" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] @@ -608,7 +654,7 @@ six = ">=1.5" [[package]] name = "pytz" -version = "2022.6" +version = "2022.7.1" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -624,7 +670,7 @@ python-versions = ">=3.6" [[package]] name = "requests" -version = "2.28.1" +version = "2.28.2" description = "Python HTTP for Humans." category = "main" optional = false @@ -632,7 +678,7 @@ python-versions = ">=3.7, <4" [package.dependencies] certifi = ">=2017.4.17" -charset-normalizer = ">=2,<3" +charset-normalizer = ">=2,<4" idna = ">=2.5,<4" urllib3 = ">=1.21.1,<1.27" @@ -653,19 +699,19 @@ test = ["pytest"] [[package]] name = "scipy" -version = "1.9.3" +version = "1.10.1" description = "Fundamental algorithms for scientific computing in Python" category = "main" optional = false -python-versions = ">=3.8" +python-versions = "<3.12,>=3.8" [package.dependencies] -numpy = ">=1.18.5,<1.26.0" +numpy = ">=1.19.5,<1.27.0" [package.extras] -test = ["pytest", "pytest-cov", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack"] -doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-panels (>=0.5.2)", "matplotlib (>2)", "numpydoc", "sphinx-tabs"] -dev = ["mypy", "typing-extensions", "pycodestyle", "flake8"] +test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"] [[package]] name = "scitokens" @@ -685,20 +731,20 @@ docs = ["sphinx"] [[package]] name = "setuptools" -version = "65.6.3" +version = "67.4.0" description = "Easily download, build, install, upgrade, and uninstall Python packages" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", 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category = "main" optional = false python-versions = ">=3.7.0" [package.dependencies] +nvidia-cublas-cu11 = {version = "11.10.3.66", markers = "platform_system == \"Linux\""} +nvidia-cuda-nvrtc-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} +nvidia-cuda-runtime-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} +nvidia-cudnn-cu11 = {version = "8.5.0.96", markers = "platform_system == \"Linux\""} typing-extensions = "*" [package.extras] opt-einsum = ["opt-einsum (>=3.3)"] -[package.source] -type = "legacy" -url = "https://download.pytorch.org/whl/cu116" -reference = "torch" - [[package]] name = "torchtyping" version = "0.1.4" @@ -798,27 +843,9 @@ python-versions = ">=3.5.3" doc = ["sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"] test = ["pytest", "typing-extensions", "mypy"] -[[package]] -name = "typeo" -version = "0.1.6" -description = "Utilities for running functions as scripts" -category = "main" -optional = false -python-versions 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b/libs/trainer/pyproject.toml index a804566..0cb5257 100644 --- a/libs/trainer/pyproject.toml +++ b/libs/trainer/pyproject.toml @@ -10,22 +10,13 @@ packages = [ [tool.poetry.dependencies] python = "^3.8,<3.11" -torch = {version = "^1.10", source = "torch"} -typeo = {git = "https://github.com/ML4GW/typeo.git", branch = "main"} +torch = {version = "^1.10"} ml4gw = {path = "../../ml4gw", develop = true} - "deepclean.utils" = {path = "../utils", develop = true} -"deepclean.architectures" = {path = "../architectures", develop = true} - -[[tool.poetry.source]] -name = "torch" -url = "https://download.pytorch.org/whl/cu116" -secondary = true -default = false [tool.poetry.dev-dependencies] pytest = "^6.2" [build-system] -requires = ["poetry>=1.2"] +requires = ["poetry-core"] build-backend = "poetry.masonry.api" diff --git a/libs/trainer/tests/test_wrapper.py b/libs/trainer/tests/test_wrapper.py deleted file mode 100644 index f83dc0f..0000000 --- a/libs/trainer/tests/test_wrapper.py +++ /dev/null @@ -1,71 +0,0 @@ -import os -import shutil -import sys - -import h5py -import numpy as np -import pytest - -from deepclean.trainer.wrapper import trainify - - -@pytest.fixture -def output_directory(): - os.makedirs("tmp") - yield "tmp" - shutil.rmtree("tmp") - - -def make_random_data( - length: int, output_directory: str, verbose: bool = False, **kwargs -): - return np.random.randn(10, length), np.random.randn(length) - - -def test_wrapper(output_directory): - fn = trainify(make_random_data) - - # make sure we can run the function as-is with regular arguments - X, y = fn(100, output_directory) - assert X.shape == (10, 100) - assert y.shape == (100,) - - fn( - 4096, - output_directory, - kernel_length=4, - kernel_stride=128 / 4096, - sample_rate=256, - max_epochs=1, - chunk_length=0, - alpha=0, - arch="autoencoder", - ) - result_path = os.path.join(output_directory, "train_results.h5") - assert os.path.exists(result_path) - with h5py.File(result_path, "r") as f: - assert len(f["train_loss"][:]) == 1 - - sys.argv = [ - None, - "--length", - "4096", - "--output-directory", - output_directory, - "--kernel-length", - "4", - "--kernel-stride", - str(128 / 4096), - "--sample-rate", - "256", - "--max-epochs", - "2", - "--chunk-length", - "0", - "--alpha", - "0", - "autoencoder", - ] - fn() - with h5py.File(result_path, "r") as f: - assert len(f["train_loss"][:]) == 2 diff --git a/projects/sandbox/pyproject.toml b/projects/sandbox/pyproject.toml index f980c7c..9c107b1 100644 --- a/projects/sandbox/pyproject.toml +++ b/projects/sandbox/pyproject.toml @@ -33,9 +33,7 @@ channels = [ ] sample_rate = 4096 verbose = false - -train_t0 = 1251335314 -train_duration = 4096 +train_duration = 1024 infer_kernel_length = 1 infer_batch_size = 64 @@ -56,11 +54,13 @@ autoencoder = {} # paths output_directory = "${PROJECT_DIR}" data_directory = "${DATA_DIR}" +segment_file = "${PROJECT_DIR}/segments.txt" # data parameters channels = "${base.channels}" -t0 = "${base.train_t0}" -duration = "${base.train_duration}" +train_duration = "${base.train_duration}" +retrain_cadence = 1024 +min_test_required = 256 valid_frac = 0.25 # data loading parameters @@ -77,6 +77,7 @@ filter_order = 8 batch_size = 256 max_epochs = 500 lr = 3.2e-2 +fine_tune_decay = 0.1 weight_decay = 1e-5 patience = 8 factor = 0.2 @@ -93,7 +94,8 @@ profile = false verbose = "${base.verbose}" # architecture subcommands -commands.autoencoder = "${base.autoencoder}" +[tool.typeo.scripts.train.architecture] +autoencoder = "${base.autoencoder}" [tool.typeo.scripts.export-model] repository_directory = "${MODEL_REPOSITORY}" diff --git a/projects/sandbox/train/poetry.lock b/projects/sandbox/train/poetry.lock index e9c47e7..fa119ba 100644 --- a/projects/sandbox/train/poetry.lock +++ b/projects/sandbox/train/poetry.lock @@ -1,23 +1,23 @@ [[package]] name = "astropy" -version = "5.1.1" +version = "5.2.1" description = "Astronomy and astrophysics core library" category = "main" optional = false python-versions = ">=3.8" [package.dependencies] -numpy = ">=1.18" +numpy = ">=1.20" packaging = ">=19.0" pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)"] -docs = ["sphinx (<4)", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (<3.1)"] -recommended = ["scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] +docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "towncrier (<22.12.0)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] +recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] -test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "pooch"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] [[package]] name = "atomicwrites" @@ -29,21 +29,23 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.6" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope-interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope-interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope-interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] [[package]] name = "bilby" -version = "1.4.0" +version = "1.4.1" description = "A user-friendly Bayesian inference library" category = "main" optional = false @@ -81,7 +83,7 @@ numpy = "*" [[package]] name = "certifi" -version = "2022.9.24" +version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -100,14 +102,11 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "2.1.1" +version = "3.0.1" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" optional = false -python-versions = ">=3.6.0" - -[package.extras] -unicode_backport = ["unicodedata2"] +python-versions = "*" [[package]] name = "ciecplib" @@ -138,21 +137,21 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7 [[package]] name = "contourpy" -version = "1.0.6" +version = "1.0.7" description = "Python library for calculating contours of 2D quadrilateral grids" category = "main" optional = false -python-versions = ">=3.7" +python-versions = ">=3.8" [package.dependencies] numpy = ">=1.16" [package.extras] -bokeh = ["bokeh", "selenium"] -docs = ["docutils (<0.18)", "sphinx (<=5.2.0)", "sphinx-rtd-theme"] -test = ["pytest", "matplotlib", "pillow", "flake8", "isort"] -test-minimal = ["pytest"] -test-no-codebase = ["pytest", "matplotlib", "pillow"] +bokeh = ["bokeh", "chromedriver", "selenium"] +docs = ["furo", "sphinx-copybutton"] +mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] +test = ["matplotlib", "pillow", "pytest"] +test-no-images = ["pytest"] [[package]] name = "corner" @@ -173,7 +172,7 @@ test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] name = "cryptography" -version = "38.0.4" +version = "39.0.1" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "main" optional = false @@ -183,12 +182,14 @@ python-versions = ">=3.6" cffi = ">=1.12" [package.extras] -docs = ["sphinx (>=1.6.5,!=1.8.0,!=3.1.0,!=3.1.1)", "sphinx-rtd-theme"] +docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "flake8", "flake8-import-order", "pep8-naming"] +pep8test = ["black", "ruff", "mypy", "types-pytz", "types-requests", "check-manifest"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test-randomorder = ["pytest-randomly"] +tox = ["tox"] [[package]] name = "cycler" @@ -245,11 +246,9 @@ python-versions = "^3.8,<3.11" develop = true [package.dependencies] -"deepclean.architectures" = {path = "../architectures", develop = true} "deepclean.utils" = {path = "../utils", develop = true} ml4gw = {path = "../../ml4gw", develop = true} torch = "^1.10" -typeo = {git = "https://github.com/ML4GW/typeo.git", branch = "main"} [package.source] type = "directory" @@ -295,13 +294,13 @@ igwn-auth-utils = ">=0.3.1" ligo-segments = ">=1.0.0" [package.extras] -test = ["requests-mock (>=1.5.0)", "pytest-cov (>=2.5.1)", "pytest (>=2.9.2)"] -lint = ["flake8-bandit", "flake8 (>=3.7.0)"] -docs = ["sphinx-immaterial", "sphinx-automodapi", "sphinx"] +docs = ["sphinx", "sphinx-automodapi", "sphinx-immaterial"] +lint = ["flake8 (>=3.7.0)", "flake8-bandit"] +test = ["pytest (>=2.9.2)", "pytest-cov (>=2.5.1)", "requests-mock (>=1.5.0)"] [[package]] name = "dynesty" -version = "2.0.2" +version = "2.1.0" description = "A dynamic nested sampling package for computing Bayesian posteriors and evidences." category = "main" optional = false @@ -309,7 +308,7 @@ python-versions = "*" [[package]] name = "emcee" -version = "3.1.3" +version = "3.1.4" description = "The Python ensemble sampling toolkit for MCMC" category = "main" optional = false @@ -384,8 +383,8 @@ python-versions = ">=3.5" requests = ">=1.0.0" [package.extras] -test = ["requests-mock (>=1.5.0)", "pytest-socket", "pytest-cov", "pytest (>=2.7.0)"] -docs = ["sphinx-rtd-theme", "sphinx-automodapi", "sphinx"] +docs = ["sphinx", "sphinx-automodapi", "sphinx-rtd-theme"] +test = ["pytest (>=2.7.0)", "pytest-cov", "pytest-socket", "requests-mock (>=1.5.0)"] [[package]] name = "gwpy" @@ -417,7 +416,7 @@ test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "p [[package]] name = "h5py" -version = "3.7.0" +version = "3.8.0" description = "Read and write HDF5 files from Python" category = "main" optional = false @@ -436,7 +435,7 @@ python-versions = ">=3.5" [[package]] name = "igwn-auth-utils" -version = "0.3.1" +version = "0.4.0" description = "Authorisation utilities for IGWN" category = "main" optional = false @@ -449,17 +448,32 @@ safe-netrc = ">=1.0.0" scitokens = ">=1.7.0" [package.extras] -test = ["safe-netrc (>=1.0.0)", "requests-mock", "requests (>=2.14)", "pytest-cov", "pytest (>=3.9.1)"] -lint = ["flake8-bandit", "flake8 (>=3.7.0)"] -docs = ["sphinx-immaterial-igwn", "sphinx (>=4.0.0)"] +docs = ["sphinx (>=4.0.0)", "sphinx-immaterial-igwn"] +lint = ["flake8 (>=3.7.0)", "flake8-bandit"] +test = ["pytest (>=3.9.1)", "pytest-cov", "requests (>=2.14)", "requests-mock", "safe-netrc (>=1.0.0)"] + +[[package]] +name = "importlib-resources" +version = "5.12.0" +description = "Read resources from Python packages" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} + +[package.extras] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] [[package]] name = "iniconfig" -version = "1.1.1" -description = "iniconfig: brain-dead simple config-ini parsing" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" category = "dev" optional = false -python-versions = "*" +python-versions = ">=3.7" [[package]] name = "kiwisolver" @@ -490,7 +504,7 @@ python-versions = ">=3.4" [[package]] name = "lxml" -version = "4.9.1" +version = "4.9.2" description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." category = "main" optional = false @@ -504,7 +518,7 @@ source = ["Cython (>=0.29.7)"] [[package]] name = "matplotlib" -version = "3.6.2" +version = "3.7.0" description = "Python plotting package" category = "main" optional = false @@ -514,18 +528,19 @@ python-versions = ">=3.8" contourpy = ">=1.0.1" cycler = ">=0.10" fonttools = ">=4.22.0" +importlib-resources = {version = ">=3.2.0", markers = "python_version < \"3.10\""} kiwisolver = ">=1.0.1" -numpy = ">=1.19" +numpy = ">=1.20" packaging = ">=20.0" pillow = ">=6.2.0" -pyparsing = ">=2.2.1" +pyparsing = ">=2.3.1" python-dateutil = ">=2.7" setuptools_scm = ">=7" [[package]] name = "ml4gw" -version = "0.0.1" -description = "Tools for building training torch models on gravitational wave data" +version = "0.1.1" +description = "Tools for training torch models on gravitational wave data" category = "main" optional = false python-versions = ">=3.8,<3.11" @@ -552,6 +567,7 @@ develop = true [package.dependencies] ciecplib = "^0.7.1" +gwpy = "^2.1" numpy = ">=1.22.0,<1.22.4" requests-gssapi = "^1.2.3" scipy = "<1.8" @@ -569,19 +585,64 @@ optional = false python-versions = ">=3.8" [[package]] -name = "packaging" -version = "21.3" -description = "Core utilities for Python packages" +name = "nvidia-cublas-cu11" +version = "11.10.3.66" +description = "CUBLAS native runtime libraries" category = "main" optional = false -python-versions = ">=3.6" +python-versions = ">=3" [package.dependencies] -pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-nvrtc-cu11" +version = "11.7.99" +description = "NVRTC native runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cuda-runtime-cu11" +version = "11.7.99" +description = "CUDA Runtime native Libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "nvidia-cudnn-cu11" +version = "8.5.0.96" +description = "cuDNN runtime libraries" +category = "main" +optional = false +python-versions = ">=3" + +[package.dependencies] +setuptools = "*" +wheel = "*" + +[[package]] +name = "packaging" +version = "23.0" +description = "Core utilities for Python packages" +category = "main" +optional = false +python-versions = ">=3.7" [[package]] name = "pandas" -version = "1.5.2" +version = "1.5.3" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false @@ -600,14 +661,14 @@ test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] [[package]] name = "pillow" -version = "9.3.0" +version = "9.4.0" description = "Python Imaging Library (Fork)" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] [[package]] @@ -712,7 +773,7 @@ six = ">=1.5" [[package]] name = "pytz" -version = "2022.6" +version = "2022.7.1" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -728,7 +789,7 @@ python-versions = ">=3.6" [[package]] name = "requests" -version = "2.28.1" +version = "2.28.2" description = "Python HTTP for Humans." category = "main" optional = false @@ -736,7 +797,7 @@ python-versions = ">=3.7, <4" [package.dependencies] certifi = ">=2017.4.17" -charset-normalizer = ">=2,<3" +charset-normalizer = ">=2,<4" idna = ">=2.5,<4" urllib3 = ">=1.21.1,<1.27" @@ -813,20 +874,20 @@ docs = ["sphinx"] [[package]] name = "setuptools" -version = "65.6.3" +version = "67.4.0" description = "Easily download, build, install, upgrade, and uninstall Python packages" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)", "pytest-flake8"] testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] [[package]] name = "setuptools-scm" -version = "7.0.5" +version = "7.1.0" description = "the blessed package to manage your versions by scm tags" category = "main" optional = false @@ -835,7 +896,7 @@ python-versions = ">=3.7" [package.dependencies] packaging = ">=20.0" setuptools = "*" -tomli = ">=1.0.0" +tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} typing-extensions = "*" [package.extras] @@ -868,23 +929,22 @@ python-versions = ">=3.7" [[package]] name = "torch" -version = "1.13.0+cu116" +version = "1.13.1" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" category = "main" optional = false python-versions = ">=3.7.0" [package.dependencies] +nvidia-cublas-cu11 = {version = "11.10.3.66", markers = "platform_system == \"Linux\""} +nvidia-cuda-nvrtc-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} +nvidia-cuda-runtime-cu11 = {version = "11.7.99", markers = "platform_system == \"Linux\""} +nvidia-cudnn-cu11 = {version = "8.5.0.96", markers = "platform_system == \"Linux\""} typing-extensions = "*" [package.extras] opt-einsum = ["opt-einsum (>=3.3)"] -[package.source] -type = "legacy" -url = "https://download.pytorch.org/whl/cu116" -reference = "torch" - [[package]] name = "torchtyping" version = "0.1.4" @@ -933,20 +993,18 @@ description = "Utilities for running functions as scripts" category = "main" optional = false python-versions = "^3.8" -develop = false +develop = true [package.dependencies] toml = "^0.10.2" [package.source] -type = "git" -url = "https://github.com/ML4GW/typeo.git" -reference = "main" -resolved_reference = "bd2429d484a3c4c3d49bfab62811b57f905573f1" +type = "directory" +url = "../../../typeo" [[package]] name = "typing-extensions" -version = "4.4.0" +version = "4.5.0" description = "Backported and Experimental Type Hints for 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"https://download.pytorch.org/whl/cu116" -secondary = true -default = false +"deepclean.trainer" = {path = "../../../libs/trainer", develop = true} [tool.poetry.dev-dependencies] pytest = "^6.2" [build-system] -requires = ["poetry>=1.2"] +requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" diff --git a/projects/sandbox/train/train.py b/projects/sandbox/train/train.py index a7402d9..8b98ff0 100644 --- a/projects/sandbox/train/train.py +++ b/projects/sandbox/train/train.py @@ -1,23 +1,98 @@ from pathlib import Path -from typing import Optional +from typing import Callable, List, Optional import numpy as np +from deepclean.architectures import architectures from deepclean.gwftools.channels import ChannelList, get_channels -from deepclean.logging import configure_logging -from deepclean.trainer.wrapper import trainify +from deepclean.logging import logger +from deepclean.trainer import train from mldatafind.io import read_timeseries +from typeo import scriptify +from typeo.utils import make_dummy -@trainify +def _intify(x): + y = int(x) + y = y if y == x else x + return str(y) + + +def _get_str(*times): + return "-".join(map(_intify, times)) + + +def read_segments(segment_file: Path): + segments = [i for i in segment_file.read_text().splitlines() if i] + segments = [tuple(map(float, i.split(","))) for i in segments] + return segments + + +def train_on_segment( + output_directory: Path, + log_file: Path, + data_directory: Path, + start: float, + stop: float, + channels: List[str], + architecture: Callable, + sample_rate: float, + valid_frac: float, + verbose: bool = False, + **kwargs, +) -> None: + output_directory.mkdir(parents=True, exist_ok=True) + str_rep = _get_str(start, stop) + logger.set_logger(f"DeepClean train {str_rep}", log_file, verbose) + + duration = stop - start + X, _ = read_timeseries( + data_directory, channels, start, stop, array_like=True + ) + + if X.shape[-1] != (duration * sample_rate): + inferred_sample_rate = X.shape[-1] / duration + raise ValueError( + "Data found in directory {} contains data " + "with sample rate {}, expected sample rate {}".format( + data_directory, inferred_sample_rate, sample_rate + ) + ) + [y], X = np.split(X, [1], axis=0) + + # carve off the last `valid_frac * duration` + # seconds worth of data for validation + split = int((1 - valid_frac) * sample_rate * duration) + train_X, valid_X = np.split(X, [split], axis=1) + train_y, valid_y = np.split(y, [split]) + kwargs["valid_data"] = (valid_X, valid_y) + + return train( + X=train_X, + y=train_y, + output_directory=output_directory, + architecture=architecture, + sample_rate=sample_rate, + **kwargs, + ) + + +@scriptify( + kwargs=make_dummy(train, exclude=["X", "y", "architecture"]), + architecture=architectures, +) def main( output_directory: Path, data_directory: Path, + segment_file: Path, channels: ChannelList, - t0: float, - duration: float, + architecture: Callable, + train_duration: float, + retrain_cadence: float, sample_rate: float, - valid_frac: Optional[float] = None, + valid_frac: float, + fine_tune_decay: Optional[float] = None, + min_test_required: float = 1024, verbose: bool = False, **kwargs, ): @@ -65,35 +140,52 @@ def main( Array of strain validation data timeseries, if validation data is requested. """ - - output_directory.mkdir(parents=True, exist_ok=True) - configure_logging(output_directory / "train.log", verbose) + log_directory = output_directory / "log" + log_directory.mkdir(parents=True, exist_ok=True) + root_logger = logger.set_logger( + "DeepClean", log_directory / "train.log", verbose=verbose + ) channels = get_channels(channels) - tf = t0 + duration + segments = read_segments(segment_file) - X, _ = read_timeseries(data_directory, channels, t0, tf, array_like=True) - if X.shape[-1] != (duration * sample_rate): - inferred_sample_rate = X.shape[-1] / duration - raise ValueError( - "Data found in directory {} contains data " - "with sample rate {}, expected sample rate {}".format( - data_directory, inferred_sample_rate, sample_rate + run_requires = train_duration - min_test_required + results_dir = None + lr = kwargs["lr"] * 1 + for start, stop in segments: + duration = stop - start + num_trains = int((duration - run_requires) // retrain_cadence) + 1 + root_logger.info( + "Running {} trainings on segment {}".format( + num_trains, _get_str(start, stop) ) ) - [y], X = np.split(X, [1], axis=0) - # if we didn't specify to create any validation data, - # return these arrays as-is - if valid_frac is None: - return X, y + for i in range(num_trains): + if fine_tune_decay is not None and results_dir is not None: + kwargs["init_weights"] = results_dir + kwargs["lr"] = lr * fine_tune_decay - # otherwise carve off the last `valid_frac * duration` - # seconds worth of data for validation - split = int((1 - valid_frac) * sample_rate * duration) - train_X, valid_X = np.split(X, [split], axis=1) - train_y, valid_y = np.split(y, [split]) - return train_X, train_y, valid_X, valid_y + train_start = start + i * retrain_cadence + train_stop = train_start + train_duration + str_rep = _get_str(train_start, train_stop) + + results_dir = output_directory / f"train_{str_rep}" + log_file = log_directory / f"train_{str_rep}.log" + root_logger.info(f"Beginning training on segment {str_rep}") + train_on_segment( + results_dir, + log_file, + data_directory, + architecture=architecture, + start=train_start, + stop=train_stop, + channels=channels, + sample_rate=sample_rate, + valid_frac=valid_frac, + verbose=verbose, + **kwargs, + ) if __name__ == "__main__": diff --git a/typeo b/typeo new file mode 160000 index 0000000..08f5583 --- /dev/null +++ b/typeo @@ -0,0 +1 @@ +Subproject commit 08f55834163d9fb3ee9248f127127c4f5bab4b9c From ca66e5efbb9842b15fe9a8fe586582051e2e642f Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 15 Mar 2023 16:50:06 -0700 Subject: [PATCH 05/31] start of major overhaul of microservice and libs accordingly --- libs/infer/pyproject.toml | 9 +- libs/trainer/deepclean/trainer/criterion.py | 2 +- libs/trainer/deepclean/trainer/trainer.py | 3 +- libs/utils/deepclean/gwftools/__init__.py | 0 libs/utils/deepclean/gwftools/channels.py | 18 -- libs/utils/deepclean/gwftools/frames.py | 73 -------- libs/utils/deepclean/gwftools/io.py | 195 -------------------- libs/utils/deepclean/utils.py | 142 -------------- 8 files changed, 6 insertions(+), 436 deletions(-) delete mode 100644 libs/utils/deepclean/gwftools/__init__.py delete mode 100644 libs/utils/deepclean/gwftools/channels.py delete mode 100644 libs/utils/deepclean/gwftools/frames.py delete mode 100644 libs/utils/deepclean/gwftools/io.py delete mode 100644 libs/utils/deepclean/utils.py diff --git a/libs/infer/pyproject.toml b/libs/infer/pyproject.toml index f22d664..15973c4 100644 --- a/libs/infer/pyproject.toml +++ b/libs/infer/pyproject.toml @@ -11,14 +11,11 @@ packages = [ [tool.poetry.dependencies] python = "^3.8,<3.11" tritonclient = {extras = ["all"], version = "^2.18.0"} - -"deepclean.base" = {path = "../base", develop = true} -"deepclean.gwftools" = {path = "../gwftools", develop = true} -"deepclean.signal" = {path = "../signal", develop = true} +"deepclean.utils" = {path = "../utils", develop = true} [tool.poetry.dev-dependencies] pytest = "^6.2" [build-system] -requires = ["poetry>=1.2"] -build-backend = "poetry.masonry.api" +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/trainer/deepclean/trainer/criterion.py b/libs/trainer/deepclean/trainer/criterion.py index 7b3dd45..08927cf 100644 --- a/libs/trainer/deepclean/trainer/criterion.py +++ b/libs/trainer/deepclean/trainer/criterion.py @@ -3,7 +3,7 @@ import numpy as np import torch -from deepclean.utils import Frequency, normalize_frequencies +from deepclean.utils.filtering import Frequency, normalize_frequencies from ml4gw.transforms import SpectralDensity diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 2c4129b..e70de58 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -9,7 +9,7 @@ from deepclean.trainer import CompositePSDLoss from deepclean.trainer.analysis import analyze_model from deepclean.trainer.utils import Checkpointer, Trainer -from deepclean.utils import BandpassFilter, Frequency +from deepclean.utils.filtering import BandpassFilter, Frequency from ml4gw.dataloading import InMemoryDataset from ml4gw.transforms import ChannelWiseScaler, SpectralDensity @@ -287,3 +287,4 @@ def train( output_scaler.forward = partial(output_scaler.forward, reverse=True) model = torch.nn.Sequential(input_scaler, model, output_scaler) torch.save(model.state_dict(), weights_path) + return weights_path diff --git a/libs/utils/deepclean/gwftools/__init__.py b/libs/utils/deepclean/gwftools/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/libs/utils/deepclean/gwftools/channels.py b/libs/utils/deepclean/gwftools/channels.py deleted file mode 100644 index d26035f..0000000 --- a/libs/utils/deepclean/gwftools/channels.py +++ /dev/null @@ -1,18 +0,0 @@ -from typing import Iterable, List, Union - -ChannelList = Union[str, Iterable[str]] - - -def get_channels(channels: ChannelList) -> List[str]: - if isinstance(channels, str) or len(channels) == 1: - if len(channels) == 1: - channels = channels[0] - - try: - with open(channels, "r") as f: - channels = [i for i in f.read().splitlines() if i] - except FileNotFoundError: - raise FileNotFoundError(f"No channel file {channels} exists") - - channels = list(channels) - return channels[:1] + sorted(channels[1:]) diff --git a/libs/utils/deepclean/gwftools/frames.py b/libs/utils/deepclean/gwftools/frames.py deleted file mode 100644 index aa900d5..0000000 --- a/libs/utils/deepclean/gwftools/frames.py +++ /dev/null @@ -1,73 +0,0 @@ -import re -from dataclasses import dataclass -from pathlib import Path -from typing import Literal, Tuple, Union - -PATH_LIKE = Union[str, Path] - - -prefix_re = "[a-zA-Z0-9_:-]+" -t0_re = "[0-9]{10}" -length_re = "[1-9][0-9]{0,3}" -fname_re = re.compile( - f"(?P{prefix_re})-" - f"(?P{t0_re})-" - f"(?P{length_re})" - ".(?Pgwf|hdf5|h5)$" -) - - -def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: - """Use the name of a frame file to infer its initial timestamp and length - - Expects frame names to follow a standard nomenclature - where the name of the frame file ends {prefix}_{timestamp}-{length}.gwf - - Args: - fname: The name of the frame file - Returns: - The prefix of the frame file name - The initial GPS timestamp of the frame file - The length of the frame file in seconds - """ - - if isinstance(fname, Path): - fname = fname.name - - match = fname_re.search(fname) - if match is None: - raise ValueError(f"Could not parse frame filename {fname}") - - prefix, t0, length, _ = match.groups() - return prefix, int(t0), int(length) - - -@dataclass -class FrameFileFormat: - prefix: str - suffix: Literal["gwf", "hdf5", "h5"] = "gwf" - - @classmethod - def from_frame_file(cls, frame_file: PATH_LIKE): - prefix, _, __ = parse_frame_name(frame_file) - suffix = Path(frame_file).suffix[1:] - return cls(prefix, suffix) - - def get_name(self, timestamp: int, length: int): - if int(timestamp) != timestamp: - raise ValueError(f"Timestamp {timestamp} must be an int") - elif len(str(timestamp)) != 10: - raise ValueError( - "Couldn't create valid GPS timestamp from timestamp {}".format( - timestamp - ) - ) - - if length <= 0: - raise ValueError(f"Length {length} must be greater than 0") - elif int(length) != length: - raise ValueError(f"Length {length} must be an int") - elif not 1 <= len(str(length)) < 5: - raise ValueError(f"Frame length {length} invalid") - - return f"{self.prefix}-{timestamp}-{length}.{self.suffix}" diff --git a/libs/utils/deepclean/gwftools/io.py b/libs/utils/deepclean/gwftools/io.py deleted file mode 100644 index bd062b7..0000000 --- a/libs/utils/deepclean/gwftools/io.py +++ /dev/null @@ -1,195 +0,0 @@ -import logging -import re -from pathlib import Path -from typing import Dict, Optional, Sequence - -import h5py -import numpy as np -from gwpy.timeseries import TimeSeries, TimeSeriesDict - -from deepclean.gwftools.channels import ChannelList -from deepclean.gwftools.frames import fname_re - -FAKE_REGEX = re.compile(r"FAKE_SINE_FREQ_(?P\d+POINT\d+)HZ") - - -def make_fake_sines( - channels: Sequence[str], t0: float, duration: float, sample_rate: float -) -> Dict[str, np.ndarray]: - time = np.arange(t0, t0 + duration, 1 / sample_rate) - - data = {} - for channel in channels: - freq = FAKE_REGEX.search(channel) - if freq is None: - raise ValueError( - f"Fake sine channel '{channel}' not properly formatted" - ) - - freq = freq.group("freq") - freq = float(freq.replace("POINT", ".")) - data[channel] = np.sin(2 * np.pi * freq * time) - return data - - -def fetch( - channels: Sequence[str], - t0: float, - duration: float, - sample_rate: float, - nproc: int = 4, -) -> Dict[str, np.ndarray]: - logging.info(f"Fetching {duration}s of data starting at timestamp {t0}") - - fake_channels = list(filter(FAKE_REGEX.fullmatch, channels)) - real_channels = [i for i in channels if i not in fake_channels] - - # get data and resample - # TODO: do any logic about data quality? - data = TimeSeriesDict.get( - real_channels, t0, t0 + duration, nproc=nproc, allow_tape=True - ) - data = data.resample(sample_rate) - data = {channel: x.value for channel, x in data.items()} - - # make any sinusoids - fake_data = make_fake_sines(fake_channels, t0, duration, sample_rate) - data.update(fake_data) - - return data - - -def read( - fname: Path, - channels: Sequence[str], - t0: float, - duration: float, - sample_rate: float, -) -> Dict[str, np.ndarray]: - logging.info(f"Reading data from '{fname}'") - - data = {} - with h5py.File(str(fname), "r") as f: - for channel in channels: - try: - dataset = f[channel] - except KeyError: - if FAKE_REGEX.fullmatch(channel) is not None: - # if the channel is missing because it's a - # fake sinusoid, generate it now - fake_data = make_fake_sines( - [channel], t0, duration, sample_rate - ) - data.update(fake_data) - continue - else: - # otherwise vocalize our unhappiness - raise ValueError( - "Data file {} doesn't contain channel {}".format( - fname, channel - ) - ) - - # enforce that the initial timestamps match, - # otherwise we're just talking about different data - if dataset.attrs["t0"] != t0: - raise ValueError( - "Channel {} has initial GPS timestamp {}, " - "expected {}".format(channel, dataset.attrs["t0"], t0) - ) - - # now grab the actual data array and preprocess it - x = dataset[:] - - # resample if necessary TODO: raise an error if this - # resampling is an upsample? - if sample_rate != dataset.attrs["sample_rate"]: - x = TimeSeries(x, dt=1 / dataset.attrs["sample_rate"]) - x = x.resample(sample_rate).value - - # allow for shorter durations so that we only need - # to download one stretch of long data then reuse - # it for shorter stretches if need be - if len(x) < (sample_rate * duration): - raise ValueError( - "Channel {} has only {}s worth of data, " - "expected at least {}".format( - channel, len(x) // sample_rate, duration - ) - ) - - # grab the first `duration` seconds of data - data[channel] = x[: int(sample_rate * duration)] - return data - - -def write( - data: Dict[str, np.ndarray], fname: Path, t0: float, sample_rate: float -) -> None: - """Write to HDF5 format""" - - logging.info(f"Writing data to {fname}") - with h5py.File(str(fname), "w") as f: - for channel, x in data.items(): - # don't write fake sinusoids - if FAKE_REGEX.fullmatch(channel) is not None: - continue - - dset = f.create_dataset(channel, data=x, compression="gzip") - dset.attrs["sample_rate"] = sample_rate - dset.attrs["t0"] = t0 - - -def find( - channels: ChannelList, - t0: float, - duration: float, - sample_rate: float, - data_path: Optional[Path] = None, - force_download: bool = False, -): - if data_path is None: - # we didn't specify a path at all, - # so don't bother reading or writing the data - # and just download it into memory - data = fetch(channels, t0, duration, sample_rate) - else: - # we specified a data path of some kind, figure - # out if it refers to a file or a directory - match = fname_re.search(data_path.name) - - if data_path.is_file() or match is not None: - # either the path refers to an existing file, - # or to a path whose name is formatted like a file - if not data_path.is_file() and match.group("suffix") != "gwf": - # the file doesn't exist yet, but it's a valid - # filename because it doesn't end in .gwf. Make - # the path to the directory containing this file - # if it doesn't exist yet - data_path.parent.mkdir(parents=True, exist_ok=True) - elif not data_path.is_file(): - # the file dosn't exist yet, but it's formatted like - # a GWF file and we're dealing with HDF5s, so let's - # raise an error to keep from muddying the waters - raise ValueError(f"Can't create data file {data_path}") - elif match is None or data_path.is_dir(): - # either data_path is an existing directory - # or data_path's terminal path node isn't formatted - # like a frame filename, so we'll assume that it - # refers to a directory and make a default filename - t0 = int(t0) if int(t0) == t0 else t0 - duration = int(duration) if int(duration) == duration else duration - fname = f"deepclean-{t0}-{duration}.h5" - data_path.mkdir(parents=True, exist_ok=True) - data_path = data_path / fname - - if data_path.is_file() and not force_download: - # if the data exists and we're not forcing a fresh - # download, attempt to read the existing data - data = read(data_path, channels, t0, duration, sample_rate) - else: - # otherwise, download the data and - # write it to the specified path - data = fetch(channels, t0, duration, sample_rate) - write(data, data_path, t0, sample_rate) - return data diff --git a/libs/utils/deepclean/utils.py b/libs/utils/deepclean/utils.py deleted file mode 100644 index 67c3e5e..0000000 --- a/libs/utils/deepclean/utils.py +++ /dev/null @@ -1,142 +0,0 @@ -from typing import List, Sequence, Tuple, Union - -from scipy import signal - -ChannelList = Union[str, Sequence[str]] -Frequency = Union[float, Sequence[float]] - - -def get_channels(channels: ChannelList) -> List[str]: - if isinstance(channels, str) or len(channels) == 1: - if len(channels) == 1: - channels = channels[0] - - try: - with open(channels, "r") as f: - channels = [i for i in f.read().splitlines() if i] - except FileNotFoundError: - raise FileNotFoundError(f"No channel file {channels} exists") - - channels = list(channels) - return channels[:1] + sorted(channels[1:]) - - -def normalize_frequencies( - freq_low: Frequency, freq_high: Frequency -) -> Tuple[List[float], List[float]]: - """Standardize frequency bands to sorted lists - - For a frequency band `(freq_low, freq_high)` or - a list of such bands `(freq_low[i], freq_high[i])`, - ensure that the number of frequencies in each argument - is the same and that the bands denoted by them are - non-overlapping. - - Args: - freq_low: - Either a single frequency representing the - low end of a band, or a list of frequencies - specifying the low ends of several bands. - freq_high: - Either a single frequency representing the - high end of a band, or a list of frequencies - specifying the high ends of several bands. - Returns: - List of low frequencies in sorted order - """ - try: - num_bands = len(freq_low) - except TypeError: - # freq_low has no `__length__` property, so - # assume it's a single number - try: - # check to make sure freq_high is also - # a single number - num_bands = len(freq_high) - except TypeError: - # it is, so wrap everything in a list to keep - # things general - freq_low = [freq_low] - freq_high = [freq_high] - num_bands = 1 - else: - # it's not, this is a problem - raise ValueError( - "If specifying multiple frequencies for " - "freq_high, must do the same for freq_low" - ) - else: - # freq_low _does_ have a `__length__` property, - # so make sure that freq_high has length at all - # and that this length is the same - try: - if num_bands != len(freq_high): - raise ValueError( - "Number of bands specified to freq_low {} " - "doesn't match number of bands specified " - "to freq_high {}".format(num_bands, len(freq_high)) - ) - except TypeError: - raise ValueError( - "If specifying multiple frequencies for " - "freq_low, must do the same for freq_high" - ) - - # sort the bands so we can make sure that - # they don't overlap at all - freq_low, indices = zip(*sorted(zip(freq_low, range(num_bands)))) - freq_low = list(freq_low) - freq_high = [freq_high[i] for i in indices] - for i in range(num_bands): - # first make sure that the low freq is - # lower than the high freq - if freq_low[i] >= freq_high[i]: - raise ValueError( - "Low frequency {} not less than high frequency {}".format( - freq_low[i], freq_high - ) - ) - - # iterate through all the higher bands and - # make sure that the current high freq is - # not higher than the higher low freqs - if i < (num_bands - 1): - for j in range(i + 1, num_bands): - if freq_high[i] > freq_low[j]: - raise ValueError( - "Top of frequency band {} greater than " - "bottom of higher frequency band {}".format( - freq_high[i], freq_low[j] - ) - ) - return freq_low, freq_high - - -class BandpassFilter: - def __init__( - self, - freq_low: Frequency, - freq_high: Frequency, - sample_rate: float, - order: int = 8, - ) -> None: - freq_low, freq_high = normalize_frequencies(freq_low, freq_high) - - # create a different filter for each band - self.sos = [] - for low, high in zip(freq_low, freq_high): - self.sos.append( - signal.butter( - order, - [low, high], - btype="bandpass", - output="sos", - fs=sample_rate, - ) - ) - - def __call__(self, x): - output = 0.0 - for sos in self.sos: - output += signal.sosfiltfilt(sos, x, axis=-1) - return output From c62ad5eb073335fd8c778cbb8c875005472dc7be Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 15 Mar 2023 16:56:03 -0700 Subject: [PATCH 06/31] adding all the new modules --- libs/infer/deepclean/infer/clean.py | 33 + libs/utils/deepclean/utils/channels.py | 18 + libs/utils/deepclean/utils/filtering.py | 126 ++ .../microservice/cleaner/cleaner/__init__.py | 0 .../cleaner/{ => cleaner}/cleaner.py | 0 projects/microservice/environment.yaml | 8 + projects/microservice/trainer/poetry.lock | 1790 +++++++++++++++++ projects/microservice/trainer/poetry.toml | 2 + projects/microservice/trainer/pyproject.toml | 22 + .../trainer/trainer/dataloader.py | 158 ++ .../microservice/trainer/trainer/trainer.py | 137 ++ .../microservice/utils/microservice/frames.py | 178 ++ projects/microservice/utils/poetry.lock | 8 + projects/microservice/utils/poetry.toml | 2 + projects/microservice/utils/pyproject.toml | 12 + 15 files changed, 2494 insertions(+) create mode 100644 libs/infer/deepclean/infer/clean.py create mode 100644 libs/utils/deepclean/utils/channels.py create mode 100644 libs/utils/deepclean/utils/filtering.py create mode 100644 projects/microservice/cleaner/cleaner/__init__.py rename projects/microservice/cleaner/{ => cleaner}/cleaner.py (100%) create mode 100644 projects/microservice/environment.yaml create mode 100644 projects/microservice/trainer/poetry.lock create mode 100644 projects/microservice/trainer/poetry.toml create mode 100644 projects/microservice/trainer/pyproject.toml create mode 100644 projects/microservice/trainer/trainer/dataloader.py create mode 100644 projects/microservice/trainer/trainer/trainer.py create mode 100644 projects/microservice/utils/microservice/frames.py create mode 100644 projects/microservice/utils/poetry.lock create mode 100644 projects/microservice/utils/poetry.toml create mode 100644 projects/microservice/utils/pyproject.toml diff --git a/libs/infer/deepclean/infer/clean.py b/libs/infer/deepclean/infer/clean.py new file mode 100644 index 0000000..001cf4e --- /dev/null +++ b/libs/infer/deepclean/infer/clean.py @@ -0,0 +1,33 @@ +from dataclasses import dataclass + +import numpy as np +from scipy.signal import windows + +from deepclean.utils.filtering import FREQUENCY, BandpassFilter + + +@dataclass +class Cleaner: + kernel_length: float + sample_rate: float + filter_pad: float + freq_low: FREQUENCY = None + freq_high: FREQUENCY = None + + def __post_init__(self): + self.bandpass = BandpassFilter( + self.freq_low, self.freq_high, self.sample_rate + ) + self.pad_size = int(self.filter_pad * self.sample_rate) + self.kernel_Size = int(self.kernel_length * self.sample_rate) + self.window = windows.hann(2 * self.pad_size) + + def __call__(self, noise: np.ndarray, strain: np.ndarray) -> np.ndarray: + noise[: self.pad_size] *= self.window[: self.pad_size] + noise[-self.pad_size :] *= self.window[-self.pad_size :] + noise = self.bandpass(noise) + + start = -self.pad_size - self.kernel_size + stop = -self.pad_size + noise = noise[start:stop] + return strain - noise diff --git a/libs/utils/deepclean/utils/channels.py b/libs/utils/deepclean/utils/channels.py new file mode 100644 index 0000000..d26035f --- /dev/null +++ b/libs/utils/deepclean/utils/channels.py @@ -0,0 +1,18 @@ +from typing import Iterable, List, Union + +ChannelList = Union[str, Iterable[str]] + + +def get_channels(channels: ChannelList) -> List[str]: + if isinstance(channels, str) or len(channels) == 1: + if len(channels) == 1: + channels = channels[0] + + try: + with open(channels, "r") as f: + channels = [i for i in f.read().splitlines() if i] + except FileNotFoundError: + raise FileNotFoundError(f"No channel file {channels} exists") + + channels = list(channels) + return channels[:1] + sorted(channels[1:]) diff --git a/libs/utils/deepclean/utils/filtering.py b/libs/utils/deepclean/utils/filtering.py new file mode 100644 index 0000000..7f6d9f1 --- /dev/null +++ b/libs/utils/deepclean/utils/filtering.py @@ -0,0 +1,126 @@ +from typing import List, Sequence, Tuple, Union + +from scipy import signal + +Frequency = Union[float, Sequence[float]] + + +def normalize_frequencies( + freq_low: Frequency, freq_high: Frequency +) -> Tuple[List[float], List[float]]: + """Standardize frequency bands to sorted lists + + For a frequency band `(freq_low, freq_high)` or + a list of such bands `(freq_low[i], freq_high[i])`, + ensure that the number of frequencies in each argument + is the same and that the bands denoted by them are + non-overlapping. + + Args: + freq_low: + Either a single frequency representing the + low end of a band, or a list of frequencies + specifying the low ends of several bands. + freq_high: + Either a single frequency representing the + high end of a band, or a list of frequencies + specifying the high ends of several bands. + Returns: + List of low frequencies in sorted order + """ + try: + num_bands = len(freq_low) + except TypeError: + # freq_low has no `__length__` property, so + # assume it's a single number + try: + # check to make sure freq_high is also + # a single number + num_bands = len(freq_high) + except TypeError: + # it is, so wrap everything in a list to keep + # things general + freq_low = [freq_low] + freq_high = [freq_high] + num_bands = 1 + else: + # it's not, this is a problem + raise ValueError( + "If specifying multiple frequencies for " + "freq_high, must do the same for freq_low" + ) + else: + # freq_low _does_ have a `__length__` property, + # so make sure that freq_high has length at all + # and that this length is the same + try: + if num_bands != len(freq_high): + raise ValueError( + "Number of bands specified to freq_low {} " + "doesn't match number of bands specified " + "to freq_high {}".format(num_bands, len(freq_high)) + ) + except TypeError: + raise ValueError( + "If specifying multiple frequencies for " + "freq_low, must do the same for freq_high" + ) + + # sort the bands so we can make sure that + # they don't overlap at all + freq_low, indices = zip(*sorted(zip(freq_low, range(num_bands)))) + freq_low = list(freq_low) + freq_high = [freq_high[i] for i in indices] + for i in range(num_bands): + # first make sure that the low freq is + # lower than the high freq + if freq_low[i] >= freq_high[i]: + raise ValueError( + "Low frequency {} not less than high frequency {}".format( + freq_low[i], freq_high + ) + ) + + # iterate through all the higher bands and + # make sure that the current high freq is + # not higher than the higher low freqs + if i < (num_bands - 1): + for j in range(i + 1, num_bands): + if freq_high[i] > freq_low[j]: + raise ValueError( + "Top of frequency band {} greater than " + "bottom of higher frequency band {}".format( + freq_high[i], freq_low[j] + ) + ) + return freq_low, freq_high + + +class BandpassFilter: + def __init__( + self, + freq_low: Frequency, + freq_high: Frequency, + sample_rate: float, + order: int = 8, + ) -> None: + freq_low, freq_high = normalize_frequencies(freq_low, freq_high) + + # create a different filter for each band + self.sos = [] + for low, high in zip(freq_low, freq_high): + self.sos.append( + signal.butter( + order, + [low, high], + btype="bandpass", + output="sos", + fs=sample_rate, + ) + ) + + def __call__(self, x): + output = 0.0 + for sos in self.sos: + output += signal.sosfiltfilt(sos, x, axis=-1) + return output diff --git a/projects/microservice/cleaner/cleaner/__init__.py b/projects/microservice/cleaner/cleaner/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/projects/microservice/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner.py similarity index 100% rename from projects/microservice/cleaner/cleaner.py rename to projects/microservice/cleaner/cleaner/cleaner.py diff --git a/projects/microservice/environment.yaml b/projects/microservice/environment.yaml new file mode 100644 index 0000000..794b230 --- /dev/null +++ b/projects/microservice/environment.yaml @@ -0,0 +1,8 @@ +name: deepclean-production-base +channels: + - conda-forge +dependencies: + - python=3.10 + - gwpy + - astropy + - ldas-tools-framecpp diff --git a/projects/microservice/trainer/poetry.lock b/projects/microservice/trainer/poetry.lock new file mode 100644 index 0000000..a40948e --- /dev/null +++ b/projects/microservice/trainer/poetry.lock @@ -0,0 +1,1790 @@ +[[package]] +name = "astropy" +version = "5.2.1" +description = "Astronomy and astrophysics core library" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = ">=1.20" +packaging = ">=19.0" +pyerfa = ">=2.0" +PyYAML = ">=3.13" + +[package.extras] +all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] +docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "towncrier (<22.12.0)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] +recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] + +[[package]] +name = "attrs" +version = "22.2.0" +description = "Classes Without Boilerplate" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.extras] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope-interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] + +[[package]] +name = "bilby" +version = "1.4.1" +description = "A user-friendly Bayesian inference library" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +attrs = "*" +"bilby.cython" = ">=0.3.0" +corner = "*" +dill = "*" +dynesty = ">=2" +emcee = "*" +h5py = "*" +matplotlib = ">=2.1" +numpy = "*" +pandas = "*" +scipy = ">=1.5" +tqdm = "*" + +[package.extras] +all = ["cpnest (>=0.9.4)", "dynesty", "emcee", "nestle", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "ptemcee", "pymc (>=4.0.0)", "pymultinest", "kombine", "ultranest (>=3.0.0)", "dnest4", "nessai (>=0.7.0)", "schwimmbad", "zeus-mcmc (>=2.3.0)", "astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw", "scikit-learn", "nflows", "celerite", "george", "plotly"] +gw = ["astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw"] +mcmc = ["scikit-learn", "nflows"] + +[[package]] +name = "bilby-cython" +version = "0.4.0" +description = "Optimized functionality for Bilby" +category = "main" +optional = false +python-versions = ">=3.8" + +[package.dependencies] +numpy = "*" + +[[package]] +name = "certifi" +version = "2022.12.7" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "cffi" +version = "1.15.1" +description = "Foreign Function Interface for Python calling C code." +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +pycparser = "*" + +[[package]] +name = "charset-normalizer" +version = "3.1.0" +description = "The Real First Universal Charset Detector. 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+"deepclean.logging" = {path = "../../../libs/logging", develop = true} +"deepclean.trainer" = {path = "../../../libs/trainer", develop = true} +"deepclean.utils" = {path = "../../../libs/utils", develop = true} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py new file mode 100644 index 0000000..d6dff3b --- /dev/null +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -0,0 +1,158 @@ +import sys +import time +import traceback +from multiprocessing import Event, Process, Queue +from pathlib import Path +from queue import Empty +from typing import Optional + +import numpy as np +from microservice.frames import FrameCrawler, load_frame + +from deepclean.logging import logger +from deepclean.utils.channels import ChannelList, get_channels + + +def collect_frames( + event: Event, + data_directory: Path, + channels: ChannelList, + duration: float, + cadence: float, + sample_rate: float, + timeout: Optional[float] = None, +) -> np.ndarray: + channels = get_channels(channels) + crawler = FrameCrawler(data_directory, timeout=timeout) + crawler = iter(crawler) + start = crawler.t0 + 0 + + size = int(duration * sample_rate) + edge_size = int(cadence * sample_rate) + X = np.zeros((len(channels), 0)) + y = np.zeros((0,)) + + while not event.is_set(): + fname = next(crawler) + witnesses = load_frame(fname, channels[1:], sample_rate) + strain = load_frame(fname, channels[0], sample_rate) + + X = np.concatenate([X, witnesses], axis=1) + y = np.concatenate([y, strain]) + + # TODO: insert some checks here about data quality, + # checking for coherence, etc., and possibly reset + # X, y, and start accordingly once the conditions have + # been met again + if False: + logger.info( + "Data quality conditions have been violated, " + "pausing data collection until conditions " + "improve." + ) + + if len(y) >= size: + logger.info( + "Accumulated {}s of data starting at " + "GPS time {}, passing to training".format(duration, start) + ) + yield X, y, start + + X = X[:, edge_size:] + y = y[edge_size:] + start += cadence + + +def target( + q: Queue, + event: Event, + data_directory: Path, + log_directory: Path, + channels: ChannelList, + duration: float, + cadence: float, + sample_rate: float, + timeout: Optional[float] = None, + verbose: bool = False, +): + logger.set_logger( + "DeepClean dataloading", + log_directory / "dataloader.log", + verbose=verbose, + ) + try: + it = collect_frames( + event, + data_directory, + channels, + duration, + cadence, + sample_rate, + timeout, + ) + for X, y, start in it: + q.put((X, y, start)) + except Exception: + exc_type, exc, tb = sys.exc_info() + tb = traceback.format_exception(exc_type, exc, tb) + tb = "".join(tb[:-1]) + q.put((exc_type, str(exc), tb)) + finally: + # if we arrived here from an exception, i.e. + # the event has not been set, then don't + # close the queue until the parent process + # has received the error message and set the + # event itself, otherwise it will never be + # able to receive the message from the queue + while not event.is_set(): + time.sleep(1e-3) + q.close() + + +def data_collector( + data_directory: Path, + log_directory: Path, + channels: ChannelList, + duration: float, + cadence: float, + sample_rate: float, + timeout: Optional[float] = None, + verbose: bool = False, +): + q = Queue() + event = Event() + args = ( + q, + event, + data_directory, + log_directory, + channels, + duration, + cadence, + sample_rate, + timeout, + verbose, + ) + + p = Process(target=target, args=args) + p.start() + try: + while True: + try: + X, y, start = q.get_nowait() + except Empty: + time.sleep(1) + continue + + if isinstance(start, str): + exc_type, msg, tb = X, y, start + logger.exception( + "Encountered exception in data collection process:\n" + tb + ) + raise exc_type(msg) + yield X, y, start + finally: + event.set() + q.close() + p.join() + p.close() diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py new file mode 100644 index 0000000..84f5fd0 --- /dev/null +++ b/projects/microservice/trainer/trainer/trainer.py @@ -0,0 +1,137 @@ +from pathlib import Path +from typing import Callable, Optional + +import numpy as np +from trainer.dataloader import data_collector + +from deepclean.architectures import architectures +from deepclean.logging import logger +from deepclean.trainer.trainer import train +from deepclean.utils.channels import ChannelList +from typeo import scriptify +from typeo.utils import make_dummy + + +def _intify(x): + y = int(x) + y = y if y == x else x + return str(y) + + +def _get_str(*times): + return "-".join(map(_intify, times)) + + +def train_on_segment( + X: np.ndarray, + y: np.ndarray, + output_directory: Path, + start: float, + architecture: Callable, + sample_rate: float, + valid_frac: float, + verbose: bool = False, + **kwargs, +) -> None: + duration = len(y) / sample_rate + str_rep = _get_str(start, start + duration) + + log_file = output_directory / "log" / f"train_{str_rep}.log" + + output_directory = output_directory / "training" / str_rep + logger.info(f"Saving training outputs to directory {output_directory}") + output_directory.mkdir(parents=True, exist_ok=True) + + # now set the logger to one specific to this training run + logger.set_logger(f"DeepClean train {str_rep}", log_file, verbose) + + # carve off the last `valid_frac * duration` + # seconds worth of data for validation + split = int((1 - valid_frac) * sample_rate * duration) + train_X, valid_X = np.split(X, [split], axis=1) + train_y, valid_y = np.split(y, [split]) + kwargs["valid_data"] = (valid_X, valid_y) + + return train( + X=train_X, + y=train_y, + output_directory=output_directory, + architecture=architecture, + sample_rate=sample_rate, + **kwargs, + ) + + +@scriptify( + kwargs=make_dummy(train, exclude=["X", "y", "architecture"]), + architecture=architectures, +) +def main( + output_directory: Path, + data_directory: Path, + channels: ChannelList, + architecture: Callable, + train_duration: float, + retrain_cadence: float, + sample_rate: float, + valid_frac: float, + fine_tune_decay: Optional[float] = None, + verbose: bool = False, + **kwargs, +): + log_directory = output_directory / "log" + log_directory.mkdir(parents=True, exist_ok=True) + root_logger = logger.set_logger( + "DeepClean trainer", log_directory / "root.log", verbose=verbose + ) + + data_it = data_collector( + data_directory, + log_directory, + channels, + train_duration, + retrain_cadence, + sample_rate, + timeout=60, + verbose=verbose, + ) + + last_start = None + for X, y, start in data_it: + if last_start is not None and start > (last_start + train_duration): + # TODO: insert any logic about how we do + # training differently on a new lock segment + pass + + root_logger.info( + "Launching training on segment {}-{}".format( + start, start + train_duration + ) + ) + + weights_path = train_on_segment( + X, + y, + output_directory=output_directory, + start=start, + architecture=architecture, + sample_rate=sample_rate, + valid_frac=valid_frac, + verbose=verbose, + **kwargs, + ) + + logger.set_logger("DeepClean train") + logger.info( + "Training on segment {}-{} complete, saved " + "optimized weights to {}".format( + start, start + train_duration, weights_path + ) + ) + + # for trainings after the first, use the previous + # optimized weights and reduce the learning rate + kwargs["init_weights"] = weights_path + if last_start is None: + kwargs["lr"] = kwargs["lr"] * fine_tune_decay + last_start = start diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py new file mode 100644 index 0000000..9f18bd9 --- /dev/null +++ b/projects/microservice/utils/microservice/frames.py @@ -0,0 +1,178 @@ +import logging +import re +import time +from dataclasses import dataclass +from pathlib import Path +from typing import Iterable, Literal, Optional, Tuple, Union + +import numpy as np +from gwpy.timeseries import TimeSeries, TimeSeriesDict + +PATH_LIKE = Union[str, Path] + +patterns = { + "prefix": "[a-zA-Z0-9_:-]+", + "start": "[0-9]{10}", + "duration": "[1-9][0-9]*", + "suffix": "(gwf)|(hdf5)|(h5)", +} +groups = {k: f"(?P<{k}>{v})" for k, v in patterns.items()} +pattern = "{prefix}-{start}-{duration}.{suffix}".format(**groups) +fname_re = re.compile(pattern) + + +def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: + """Use the name of a frame file to infer its initial timestamp and length + + Expects frame names to follow a standard nomenclature + where the name of the frame file ends {prefix}_{timestamp}-{length}.gwf + + Args: + fname: The name of the frame file + Returns: + The prefix of the frame file name + The initial GPS timestamp of the frame file + The length of the frame file in seconds + """ + + if isinstance(fname, Path): + fname = fname.name + + match = fname_re.search(fname) + if match is None: + raise ValueError(f"Could not parse frame filename {fname}") + + prefix, start, duration, _ = match.groups() + return prefix, int(start), int(duration) + + +@dataclass +class FrameFileFormat: + prefix: str + suffix: Literal["gwf", "hdf5", "h5"] = "gwf" + + @classmethod + def from_frame_file(cls, frame_file: PATH_LIKE): + prefix, _, __ = parse_frame_name(frame_file) + suffix = Path(frame_file).suffix[1:] + return cls(prefix, suffix) + + def get_name(self, timestamp: int, length: int): + if int(timestamp) != timestamp: + raise ValueError(f"Timestamp {timestamp} must be an int") + elif len(str(timestamp)) != 10: + raise ValueError( + "Couldn't create valid GPS timestamp from timestamp {}".format( + timestamp + ) + ) + + if length <= 0: + raise ValueError(f"Length {length} must be greater than 0") + elif int(length) != length: + raise ValueError(f"Length {length} must be an int") + elif not 1 <= len(str(length)) < 5: + raise ValueError(f"Frame length {length} invalid") + + return f"{self.prefix}-{timestamp}-{length}.{self.suffix}" + + +def _is_gwf(match): + return match is not None and match.group("suffix") == "gwf" + + +def get_prefix(data_dir: Path): + if not data_dir.exists(): + raise FileNotFoundError("No data directory '{data_dir}'") + + fnames = map(str, data_dir.iterdir()) + matches = map(fname_re.search, fnames) + matches = list(filter(_is_gwf, matches)) + + if len(matches) == 0: + raise ValueError(f"No valid .gwf files in data directory '{data_dir}'") + + prefixes = set([i.group("prefix") for i in matches]) + if len(prefixes) > 1: + raise ValueError( + "Too many prefixes {} in data directory '{}'".format( + list(prefixes), data_dir + ) + ) + + durations = set([i.group("duration") for i in matches]) + if len(durations) > 1: + raise ValueError( + "Too many lengths {} in data directory '{}'".format( + list(durations), data_dir + ) + ) + return list(prefixes)[0], int(list(durations)[0]) + + +@dataclass +class FrameCrawler: + data_dir: Path + t0: Optional[float] = None + timeout: Optional[float] = None + + def __post_init__(self): + prefix, self.length = get_prefix(self.data_dir) + self.file_format = FrameFileFormat(prefix) + + # t0 being None or 0 means start at the first timestamp + # -1 means start at the last + if self.t0 is None or self.t0 == 0 or self.t0 == -1: + matches = map(fname_re.search, map(str, self.data_dir.iterdir())) + starts = [int(i.group("start")) for i in matches if i is not None] + self.t0 = sorted(starts, reverse=self.t0 == -1)[0] + + def __iter__(self): + return self + + def __next__(self): + fname = self.file_format.get_name(self.t0, self.length) + fname = self.data_dir / fname + + start_time = time.time() + i, interval = 1, 3 + while not fname.exists(): + time.sleep(1e-3) + if (time.time() - start_time) > (i * interval): + logging.debug( + "Waiting for frame files for timestamp {}, " + "{}s elapsed".format(self.t0, i * interval) + ) + i += 1 + + if self.timeout is None: + continue + elif self.timeout == 0: + raise StopIteration + elif (time.time() - start_time) > self.timeout: + raise RuntimeError( + f"No frame file {fname} after {self.timeout}s" + ) + + self.t0 += self.length + return fname + + +def load_frame( + fname: str, channels: Union[str, Iterable[str]], sample_rate: float +) -> np.ndarray: + """Load the indicated channels from the indicated frame file""" + + if isinstance(channels, str): + # if we don't have multiple channels, then just grab the data + data = TimeSeries.read(fname, channels) + data = data.resample(sample_rate) + data = data.value + else: + # otherwise stack the arrays + data = TimeSeriesDict.read(fname, channels) + data = data.resample(sample_rate) + data = np.stack([data[i].value for i in channels]) + + # return as the expected type + return data.astype("float32") diff --git a/projects/microservice/utils/poetry.lock b/projects/microservice/utils/poetry.lock new file mode 100644 index 0000000..a90a549 --- /dev/null +++ b/projects/microservice/utils/poetry.lock @@ -0,0 +1,8 @@ +package = [] + +[metadata] +lock-version = "1.1" +python-versions = ">=3.8,<3.11" +content-hash = "be93d79ab4f7b8e30c72ada4242fd449eae3e11cca5547e7dd9f6c56e8d44836" + +[metadata.files] diff --git a/projects/microservice/utils/poetry.toml b/projects/microservice/utils/poetry.toml new file mode 100644 index 0000000..084377a --- /dev/null +++ b/projects/microservice/utils/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +create = false diff --git a/projects/microservice/utils/pyproject.toml b/projects/microservice/utils/pyproject.toml new file mode 100644 index 0000000..35fdd63 --- /dev/null +++ b/projects/microservice/utils/pyproject.toml @@ -0,0 +1,12 @@ +[tool.poetry] +name = "microservice" +version = "0.0.1" +description = "Utilities for DeepClean production deployment" +authors = ["Alec Gunny "] + +[tool.poetry.dependencies] +python = ">=3.8,<3.11" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" From 5ddd7259245027c41907d9f1ebdf6baa0d81ebd9 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 21 Mar 2023 15:59:07 -0700 Subject: [PATCH 07/31] finishing training microservice --- projects/microservice/trainer/trainer/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 84f5fd0..2b8714d 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -98,7 +98,7 @@ def main( last_start = None for X, y, start in data_it: - if last_start is not None and start > (last_start + train_duration): + if last_start is not None and start > (last_start + retrain_cadence): # TODO: insert any logic about how we do # training differently on a new lock segment pass From a5ae486ea881d3a007eaba3f1ff0666fe53f82c7 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Thu, 23 Mar 2023 15:30:55 -0500 Subject: [PATCH 08/31] getting training deployment working --- libs/trainer/deepclean/trainer/trainer.py | 87 ++++++-- libs/trainer/deepclean/trainer/utils.py | 1 - projects/microservice/environment.yaml | 4 +- projects/microservice/pyproject.toml | 107 ++++++++-- projects/microservice/singularity-compose.yml | 2 +- .../trainer/trainer/dataloader.py | 191 ++++++++++-------- .../microservice/trainer/trainer/trainer.py | 81 ++++---- 7 files changed, 304 insertions(+), 169 deletions(-) diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index e70de58..703fada 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -1,6 +1,5 @@ -from functools import partial from pathlib import Path -from typing import Callable, Optional, Tuple +from typing import Callable, Dict, Optional, Tuple import numpy as np import torch @@ -16,6 +15,50 @@ torch.set_default_tensor_type(torch.FloatTensor) +class DeepClean(torch.nn.Module): + def __init__(self, preprocessor, deepclean, postprocessor): + super().__init__() + self.preprocessor = preprocessor + self.deepclean = deepclean + self.postprocessor = postprocessor + + def forward(self, X): + X = self.preprocessor(X) + X = self.deepclean(X) + X = self.postprocessor(X, reverse=True) + return X + + +def get_weights(state_dict: Dict, prefix: str): + prefix = f"{prefix}." + weights = {} + for name, param in state_dict.items(): + if name.startswith(prefix): + name = name.replace(prefix, "") + weights[name] = param + return weights + + +def load_weights( + init_weights: Path, + model: torch.nn.Module, + preprocessor: ChannelWiseScaler +): + logger.info(f"Initializing model from checkpoint '{init_weights}'") + state_dict = torch.load(init_weights) + weights = get_weights(state_dict, "deepclean") + model.load_state_dict(weights) + + # if any of the witness channels have been + # dropped or gone constant since the last + # training, keep the old preprocessing params + preproc_params = get_weights(state_dict, "preprocessor") + mask = preprocessor.std == 0 + for param_name in ["mean", "std"]: + param = getattr(preprocessor, param_name) + param[mask] = preproc_params[param_name][mask] + + def train( architecture: Callable, output_directory: Path, @@ -172,6 +215,24 @@ def train( input_scaler.fit(X) output_scaler.fit(y) + # Create the model, potentially loading in weights from + # a file. Do this _before_ performing preprocessing + # so that we can potentially resolve any bad preprocessing + # parameters fit from the current witness data + logger.info("Building and initializing model") + model = architecture(len(X)).to(device) + logger.info(model) + + if init_weights is not None: + # allow us to easily point to the best weights + # from another run of this same function + if init_weights.is_dir(): + init_weights = init_weights / "weights.pt" + load_weights(init_weights, model, input_scaler) + # get rid of any remaining 0s in the preprocessing module + input_scaler.std[input_scaler.std == 0] = 1 + print(input_scaler.std) + # bandpass the strain up front so that we only # deal with the frequency ranges we care about strain_filter = BandpassFilter( @@ -191,7 +252,6 @@ def train( ) train_data.X = input_scaler(train_data.X) train_data.y = output_scaler(train_data.y) - if valid_data is not None: valid_X, valid_y = valid_data valid_y = strain_filter(valid_y) @@ -209,19 +269,7 @@ def train( valid_data.X = input_scaler(valid_data.X) valid_data.y = output_scaler(valid_data.y) - # Creating model, loss function, optimizer and lr scheduler - logger.info("Building and initializing model") - model = architecture(len(X)).to(device) - if init_weights is not None: - # allow us to easily point to the best weights - # from another run of this same function - if init_weights.is_dir(): - init_weights = init_weights / "weights.pt" - - logger.info(f"Initializing model from checkpoint '{init_weights}'") - model.load_state_dict(torch.load(init_weights)) - logger.info(model) - + # now set up the optimization function logger.info("Initializing loss and optimizer") criterion = CompositePSDLoss( alpha, @@ -278,13 +326,10 @@ def train( ) # reset batch sizes to be more manageable since - # gradient computation is analysis is expensive + # gradient computation in analysis is expensive valid_data.batch_size = train_data.batch_size = 64 analyze_model(output_directory, model, welch, train_data, valid_data) - # now create a version of the model which - # has the pre- and postprocessing built in - output_scaler.forward = partial(output_scaler.forward, reverse=True) - model = torch.nn.Sequential(input_scaler, model, output_scaler) + model = DeepClean(input_scaler, model, output_scaler) torch.save(model.state_dict(), weights_path) return weights_path diff --git a/libs/trainer/deepclean/trainer/utils.py b/libs/trainer/deepclean/trainer/utils.py index 81fba03..c554ced 100644 --- a/libs/trainer/deepclean/trainer/utils.py +++ b/libs/trainer/deepclean/trainer/utils.py @@ -109,7 +109,6 @@ def __call__( valid_data: Optional[DataGenerator] = None, profiler: Optional[torch.profiler.profile] = None, ) -> Tuple[float, Optional[float]]: - self.model.train() tracker = EpochTracker(profiler) for witnesses, strain in train_data: diff --git a/projects/microservice/environment.yaml b/projects/microservice/environment.yaml index 794b230..2e60fb8 100644 --- a/projects/microservice/environment.yaml +++ b/projects/microservice/environment.yaml @@ -3,6 +3,6 @@ channels: - conda-forge dependencies: - python=3.10 - - gwpy + - gwpy<3.0 - astropy - - ldas-tools-framecpp + - python-ldas-tools-framecpp<3.0 diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 9e6c698..81fe39c 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -1,29 +1,92 @@ [tool.typeo.base] channels = [ "H1:GDS-CALIB_STRAIN", - "H1:PEM-CS_MAINSMON_EBAY_1_DQ", - "H1:ASC-INP1_P_INMON", - "H1:ASC-INP1_Y_INMON", - "H1:ASC-MICH_P_INMON", - "H1:ASC-MICH_Y_INMON", - "H1:ASC-PRC1_P_INMON", - "H1:ASC-PRC1_Y_INMON", - "H1:ASC-PRC2_P_INMON", - "H1:ASC-PRC2_Y_INMON", - "H1:ASC-SRC1_P_INMON", - "H1:ASC-SRC1_Y_INMON", - "H1:ASC-SRC2_P_INMON", - "H1:ASC-SRC2_Y_INMON", - "H1:ASC-DHARD_P_INMON", - "H1:ASC-DHARD_Y_INMON", - "H1:ASC-CHARD_P_INMON", - "H1:ASC-CHARD_Y_INMON", - "H1:ASC-DSOFT_P_INMON", - "H1:ASC-DSOFT_Y_INMON", - "H1:ASC-CSOFT_P_INMON", - "H1:ASC-CSOFT_Y_INMON" + "H1:GDS-CALIB_F_S_SQUARED", + "H1:GDS-CALIB_F_S_SQUARED_NOGATE", + "H1:GDS-CALIB_SRC_Q_INVERSE", + "H1:GDS-CALIB_SRC_Q_INVERSE_NOGATE", + "H1:GDS-CALIB_F_CC", + "H1:GDS-CALIB_F_CC_NOGATE", + "H1:GDS-CALIB_KAPPA_C", + "H1:GDS-CALIB_KAPPA_C_NOGATE", + "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY", + "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY_NOGATE", + "H1:GDS-CALIB_KAPPA_PUM_REAL", + "H1:GDS-CALIB_KAPPA_PUM_REAL_NOGATE", + "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY", + "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY_NOGATE", + "H1:GDS-CALIB_KAPPA_UIM_REAL", + "H1:GDS-CALIB_KAPPA_UIM_REAL_NOGATE", + "H1:GDS-CALIB_KAPPA_TST_IMAGINARY", + "H1:GDS-CALIB_KAPPA_TST_IMAGINARY_NOGATE", + "H1:GDS-CALIB_KAPPA_TST_REAL", + "H1:GDS-CALIB_KAPPA_TST_REAL_NOGATE", + "H1:GDS-CALIB_STATE_VECTOR", ] +# "H1:PEM-CS_MAINSMON_EBAY_1_DQ", +# "H1:ASC-INP1_P_INMON", +# "H1:ASC-INP1_Y_INMON", +# "H1:ASC-MICH_P_INMON", +# "H1:ASC-MICH_Y_INMON", +# "H1:ASC-PRC1_P_INMON", +# "H1:ASC-PRC1_Y_INMON", +# "H1:ASC-PRC2_P_INMON", +# "H1:ASC-PRC2_Y_INMON", +# "H1:ASC-SRC1_P_INMON", +# "H1:ASC-SRC1_Y_INMON", +# "H1:ASC-SRC2_P_INMON", +# "H1:ASC-SRC2_Y_INMON", +# "H1:ASC-DHARD_P_INMON", +# "H1:ASC-DHARD_Y_INMON", +# "H1:ASC-CHARD_P_INMON", +# "H1:ASC-CHARD_Y_INMON", +# "H1:ASC-DSOFT_P_INMON", +# "H1:ASC-DSOFT_Y_INMON", +# "H1:ASC-CSOFT_P_INMON", +# "H1:ASC-CSOFT_Y_INMON" +# ] sample_rate = 4096 +verbose = true + +[tool.typeo.scripts.train] +# paths +output_directory = "${PROJECT_DIR}/train" +data_directory = "${DATA_DIR}/H1" + +# data parameters +channels = "${base.channels}" +train_duration = 2048 +retrain_cadence = 1024 +sample_rate = "${base.sample_rate}" +valid_frac = 0.25 + +# preprocessing parameters +kernel_length = 8 +kernel_stride = 0.0625 +freq_low = 55 +freq_high = 65 +filter_order = 8 +fftlength = 2 +overlap = 1 +alpha = 1 + +# optimziation parameters +batch_size = 256 +lr = 1e-3 +fine_tune_decay = 0.1 +weight_decay = 1e-5 +max_epochs = 50 +patience = 5 +factor = 0.1 + +# misc. parameters +device = "cuda:0" +verbose = "${base.verbose}" + +# architectures +[tool.typeo.scripts.train.architecture] +autoencoder = {} + [tool.typeo.scripts.clean] url = "localhost:8001" @@ -32,7 +95,7 @@ model_version = 1 witness_data_dir = "${DATA_DIR}/lldetchar" strain_data_dir = "${DATA_DIR}/llhoft" -write_dir = "${PROJECT_DIR}/production" +write_dir = "${PROJECT_DIR}/production/data" channels = "${base.channels}" kernel_length = 1 diff --git a/projects/microservice/singularity-compose.yml b/projects/microservice/singularity-compose.yml index 7c01502..9a7787f 100644 --- a/projects/microservice/singularity-compose.yml +++ b/projects/microservice/singularity-compose.yml @@ -1,7 +1,7 @@ version: "2.0" instances: server: - image: /cvmfs/singularity.opensciencegrid.org/fastml/gwiaas.tritonserver:stable + image: /cvmfs/singularity.opensciencegrid.org/ml4gw/hermes/tritonserver:22.12 network: enable: false allocate_ip: false diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index d6dff3b..9e4a98e 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -1,6 +1,7 @@ import sys import time import traceback +from dataclasses import dataclass from multiprocessing import Event, Process, Queue from pathlib import Path from queue import Empty @@ -29,13 +30,20 @@ def collect_frames( size = int(duration * sample_rate) edge_size = int(cadence * sample_rate) - X = np.zeros((len(channels), 0)) + X = np.zeros((len(channels) - 1, 0)) y = np.zeros((0,)) + # clear the first couple filenames to avoid + # a race condition where the first file + # accidentally gets cycled out before we start + for _ in range(2): + next(crawler) + while not event.is_set(): fname = next(crawler) witnesses = load_frame(fname, channels[1:], sample_rate) strain = load_frame(fname, channels[0], sample_rate) + # logger.debug(f"Loaded {fname} with size {len(strain)}, {len(y)}/{size}") X = np.concatenate([X, witnesses], axis=1) y = np.concatenate([y, strain]) @@ -58,88 +66,59 @@ def collect_frames( ) yield X, y, start - X = X[:, edge_size:] - y = y[edge_size:] - start += cadence - - -def target( - q: Queue, - event: Event, - data_directory: Path, - log_directory: Path, - channels: ChannelList, - duration: float, - cadence: float, - sample_rate: float, - timeout: Optional[float] = None, - verbose: bool = False, -): - logger.set_logger( - "DeepClean dataloading", - log_directory / "dataloader.log", - verbose=verbose, - ) - try: - it = collect_frames( - event, - data_directory, - channels, - duration, - cadence, - sample_rate, - timeout, - ) - for X, y, start in it: - q.put((X, y, start)) - except Exception: - exc_type, exc, tb = sys.exc_info() - tb = traceback.format_exception(exc_type, exc, tb) - tb = "".join(tb[:-1]) - q.put((exc_type, str(exc), tb)) - finally: - # if we arrived here from an exception, i.e. - # the event has not been set, then don't - # close the queue until the parent process - # has received the error message and set the - # event itself, otherwise it will never be - # able to receive the message from the queue - while not event.is_set(): + X = X[:, edge_size:] + y = y[edge_size:] + start += cadence + + +@dataclass +class DataCollector: + data_directory: Path + log_directory: Path + channels: ChannelList + duration: float + cadence: float + sample_rate: float + timeout: Optional[float] = None + verbose: bool = False + + def __enter__(self): + self.q = Queue() + self.event = Event() + self.done_event = Event() + self.clear_event = Event() + self.p = Process(target=self) + self.p.start() + return self._iter_through_q() + + def __exit__(self, *_): + # set the event to let the child process + # know that we're done with whatever data + # it's generating and it should stop + self.event.set() + + # wait for the child to indicate to us + # that it has been able to finish gracefully + while not self.done_event.is_set(): time.sleep(1e-3) - q.close() + # remove any remaining data from the queue + # to flush the child process's buffer so + # that it can exit gracefully, then close + # the queue from our end + self._clear_q() + self.q.close() + self.clear_event.set() -def data_collector( - data_directory: Path, - log_directory: Path, - channels: ChannelList, - duration: float, - cadence: float, - sample_rate: float, - timeout: Optional[float] = None, - verbose: bool = False, -): - q = Queue() - event = Event() - args = ( - q, - event, - data_directory, - log_directory, - channels, - duration, - cadence, - sample_rate, - timeout, - verbose, - ) - - p = Process(target=target, args=args) - p.start() - try: + # now wait for the child to exit + # gracefully then close it + self.p.join() + self.p.close() + + def _iter_through_q(self): while True: try: - X, y, start = q.get_nowait() + X, y, start = self.q.get_nowait() except Empty: time.sleep(1) continue @@ -151,8 +130,54 @@ def data_collector( ) raise exc_type(msg) yield X, y, start - finally: - event.set() - q.close() - p.join() - p.close() + + def _clear_q(self): + """Greedily empty our internal queue""" + + while True: + try: + self.q.get_nowait() + except Empty: + break + time.sleep(1e-3) + + def __call__(self): + logger.set_logger( + "DeepClean dataloading", + self.log_directory / "dataloader.log", + verbose=self.verbose, + ) + try: + it = collect_frames( + self.event, + self.data_directory, + self.channels, + self.duration, + self.cadence, + self.sample_rate, + self.timeout, + ) + for X, y, start in it: + self.q.put((X, y, start)) + except Exception: + exc_type, exc, tb = sys.exc_info() + tb = traceback.format_exception(exc_type, exc, tb) + tb = "".join(tb[:-1]) + self.q.put((exc_type, str(exc), tb)) + finally: + # now let the parent process know that + # there's no more information going into + # the queue, and it's free to empty it + self.done_event.set() + + # if we arrived here from an exception, i.e. + # the event has not been set, then don't + # close the queue until the parent process + # has received the error message and set the + # event itself, otherwise it will never be + # able to receive the message from the queue + while not self.event.is_set() or not self.clear_event.is_set(): + time.sleep(1e-3) + + self.q.close() + self.q.cancel_join_thread() diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 2b8714d..34f8d15 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -2,7 +2,7 @@ from typing import Callable, Optional import numpy as np -from trainer.dataloader import data_collector +from trainer.dataloader import DataCollector from deepclean.architectures import architectures from deepclean.logging import logger @@ -63,7 +63,7 @@ def train_on_segment( @scriptify( - kwargs=make_dummy(train, exclude=["X", "y", "architecture"]), + kwargs=make_dummy(train, exclude=["X", "y", "architecture", "valid_data"]), architecture=architectures, ) def main( @@ -85,7 +85,7 @@ def main( "DeepClean trainer", log_directory / "root.log", verbose=verbose ) - data_it = data_collector( + frame_collector = DataCollector( data_directory, log_directory, channels, @@ -97,41 +97,44 @@ def main( ) last_start = None - for X, y, start in data_it: - if last_start is not None and start > (last_start + retrain_cadence): - # TODO: insert any logic about how we do - # training differently on a new lock segment - pass - - root_logger.info( - "Launching training on segment {}-{}".format( - start, start + train_duration + with frame_collector as data_it: + for X, y, start in data_it: + if last_start is not None: + expected_start = last_start + retrain_cadence + if start > expected_start: + # TODO: insert any logic about how we do + # training differently on a new lock segment + pass + + root_logger.info( + "Launching training on segment {}-{}".format( + start, start + train_duration + ) ) - ) - - weights_path = train_on_segment( - X, - y, - output_directory=output_directory, - start=start, - architecture=architecture, - sample_rate=sample_rate, - valid_frac=valid_frac, - verbose=verbose, - **kwargs, - ) - - logger.set_logger("DeepClean train") - logger.info( - "Training on segment {}-{} complete, saved " - "optimized weights to {}".format( - start, start + train_duration, weights_path + + weights_path = train_on_segment( + X, + y, + output_directory=output_directory, + start=start, + architecture=architecture, + sample_rate=sample_rate, + valid_frac=valid_frac, + verbose=verbose, + **kwargs, + ) + + logger.set_logger("DeepClean train") + logger.info( + "Training on segment {}-{} complete, saved " + "optimized weights to {}".format( + start, start + train_duration, weights_path + ) ) - ) - - # for trainings after the first, use the previous - # optimized weights and reduce the learning rate - kwargs["init_weights"] = weights_path - if last_start is None: - kwargs["lr"] = kwargs["lr"] * fine_tune_decay - last_start = start + + # for trainings after the first, use the previous + # optimized weights and reduce the learning rate + kwargs["init_weights"] = weights_path + if last_start is None: + kwargs["lr"] = kwargs["lr"] * fine_tune_decay + last_start = start From 593c3c27185dafc18b1b22ca2f71d1fefa7702da Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 29 Mar 2023 14:06:21 -0500 Subject: [PATCH 09/31] adding production exporter --- libs/export/deepclean/export/__init__.py | 0 libs/export/deepclean/export/model.py | 39 + libs/export/deepclean/export/repo.py | 33 + libs/export/poetry.lock | 2778 ++++++++++++++++ libs/export/pyproject.toml | 25 + libs/trainer/deepclean/trainer/trainer.py | 15 +- libs/trainer/poetry.lock | 1119 ++++++- libs/trainer/pyproject.toml | 3 +- .../exporter/exporter/exporter.py | 183 + .../microservice/exporter/exporter/main.py | 10 + projects/microservice/exporter/poetry.lock | 2955 +++++++++++++++++ projects/microservice/exporter/pyproject.toml | 36 + .../exporter/tests/test_exporter.py | 40 + .../microservice/trainer/trainer/trainer.py | 2 +- 14 files changed, 7173 insertions(+), 65 deletions(-) create mode 100644 libs/export/deepclean/export/__init__.py create mode 100644 libs/export/deepclean/export/model.py create mode 100644 libs/export/deepclean/export/repo.py create mode 100644 libs/export/poetry.lock create mode 100644 libs/export/pyproject.toml create mode 100644 projects/microservice/exporter/exporter/exporter.py create mode 100644 projects/microservice/exporter/exporter/main.py create mode 100644 projects/microservice/exporter/poetry.lock create mode 100644 projects/microservice/exporter/pyproject.toml create mode 100644 projects/microservice/exporter/tests/test_exporter.py diff --git a/libs/export/deepclean/export/__init__.py b/libs/export/deepclean/export/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/libs/export/deepclean/export/model.py b/libs/export/deepclean/export/model.py new file mode 100644 index 0000000..fd41661 --- /dev/null +++ b/libs/export/deepclean/export/model.py @@ -0,0 +1,39 @@ +import torch +from ml4gw.transforms import ChannelWiseScaler + + +class Preprocessor(torch.nn.Module): + def __init__(self, scaler: ChannelWiseScaler) -> None: + super().__init__() + self.scaler = scaler + + def forward(self, X: torch.Tensor) -> torch.Tensor: + return self.scaler(X) + + +class Postprocessor(torch.nn.Module): + def __init__(self, scaler: ChannelWiseScaler) -> None: + super().__init__() + self.scaler = scaler + + def forward(self, X: torch.Tensor): + return self.scaler(X, reverse=True) + + +class DeepClean(torch.nn.Module): + def __init__( + self, + preprocessor: ChannelWiseScaler, + deepclean: torch.nn.Module, + postprocessor: ChannelWiseScaler + ) -> None: + super().__init__() + self.preprocessor = Preprocessor(preprocessor) + self.deepclean = deepclean + self.postprocessor = Postprocessor(postprocessor) + + def forward(self, X: torch.Tensor) -> torch.Tensor: + X = self.preprocessor(X) + X = self.deepclean(X) + X = self.postprocessor(X) + return X \ No newline at end of file diff --git a/libs/export/deepclean/export/repo.py b/libs/export/deepclean/export/repo.py new file mode 100644 index 0000000..eb47f44 --- /dev/null +++ b/libs/export/deepclean/export/repo.py @@ -0,0 +1,33 @@ +from typing import List, Optional + +from hermes import quiver as qv +from tritonclient.grpc import model_config_pb2 as model_config + + +def configure_stateful( + config: model_config.ModelConfig, + idle_time: float +): + idle_time = int(idle_time * 10**6) + config.sequence_batching.max_sequence_idle_microseconds = idle_time + + +def initialize_repo( + repo_dir: str, + channels: List[str], + platform: qv.Platform, + instances: Optional[int] = None +): + repo = qv.ModelRepository(repo_dir) + try: + deepclean = repo.models["deepclean"] + except KeyError: + deepclean = repo.add("deepclean", platform=platform) + if instances is not None: + deepclean.config.add_instance_group(count=instances) + else: + if instances is not None: + deepclean.config.scale_instance_group(count=instances) + + deepclean.config.parameters["channels"].string_value = ",".join(channels) + return repo \ No newline at end of file diff --git a/libs/export/poetry.lock b/libs/export/poetry.lock new file mode 100644 index 0000000..258f77a --- /dev/null +++ b/libs/export/poetry.lock @@ -0,0 +1,2778 @@ +[[package]] +name = "aiohttp" +version = "3.8.4" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<4.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "cchardet"] + +[[package]] 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+packages = [ + {include = "deepclean"} +] + +[tool.poetry.dependencies] +python = "^3.8,<3.11" +ml4gw = {path = "../../ml4gw", develop = true} + +[tool.poetry.dependencies."hermes.quiver"] +path = "../../hermes/hermes/hermes.quiver" +develop = true +extras = ["torch"] + +[tool.poetry.dev-dependencies] +pytest = "^6.2" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 703fada..0af4772 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -5,6 +5,7 @@ import torch from deepclean.logging import logger +from deepclean.export.model import DeepClean from deepclean.trainer import CompositePSDLoss from deepclean.trainer.analysis import analyze_model from deepclean.trainer.utils import Checkpointer, Trainer @@ -15,20 +16,6 @@ torch.set_default_tensor_type(torch.FloatTensor) -class DeepClean(torch.nn.Module): - def __init__(self, preprocessor, deepclean, postprocessor): - super().__init__() - self.preprocessor = preprocessor - self.deepclean = deepclean - self.postprocessor = postprocessor - - def forward(self, X): - X = self.preprocessor(X) - X = self.deepclean(X) - X = self.postprocessor(X, reverse=True) - return X - - def get_weights(state_dict: Dict, prefix: str): prefix = f"{prefix}." weights = {} diff --git a/libs/trainer/poetry.lock b/libs/trainer/poetry.lock index be871b9..265abe7 100644 --- a/libs/trainer/poetry.lock +++ b/libs/trainer/poetry.lock @@ -1,3 +1,34 @@ +[[package]] +name = "aiohttp" +version = "3.8.4" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<4.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + 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["matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"] +test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist"] +test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "sgp4 (>=2.3)", "skyfield (>=1.20)"] + +[[package]] +name = "async-timeout" +version = "4.0.2" +description = "Timeout context manager for asyncio programs" +category = "main" +optional = false +python-versions = ">=3.6" [[package]] name = "atomicwrites" @@ -36,12 +75,11 @@ optional = false python-versions = ">=3.6" [package.extras] -cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] -dev = ["attrs"] -docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["attrs", "zope-interface"] -tests-no-zope = ["hypothesis", 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+description = "Python bindings for the Brotli compression library" +category = "main" +optional = false +python-versions = "*" + [[package]] name = "certifi" version = "2022.12.7" @@ -130,8 +176,8 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "chromedriver", "selenium"] docs = ["furo", "sphinx-copybutton"] -mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] -test = ["matplotlib", "pillow", "pytest"] +mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"] +test = ["Pillow", "matplotlib", "pytest"] test-no-images = ["pytest"] [[package]] @@ -147,9 +193,9 @@ matplotlib = ">=2.1" [package.extras] arviz = ["arviz (>=0.9)"] -dev = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml", "arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme", "pre-commit", "flake8", "pep517", "twine"] -docs = ["arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme"] -test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] +dev = ["arviz (>=0.9)", "black", "flake8", "isort", "myst-nb", "pandoc", "pep517", "pre-commit", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "sphinx (>=1.7.5)", "sphinx-book-theme", "toml", "twine"] +docs = ["arviz (>=0.9)", "myst-nb", "pandoc", "sphinx (>=1.7.5)", "sphinx-book-theme"] +test = ["black", "isort", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "toml"] [[package]] name = "cryptography" @@ -164,11 +210,11 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] -docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "ruff", "mypy", "types-pytz", "types-requests", "check-manifest"] +docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] +pep8test = ["black", "check-manifest", "mypy", "ruff", "types-pytz", "types-requests"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test = ["hypothesis (>=1.11.4,!=3.79.2)", "iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist", "pytz"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -180,6 +226,23 @@ category = "main" optional = false python-versions = ">=3.6" +[[package]] +name = "deepclean-export" +version = "0.0.1" +description = "IaaS export utilities" +category = "main" +optional = false +python-versions = "^3.8,<3.11" +develop = true + +[package.dependencies] +"hermes.quiver" = {path = "../../hermes/hermes/hermes.quiver", develop = true, extras = ["torch"]} +ml4gw = {path = "../../ml4gw", develop = true} + +[package.source] +type = "directory" +url = "../export" + [[package]] name = "deepclean-utils" version = "0.0.1" @@ -245,7 +308,7 @@ numpy = "*" [package.extras] extras = ["h5py", "scipy"] -tests = ["pytest", "pytest-cov", "coverage"] +tests = ["coverage[toml]", "pytest", "pytest-cov"] [[package]] name = "fonttools" @@ -256,9 +319,9 @@ optional = false python-versions = ">=3.7" [package.extras] -all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -267,7 +330,74 @@ 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+import argparse +from dataclasses import dataclass +from pathlib import Path +from typing import Optional + +import torch +from ml4gw.transforms import ChannelWiseScaler +from typeo.parser import make_parser + +import hermes.quiver as qv +from deepclean.architectures import DeepCleanAE as architecture +from deepclean.utils.channels import ChannelList, get_channels +from deepclean.export import repo as repo_utils +from deepclean.export.model import DeepClean + + +@dataclass +class Exporter: + repository_directory: str + channels: ChannelList + kernel_length: float + sample_rate: float + inference_sampling_rate: float + batch_size: int + aggregation_time: float + streams_per_gpu: int = 2 + platform: qv.Platform = qv.Platform.ONNX + instances: Optional[int] = None + + @classmethod + def from_config(cls, config: Optional[Path] = None): + if config is None: + config = Path(__file__).parent.parent / "pyproject.toml" + + parser = argparse.ArgumentParser() + make_parser(cls, parser) + args = parser.parse_args(["--typeo", config]) + return cls(**vars(args)) + + def __post_init__(self): + self.channels = get_channels(self.channels) + self.repo = repo_utils.initialize_repo( + self.repository_directory, + self.channels[1:], + self.platform, + self.instances + ) + + self.num_witnesses = len(self.channels) - 1 + preprocessor = ChannelWiseScaler(self.num_witnesses) + postprocessor = ChannelWiseScaler() + + preprocessor.built = True + postprocessor.built = True + + deepclean = architecture(self.num_witnesses) + self.model = DeepClean(preprocessor, deepclean, postprocessor) + + kernel_size = int(self.sample_rate * self.kernel_length) + stream_size = int(self.sample_rate / self.inference_sampling_rate) + num_updates = int(self.aggregation_time * self.inference_sampling_rate) + + input_shape = (self.batch_size, self.num_witnesses, kernel_size) + preproc = self.repo.add("preprocessor", platform=qv.Platform.ONNX) + preproc.export_version( + self.model.preprocessor, + input_shapes={"witnesses": input_shape}, + output_names=["normalized"] + ) + + self.repo.models["deepclean"].export_version( + deepclean, + input_shapes={"normalized": input_shape}, + output_names=["noise_prediction"] + ) + + postproc = self.repo.add("postprocessor", platform=qv.Platform.ONNX) + postproc.export_version( + self.model.postprocessor, + input_shapes={"noise_prediction": (self.batch_size, kernel_size)}, + output_names=["unscaled"] + ) + + production = self.repo.add( + "deepclean-stream-production", platform=qv.Platform.ENSEMBLE + ) + production.add_streaming_inputs( + inputs=[preproc.inputs["witnesses"]], + stream_size=stream_size, + name="snapshotter", + streams_per_gpu=self.streams_per_gpu, + batch_size=self.batch_size + ) + production.add_streaming_output( + postproc.outputs["unscaled"], + update_size=stream_size, + num_updates=num_updates, + batch_size=self.batch_size, + name="aggregator", + streams_per_gpu=self.streams_per_gpu + ) + self.construct_ensemble(production) + + snapshotter = self.repo.models["snapshotter"] + aggregator = self.repo.models["aggregator"] + + # now set up the non-production model, piping + # inputs and outputs to the snaphotter and + # aggregator models explicitly + canary = self.repo.add( + "deepclean-stream-canary", platform=qv.Platform.ENSEMBLE + ) + canary.pipe( + snapshotter.outputs["preprocessor.witnesses_snapshot"], + preproc.inputs["witnesses"] + ) + self.construct_ensemble(canary) + canary.pipe( + postproc.outputs["unscaled"], + aggregator.inputs["update"] + ) + + self.configure_stateful(self.repo.models["snapshotter"]) + self.configure_stateful(self.repo.models["aggregator"]) + + def configure_stateful(self, model): + repo_utils.configure_stateful(model.config, 60) + model.config.write() + + def construct_ensemble(self, ensemble): + ensemble.pipe( + self.repo.models["preprocessor"].outputs["normalized"], + self.repo.models["deepclean"].inputs["normalized"], + ) + ensemble.pipe( + self.repo.models["deepclean"].outputs["noise_prediction"], + self.repo.models["postprocessor"].inputs["noise_prediction"] + ) + self.update_ensemble_versions(ensemble, 1) + + def update_ensemble_versions(self, ensemble, version): + for step in ensemble.config.ensemble_scheduling.step: + if step.model_name not in ["snapshotter", "aggregator"]: + step.model_version = version + ensemble.config.write() + + @property + def preprocessor(self): + return self.model.preprocessor + + @property + def postprocessor(self): + return self.model.postprocessor + + @property + def deepclean(self): + return self.model.deepclean + + def export_version(self, train_dir: Path): + weights_path = train_dir / "weights.pt" + + state_dict = torch.load(weights_path) + self.deepclean.load_state_dict(state_dict) + + self.repo.models["preprocessor"].export_version(self.preprocessor) + self.repo.models["postprocessor"].export_version(self.postprocessor) + + kwargs = {} + if self.platform == qv.Platform.TENSORRT: + kwargs["use_fp16"] = True + export_path = self.repo.models["deepclean"].export_version( + self.deepclean, **kwargs + ) + version = int(Path(export_path).parent.name) + self.update_ensemble_versions( + self.repo.models["deepclean-stream-canary"], version + ) + + _, start, stop = train_dir.name.split("-") + self.deepclean.config.parameters["versions"] += "{}={}-{},".format( + version, start, stop + ) + self.deepclean.config.write() diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py new file mode 100644 index 0000000..9d99131 --- /dev/null +++ b/projects/microservice/exporter/exporter/main.py @@ -0,0 +1,10 @@ +from flask import Flask +from export.exporter import Exporter + + +app = Flask(__name__) +exporter = Exporter.from_config() + +@app.route("/") +def export_model(train_dir): + exporter.export_version(train_dir) diff --git a/projects/microservice/exporter/poetry.lock b/projects/microservice/exporter/poetry.lock new file mode 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+[tool.poetry] +name = "exporter" +version = "0.0.1" +description = "DeepClean production IaaS export application" +authors = ["Alec Gunny "] +# readme = "README.md" + +[tool.poetry.dependencies] +python = "^3.8,<3.11" +tensorrt = "8.5.1.7" +flask = "^2.2" + +torch = {version = "^1.10", source = "torch"} +ml4gw = {path = "../../../ml4gw", develop = true} + +typeo = {path = "../../../typeo", develop = true} +"deepclean.architectures" = {path = "../../../libs/architectures", develop = true} +"deepclean.export" = {path = "../../../libs/export", develop = true} +"deepclean.logging" = {path = "../../../libs/logging", develop = true} +"deepclean.utils" = {path = "../../../libs/utils", develop = true} + +[tool.poetry.dependencies."hermes.quiver"] +path = "../../../hermes/hermes/hermes.quiver" +develop = true +extras = ["torch", "tensortt"] + +[tool.poetry.dev-dependencies] +pytest = "^7.0" + +[[tool.poetry.source]] +name = "torch" +url = "https://download.pytorch.org/whl/cpu" + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/projects/microservice/exporter/tests/test_exporter.py b/projects/microservice/exporter/tests/test_exporter.py new file mode 100644 index 0000000..58b2062 --- /dev/null +++ b/projects/microservice/exporter/tests/test_exporter.py @@ -0,0 +1,40 @@ +from exporter.exporter import Exporter + +import pytest +from tritonclient.grpc import model_config_pb2 as model_config +from google.protobuf import text_format + + +def load_config(model): + config = model_config.ModelConfig() + with open(model / "config.pbtxt", "r") as f: + text_format.Merge(f.read(), config) + return config + + +class TestExporter: + def test_init(self, tmp_path): + tmp_path.mkdir(exist_ok=True, parents=True) + exporter = Exporter( + tmp_path, + channels=list("DCAEB"), + kernel_length=1, + sample_rate=4096, + inference_sampling_rate=512, + batch_size=128, + aggregation_time=0.25, + streams_per_gpu=2, + instances=4 + ) + + deepclean_path = tmp_path / "deepclean" + for fname in deepclean_path.iterdir(): + assert fname.name in ("1", "config.pbtxt") + deepclean_config = load_config(deepclean_path) + + assert deepclean_config.input[0].dims == [128, 4, 4096] + assert deepclean_config.output[0].dims == [128, 4096] + assert deepclean_config.instance_group[0].count == 4 + assert deepclean_config.parameters["channels"].string_value == ( + "A,B,C,E" + ) \ No newline at end of file diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 34f8d15..47e23cd 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -36,7 +36,7 @@ def train_on_segment( duration = len(y) / sample_rate str_rep = _get_str(start, start + duration) - log_file = output_directory / "log" / f"train_{str_rep}.log" + log_file = output_directory / "log" / f"train-{str_rep}.log" output_directory = output_directory / "training" / str_rep logger.info(f"Saving training outputs to directory {output_directory}") From 2bfb55898bf65a48e28627e85bc29c4fe0565bd8 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 29 Mar 2023 20:48:57 -0500 Subject: [PATCH 10/31] getting ensemble export working --- hermes | 2 +- libs/export/deepclean/export/repo.py | 5 +- .../exporter/exporter/exporter.py | 153 ++++++++++-------- 3 files changed, 90 insertions(+), 70 deletions(-) diff --git a/hermes b/hermes index 12e19f1..2a8dea7 160000 --- a/hermes +++ b/hermes @@ -1 +1 @@ -Subproject commit 12e19f1887616ebb025846128e08e99e7338619e +Subproject commit 2a8dea7ca0b9fe898c50f82d4190915019822033 diff --git a/libs/export/deepclean/export/repo.py b/libs/export/deepclean/export/repo.py index eb47f44..df58204 100644 --- a/libs/export/deepclean/export/repo.py +++ b/libs/export/deepclean/export/repo.py @@ -16,9 +16,10 @@ def initialize_repo( repo_dir: str, channels: List[str], platform: qv.Platform, - instances: Optional[int] = None + instances: Optional[int] = None, + clean: bool = False ): - repo = qv.ModelRepository(repo_dir) + repo = qv.ModelRepository(repo_dir, clean=clean) try: deepclean = repo.models["deepclean"] except KeyError: diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index dc8968d..6596654 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -39,28 +39,45 @@ def from_config(cls, config: Optional[Path] = None): def __post_init__(self): self.channels = get_channels(self.channels) - self.repo = repo_utils.initialize_repo( - self.repository_directory, - self.channels[1:], - self.platform, - self.instances - ) - self.num_witnesses = len(self.channels) - 1 + + # start by initializing a full model instance + # we'll use to populate weights into preprocessor = ChannelWiseScaler(self.num_witnesses) postprocessor = ChannelWiseScaler() + deepclean = architecture(self.num_witnesses) + self.model = DeepClean(preprocessor, deepclean, postprocessor) + # set these manually since we don't actually + # need to do any fitting, but we're going to + # export a dummy version before we load any weights preprocessor.built = True postprocessor.built = True - deepclean = architecture(self.num_witnesses) - self.model = DeepClean(preprocessor, deepclean, postprocessor) - + # define a few helpful values kernel_size = int(self.sample_rate * self.kernel_length) stream_size = int(self.sample_rate / self.inference_sampling_rate) num_updates = int(self.aggregation_time * self.inference_sampling_rate) - input_shape = (self.batch_size, self.num_witnesses, kernel_size) + output_shape = (self.batch_size, kernel_size) + + # initialize an empty repository with an + # associated deepclean entry already in it + self.repo = repo_utils.initialize_repo( + self.repository_directory, + self.channels[1:], + self.platform, + self.instances, + clean=True + ) + + # initialize an ensemble that we'll add steps to + ensemble = self.repo.add( + "deepclean-stream", platform=qv.Platform.ENSEMBLE + ) + + # start by creating a repo entry + # for the preprocessing model preproc = self.repo.add("preprocessor", platform=qv.Platform.ONNX) preproc.export_version( self.model.preprocessor, @@ -68,78 +85,80 @@ def __post_init__(self): output_names=["normalized"] ) - self.repo.models["deepclean"].export_version( - deepclean, - input_shapes={"normalized": input_shape}, - output_names=["noise_prediction"] - ) - - postproc = self.repo.add("postprocessor", platform=qv.Platform.ONNX) - postproc.export_version( - self.model.postprocessor, - input_shapes={"noise_prediction": (self.batch_size, kernel_size)}, - output_names=["unscaled"] - ) - - production = self.repo.add( - "deepclean-stream-production", platform=qv.Platform.ENSEMBLE - ) - production.add_streaming_inputs( + # now create a snapshotter instance + # for this preprocessor, mapped to + # the latest version for the canary model + ensemble.add_streaming_inputs( inputs=[preproc.inputs["witnesses"]], stream_size=stream_size, name="snapshotter", streams_per_gpu=self.streams_per_gpu, - batch_size=self.batch_size - ) - production.add_streaming_output( - postproc.outputs["unscaled"], - update_size=stream_size, - num_updates=num_updates, batch_size=self.batch_size, - name="aggregator", - streams_per_gpu=self.streams_per_gpu + versions=-1 ) - self.construct_ensemble(production) + # now create a second mapping from the + # snapshot state to the fixed production + # version of the preprocessor model snapshotter = self.repo.models["snapshotter"] - aggregator = self.repo.models["aggregator"] - - # now set up the non-production model, piping - # inputs and outputs to the snaphotter and - # aggregator models explicitly - canary = self.repo.add( - "deepclean-stream-canary", platform=qv.Platform.ENSEMBLE - ) - canary.pipe( + ensemble.pipe( snapshotter.outputs["preprocessor.witnesses_snapshot"], - preproc.inputs["witnesses"] - ) - self.construct_ensemble(canary) - canary.pipe( - postproc.outputs["unscaled"], - aggregator.inputs["update"] + preproc.inputs["witnesses"], + inbound_version=1 ) - self.configure_stateful(self.repo.models["snapshotter"]) - self.configure_stateful(self.repo.models["aggregator"]) - - def configure_stateful(self, model): - repo_utils.configure_stateful(model.config, 60) - model.config.write() - - def construct_ensemble(self, ensemble): - ensemble.pipe( - self.repo.models["preprocessor"].outputs["normalized"], - self.repo.models["deepclean"].inputs["normalized"], + # now export deepclean, and create mappings + # from each version of the preprocessor to + # the corresponding version of deepclean + self.repo.models["deepclean"].export_version( + deepclean, + input_shapes={"normalized": input_shape}, + output_names=["noise_prediction"] ) - ensemble.pipe( - self.repo.models["deepclean"].outputs["noise_prediction"], - self.repo.models["postprocessor"].inputs["noise_prediction"] + + keys = {"canary": -1, "production": 1} + for key, version in keys.items(): + ensemble.pipe( + preproc.outputs["normalized"], + self.repo.models["deepclean"].inputs["normalized"], + key=f"normalized-{key}", + outbound_version=version, + inbound_version=version + ) + + # now do the same for a postprocessing model, + # adding a streaming output from the model + # to the back end of our ensemble + postproc = self.repo.add("postprocessor", platform=qv.Platform.ONNX) + postproc.export_version( + self.model.postprocessor, + input_shapes={"noise_prediction": output_shape}, + output_names=["unscaled"] ) - self.update_ensemble_versions(ensemble, 1) + for key, version in keys.items(): + ensemble.pipe( + self.repo.models["deepclean"].outputs["noise_prediction"], + postproc.inputs["noise_prediction"], + key=f"noise_prediction-{key}", + outbound_version=version, + inbound_version=version + ) + ensemble.add_streaming_output( + postproc.outputs["unscaled"], + update_size=stream_size, + num_updates=num_updates, + batch_size=self.batch_size, + name=f"aggregator-{key}", + key=f"unscaled-{key}", + streams_per_gpu=self.streams_per_gpu, + version=version + ) + ensemble.export_version(None) def update_ensemble_versions(self, ensemble, version): for step in ensemble.config.ensemble_scheduling.step: + if step.model_version == -1: + continue if step.model_name not in ["snapshotter", "aggregator"]: step.model_version = version ensemble.config.write() From 9e4689807e118a4fb11688d37ce354b3e6a14033 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 4 Apr 2023 20:11:13 -0500 Subject: [PATCH 11/31] overhauling inference/cleaning picture --- libs/infer/deepclean/infer/callback.py | 152 ++++ libs/infer/deepclean/infer/clean.py | 2 +- libs/infer/deepclean/infer/frame_crawler.py | 88 --- libs/infer/deepclean/infer/load.py | 78 -- libs/infer/deepclean/infer/write.py | 283 -------- libs/infer/poetry.lock | 681 +++--------------- libs/infer/poetry.toml | 2 - libs/infer/pyproject.toml | 2 +- .../microservice/cleaner/cleaner/cleaner.py | 85 ++- .../cleaner/cleaner/{ => cleaner}/__init__.py | 0 .../cleaner/cleaner/cleaner/cleaner.py | 218 ++++++ .../cleaner/cleaner/cleaner/dataloader.py | 73 ++ .../cleaner/cleaner/cleaner/writer.py | 36 + projects/microservice/cleaner/pyproject.toml | 11 +- .../exporter/exporter/exporter.py | 43 +- 15 files changed, 655 insertions(+), 1099 deletions(-) create mode 100644 libs/infer/deepclean/infer/callback.py delete mode 100644 libs/infer/deepclean/infer/frame_crawler.py delete mode 100644 libs/infer/deepclean/infer/load.py delete mode 100644 libs/infer/deepclean/infer/write.py delete mode 100644 libs/infer/poetry.toml rename projects/microservice/cleaner/cleaner/{ => cleaner}/__init__.py (100%) create mode 100644 projects/microservice/cleaner/cleaner/cleaner/cleaner.py create mode 100644 projects/microservice/cleaner/cleaner/cleaner/dataloader.py create mode 100644 projects/microservice/cleaner/cleaner/cleaner/writer.py diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py new file mode 100644 index 0000000..9b12fbc --- /dev/null +++ b/libs/infer/deepclean/infer/callback.py @@ -0,0 +1,152 @@ +import logging +import math +import time +from typing import Callable, Optional + +import numpy as np + + +class State: + def __init__( + self, + name: str, + frame_length: float, + memory: float, + filter_pad: float, + sample_rate: float, + inference_sampling_rate: float, + batch_size: int, + aggregation_steps: int + ) -> None: + self.frame_size = int(sample_rate * frame_length) + self.stride = int(sample_rate // inference_sampling_rate) + self.steps_per_frame = math.ceil(self.frame_size / self.stride) + + self.memory = int(memory * sample_rate) + self.samples_ahead = int(filter_pad * sample_rate) + self.steps_ahead = math.ceil(self.samples_ahead / self.stride) + + self.batch_size = batch_size + self.aggregation_steps = aggregation_steps + self.agg_batches, self.agg_leftover = divmod( + aggregation_steps, batch_size + ) + + self._frame_idx = 0 + self._latest_seen = 0 + self.logger = logging.getLogger(f"Output state {name}") + + def validate(self, response, request_id): + # flatten out to 1D and verify that this + # output aligns with our expectations + x = response.reshape(-1) + if len(x) != (self.stride * self.batch_size): + raise ValueError( + "Noise prediction is of wrong length {}, " + "expected length {}".format(len(x), self.stride) + ) + + # now make sure that this request is arriving in order + if request_id < self._latest_seen: + self.logger.warning(f"Request id {request_id} came in late") + else: + if request_id > (self._latest_seen + 1): + # if we skipped some requests, log the ones we missed + for i in range(1, request_id - self._latest_seen): + idx = self._latest_seen + i + self.logger.warning(f"No response for request id {idx}") + + # update `self._latest_seen` to reflect whatever + # the new latest request id is + self._latest_seen = request_id + + if request_id < self.agg_batches: + # throw away the first `aggregation_steps` responses, since + # these technically corresponds to predictions from the _past_. + # Return `None`s so that we know not to update our internal arrays + self.logger.debug( + f"Throwing away received package id {request_id}" + ) + return None + elif request_id == self.agg_batches: + # if our batch size doesn't evenly divide the number + # of aggregation steps, slice off any steps in this + # batch of responses that are aggregation steps + x = x[self.agg_leftover * self.stride :] + + # otherwise return the parsed array and its request id + return x + + def update(self, x, request_id): + x = self.validate(x, request_id) + if x is None: + return None + + # use the package request id to figure out where + # in the blank noise array we need to insert + # this prediction. Subtract the steps that we threw away + total_steps = request_id * self.batch_size + step_idx = total_steps - self.aggregation_steps + start_idx = 0 if step_idx < 0 else int(step_idx * self.stride) + + # If we've sloughed off any past data so far, + # make sure to account for that + frame_idx = self._frame_idx * self.frame_size + start_idx -= max(frame_idx - self.memory, 0) + + # now make sure that we have data to fill out, + # otherwise extend the array + if (start_idx + len(x)) > len(self._noise): + self._noise = np.append(self._noise, self._zeros) + + # now insert the response into the existing array + # TODO: should we check that this is all 0s to + # double check ourselves here? + self._noise[start_idx : start_idx + len(x)] = x + + # return the number of steps that have been completed + step_idx = step_idx + self.batch_size + + # modulate the total number of cleaning steps taken by the + # number of steps in each frame to see if the current + # cleanable index is past the end of the next frame + div, rem = divmod(step_idx, self.steps_per_frame) + if ( + (div == self._frame_idx and rem >= self.steps_ahead) + or div > self._frame_idx + ): + frame_idx = self._frame_idx * self.frame_size + idx = min(self.memory, frame_idx) + idx += self.frame_size + self.samples_ahead + noise = self._noise[:idx] + + extra = len(noise) - self.samples_ahead - self.memory + if extra > 0: + self._noise = self._noise[extra:] + self._frame_idx += 1 + return noise + return None + + +class Callback: + def __init__( + self, postprocessor: Optional[Callable] = None, **states: State + ): + self.postprocessor = postprocessor + self.states = states + + def block(self, i: int = 1): + while any([state._latest_seen < i for state in self.states.values()]): + time.sleep(1e-3) + + def __call__(self, responses: np.ndarray, request_id: int, *arg): + predictions = {} + for state_name, y in responses.items(): + state = self.states[state_name] + prediction = state.update(y, request_id) + if prediction is not None: + predictions[state_name] = prediction + + if predictions and self.postprocessor is not None: + return self.postprocessor(**predictions) + return None diff --git a/libs/infer/deepclean/infer/clean.py b/libs/infer/deepclean/infer/clean.py index 001cf4e..b78f941 100644 --- a/libs/infer/deepclean/infer/clean.py +++ b/libs/infer/deepclean/infer/clean.py @@ -19,7 +19,7 @@ def __post_init__(self): self.freq_low, self.freq_high, self.sample_rate ) self.pad_size = int(self.filter_pad * self.sample_rate) - self.kernel_Size = int(self.kernel_length * self.sample_rate) + self.kernel_size = int(self.kernel_length * self.sample_rate) self.window = windows.hann(2 * self.pad_size) def __call__(self, noise: np.ndarray, strain: np.ndarray) -> np.ndarray: diff --git a/libs/infer/deepclean/infer/frame_crawler.py b/libs/infer/deepclean/infer/frame_crawler.py deleted file mode 100644 index 5da6224..0000000 --- a/libs/infer/deepclean/infer/frame_crawler.py +++ /dev/null @@ -1,88 +0,0 @@ -import logging -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Optional - -from deepclean.gwftools.frames import FrameFileFormat, fname_re - - -def _is_gwf(match): - return match is not None and match.group("suffix") == "gwf" - - -def get_prefix(data_dir: Path): - if not data_dir.exists(): - raise FileNotFoundError("No data directory '{data_dir}'") - - fnames = map(str, data_dir.iterdir()) - matches = map(fname_re.search, fnames) - matches = list(filter(_is_gwf, matches)) - - if len(matches) == 0: - raise ValueError(f"No valid .gwf files in data directory '{data_dir}'") - - prefixes = set([i.group("prefix") for i in matches]) - if len(prefixes) > 1: - raise ValueError( - "Too many prefixes {} in data directory '{}'".format( - list(prefixes), data_dir - ) - ) - - lengths = set([i.group("length") for i in matches]) - if len(lengths) > 1: - raise ValueError( - "Too many lengths {} in data directory '{}'".format( - list(lengths), data_dir - ) - ) - return list(prefixes)[0], int(list(lengths)[0]) - - -@dataclass -class FrameCrawler: - data_dir: Path - t0: Optional[float] = None - timeout: Optional[float] = None - - def __post_init__(self): - prefix, self.length = get_prefix(self.data_dir) - self.file_format = FrameFileFormat(prefix) - - # t0 being None or 0 means start at the first timestamp - # -1 means start at the last - if self.t0 is None or self.t0 == 0 or self.t0 == -1: - matches = map(fname_re.search, map(str, self.data_dir.iterdir())) - tstamps = [int(i.group("t0")) for i in matches if i is not None] - self.t0 = sorted(tstamps, reverse=self.t0 == -1)[0] - - def __iter__(self): - return self - - def __next__(self): - fname = self.file_format.get_name(self.t0, self.length) - fname = self.data_dir / fname - - start_time = time.time() - i, interval = 1, 3 - while not fname.exists(): - time.sleep(1e-3) - if (time.time() - start_time) > (i * interval): - logging.debug( - "Waiting for frame files for timestamp {}, " - "{}s elapsed".format(self.t0, i * interval) - ) - i += 1 - - if self.timeout is None: - continue - elif self.timeout == 0: - raise StopIteration - elif (time.time() - start_time) > self.timeout: - raise RuntimeError( - f"No frame file {fname} after {self.timeout}s" - ) - - self.t0 += self.length - return fname diff --git a/libs/infer/deepclean/infer/load.py b/libs/infer/deepclean/infer/load.py deleted file mode 100644 index 95b1af2..0000000 --- a/libs/infer/deepclean/infer/load.py +++ /dev/null @@ -1,78 +0,0 @@ -import logging -from typing import Iterable, Union - -import numpy as np -from gwpy.timeseries import TimeSeries, TimeSeriesDict - - -def load_frame( - fname: str, channels: Union[str, Iterable[str]], sample_rate: float -) -> np.ndarray: - """Load the indicated channels from the indicated frame file""" - - if isinstance(channels, str): - # if we don't have multiple channels, then just grab the data - data = TimeSeries.read(fname, channels) - data = data.resample(sample_rate) - data = data.value - else: - # otherwise stack the arrays - data = TimeSeriesDict.read(fname, channels) - data = data.resample(sample_rate) - data = np.stack([data[i].value for i in channels]) - - # return as the expected type - return data.astype("float32") - - -def frame_iterator( - crawler: Iterable, - channels: Union[str, Iterable[str]], - sample_rate: float, - inference_sampling_rate: float, -) -> np.ndarray: - stride = int(sample_rate / inference_sampling_rate) - - if isinstance(channels, str): - data = np.array([], dtype="float32") - else: - data = np.array([[] for _ in channels], dtype="float32") - slc = np.arange(stride) - - # manually iterate through the frame crawler so - # we can intercept if it's about to run out and - # mark the sequence as completed - fname_it = iter(crawler) - fname = next(fname_it) - - sequence_end = False - while True: - logging.debug(f"Loading frame file {fname}") - frame = load_frame(fname, channels, sample_rate) - data = np.append(data, frame, axis=-1) - - num_steps = data.shape[-1] // stride - for i in range(num_steps): - x = data.take(slc + i * stride, axis=-1) - - # this basically checks if the next step will - # be the last full stride of data we'll be able - # to use. If it is, try loading a frame to see - # if we have any left. - if (i + 2) * stride > data.shape[-1]: - try: - fname = next(fname_it) - except StopIteration: - # we're out of frames _and_ the next - # step will be the last, so indicate - # that the sequence has terminated - sequence_end = True - - yield x, sequence_end - - # if we've run out of frames to load, kill the loop - if sequence_end: - break - - # slough off any already processed data along the time axis - _, data = np.split(data, [(i + 1) * stride], axis=-1) diff --git a/libs/infer/deepclean/infer/write.py b/libs/infer/deepclean/infer/write.py deleted file mode 100644 index e8ce26c..0000000 --- a/libs/infer/deepclean/infer/write.py +++ /dev/null @@ -1,283 +0,0 @@ -import logging -import math -import time -from pathlib import Path -from typing import Callable, Iterator, Optional, Tuple - -import numpy as np -from gwpy.timeseries import TimeSeries - -from deepclean.gwftools.frames import parse_frame_name - - -class FrameWriter: - def __init__( - self, - write_dir: Path, - strain_iter: Iterator, - channel_name: str, - inference_sampling_rate: float, - sample_rate: float, - frame_length: int = 1, - batch_size: float = 1, - postprocessor: Optional[Callable] = None, - memory: float = 10, - look_ahead: float = 0.05, - aggregation_steps: int = 0, - ) -> None: - """DeepClean predictions postprocessor and frame writer - - Asynchronous process for concatenating averaged - responses from a streaming DeepClean model, post- - processing them, subtacting them from the associated - strain data, then writing the cleaned strain to a - frame file. Frame files will be written using the - same filename given to the associated raw strain file. - - Args: - write_dir: - The directory to which to save the cleaned strain frames - channel_name: - The name to give the strain channel in the cleaned frames - step_size: - The number of samples to expect in responses streamed - back from the inference server - inference_sampling_rate: - The rate at which kernels are sampled from the strain - and witness timeseries - memory: - The number of seconds of noise predictions from - _before_ each frame being cleaned to include in-memory - when performing postprocessing. Particularly useful - for the purposes of avoiding filtering edge effects - look_ahead: - The number of seconds of noise predictions from - _after_ each frame being cleaned to wait for before - performing postprocessing. Note that this introduces - extra latency on top of whatever latency is incurred - by aggregation on the inference server. - aggregation_steps: - The number of overlapping timesteps over which overlapping - segments are averaged on the inference server - """ - - # make the write directory if it doesn't exist - write_dir.mkdir(parents=True, exist_ok=True) - - # record some of our writing parameters as-is - self.write_dir = write_dir - self.strain_iter = strain_iter - self.sample_rate = sample_rate - self.channel_name = channel_name - - # record some of the parameters of the data, mapping - # from time or frequency units to sample units - self.frame_size = int(sample_rate * frame_length) - self.stride = int(sample_rate // inference_sampling_rate) - self.steps_per_frame = math.ceil(self.frame_size / self.stride) - - self.memory = int(memory * sample_rate) - self.samples_ahead = int(look_ahead * sample_rate) - self.steps_ahead = math.ceil(self.samples_ahead / self.stride) - - self.batch_size = batch_size - self.aggregation_steps = aggregation_steps - self.agg_batches, self.agg_leftover = divmod( - aggregation_steps, batch_size - ) - - # load in our postprocessing pipeline - self.postprocessor = postprocessor - - self._zeros = np.zeros((self.frame_size,), dtype=np.float32) - self._noise = np.array([]) - self._frame_idx = 0 - self._latest_seen = -1 - - self.logger = logging.getLogger("Frame Writer") - - def block(self, i: int, callback: Optional[Callable] = None): - while self._latest_seen < i: - if callback is not None: - callback() - time.sleep(1e-3) - - def validate_response( - self, noise_prediction: np.ndarray, request_id: int - ) -> Tuple[np.ndarray, int]: - """ - Parse the response from the server to get - the noise prediction and the corresponding - request id. Ensure that the request id falls - in the expected order and if not, log the ids - that we missed. - - Returns: - The numpy array of the server response - """ - self.logger.debug(f"Received response for package {request_id}") - - # flatten out to 1D and verify that this - # output aligns with our expectations - x = noise_prediction.reshape(-1) - if len(x) != (self.stride * self.batch_size): - raise ValueError( - "Noise prediction is of wrong length {}, " - "expected length {}".format(len(x), self.stride) - ) - - # now make sure that this request is arriving in order - if request_id < self._latest_seen: - self.logger.warning(f"Request id {request_id} came in late") - else: - if request_id > (self._latest_seen + 1): - # if we skipped some requests, log the ones we missed - for i in range(1, request_id - self._latest_seen): - idx = self._latest_seen + i - self.logger.warning(f"No response for request id {idx}") - - # update `self._latest_seen` to reflect whatever - # the new latest request id is - self._latest_seen = request_id - - if request_id < self.agg_batches: - # throw away the first `aggregation_steps` responses, since - # these technically corresponds to predictions from the _past_. - # Return `None`s so that we know not to update our internal arrays - self.logger.debug( - f"Throwing away received package id {request_id}" - ) - return None - elif request_id == self.agg_batches: - # if our batch size doesn't evenly divide the number - # of aggregation steps, slice off any steps in this - # batch of responses that are aggregation steps - x = x[self.agg_leftover * self.stride :] - - # otherwise return the parsed array and its request id - return x - - def update_prediction_array(self, x: np.ndarray, request_id: int) -> int: - """ - Update our initialized `_noise` array with the prediction - `x` by inserting it at the appropriate location. Update - our `_mask` array to indicate that this stretch has been - appropriately filled. - """ - - # use the package request id to figure out where - # in the blank noise array we need to insert - # this prediction. Subtract the steps that we threw away - total_steps = request_id * self.batch_size - step_idx = total_steps - self.aggregation_steps - start_idx = 0 if step_idx < 0 else int(step_idx * self.stride) - - # If we've sloughed off any past data so far, - # make sure to account for that - frame_idx = self._frame_idx * self.frame_size - start_idx -= max(frame_idx - self.memory, 0) - - # now make sure that we have data to fill out, - # otherwise extend the array - if (start_idx + len(x)) > len(self._noise): - self._noise = np.append(self._noise, self._zeros) - - # now insert the response into the existing array - # TODO: should we check that this is all 0s to - # double check ourselves here? - self._noise[start_idx : start_idx + len(x)] = x - - # return the number of steps that have been completed - return step_idx + self.batch_size - - def clean(self, noise: np.ndarray) -> Tuple[Path, float]: - """ - Grab a new strain file, use the provided timeseries of - noise predictions to clean it, then write the cleaned - strain to a .gwf file. Return the name of this file as - well as the time delta in seconds between when the - strain file was written and when the cleaned strain file - completes writing for profiling purposes. - - Args: - noise: - The timeseries of DeepClean noise predictions, - including any past and future data needed for - postprocessing before subtraction. - Returns: - The name of the file the cleaned strain was written to - """ - - # apply any postprocessing - # to the padded noise channel, - # then slice out current frame - if self.postprocessor is not None: - noise = self.postprocessor(noise) - offset = -self.samples_ahead - noise_segment = noise[offset - self.frame_size : offset] - - # now grab the next strain segment - # and subtract this noise from it - strain, fname = next(self.strain_iter) - strain = strain - noise_segment - - # create a timeseries from the cleaned strain that - # we can write to a .gwf. Make sure to give it - # the same timestamp as the original file - timestamp = parse_frame_name(fname.name)[1] - timeseries = TimeSeries( - strain, - t0=timestamp, - sample_rate=self.sample_rate, - channel=self.channel_name + "-CLEANED", - ) - - # write the timeseries to our `write_dir`, - # measure the latency from when the witness - # file became available and when we wrote the - # frame file for profiling purposes - write_path = self.write_dir / fname.name - timeseries.write(write_path) - - # if moving this frame into our memory will - # give us more than `memory` samples, then - # slough off a frame from the start of our - # _noises and _mask arrays - extra = len(noise) - self.samples_ahead - self.memory - if extra > 0: - self._noise = self._noise[extra:] - return write_path - - def __call__(self, noise_prediction: np.ndarray, request_id: int, *arg): - # get the server response and corresponding - # request id from the passed package - noise_prediction = self.validate_response(noise_prediction, request_id) - if noise_prediction is None: - return - - # now insert it into our `_noises` prediction - # array and update our `_mask` to reflect this - step_idx = self.update_prediction_array(noise_prediction, request_id) - - # modulate the total number of cleaning steps taken by the - # number of steps in each frame to see if the current - # cleanable index is past the end of the next frame - div, rem = divmod(step_idx, self.steps_per_frame) - if div <= self._frame_idx: - return None - elif rem >= self.steps_ahead or div > self._frame_idx + 1: - # slice out the data corresponding to current - # frame, first figuring out if we have a full - # memory to use or not - frame_idx = self._frame_idx * self.frame_size - idx = min(self.memory, frame_idx) - idx += self.frame_size + self.samples_ahead - noise = self._noise[:idx] - - fname = self.clean(noise) - self.logger.info(f"Wrote cleaned frame {fname}") - - self._frame_idx += 1 - return fname - else: - return None diff --git a/libs/infer/poetry.lock b/libs/infer/poetry.lock index c769a7b..85e580b 100644 --- a/libs/infer/poetry.lock +++ b/libs/infer/poetry.lock @@ -1,59 +1,3 @@ -[[package]] -name = "aiohttp" -version = "3.8.3" -description = "Async http client/server framework (asyncio)" -category = "main" -optional = false -python-versions = ">=3.6" - -[package.dependencies] -aiosignal = ">=1.1.2" -async-timeout = ">=4.0.0a3,<5.0" -attrs = ">=17.3.0" -charset-normalizer = ">=2.0,<3.0" -frozenlist = ">=1.1.1" -multidict = ">=4.5,<7.0" -yarl = ">=1.0,<2.0" - -[package.extras] -speedups = ["aiodns", "brotli", "cchardet"] - -[[package]] -name = "aiosignal" -version = "1.2.0" -description = "aiosignal: a list of registered asynchronous callbacks" -category = "main" -optional = false -python-versions = ">=3.6" - -[package.dependencies] -frozenlist = ">=1.1.0" - -[[package]] -name = "astropy" -version = "4.3.1" -description = "Astronomy and astrophysics core library" -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -numpy = ">=1.17" -pyerfa = ">=1.7.3" - -[package.extras] -all = ["scipy (>=1.1)", "certifi", "dask", "h5py", "beautifulsoup4", "html5lib", "bleach", "PyYAML (>=3.13)", "pandas", "sortedcontainers", "pytz", "jplephem", "matplotlib (>=3.0,!=3.4.0)", "mpmath", "asdf (>=2.6)", "bottleneck", "ipython (>=4.2)", "pytest"] -docs = ["sphinx (<4)", "sphinx-astropy (>=1.3)", "pytest", "PyYAML (>=3.13)", "scipy (>=1.1)", "matplotlib (>=3.1,!=3.4.0)", "sphinx-changelog (>=1.1.0)", "packaging"] -test = ["pytest-astropy (>=0.8)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)", "packaging"] - -[[package]] -name = "async-timeout" -version = "4.0.2" -description = "Timeout context manager for asyncio programs" -category = "main" -optional = false -python-versions = ">=3.6" - [[package]] name = "atomicwrites" version = "1.4.1" @@ -64,227 +8,66 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "attrs" -version = "22.1.0" +version = "22.2.0" description = "Classes Without Boilerplate" -category = "main" -optional = false -python-versions = ">=3.5" - -[package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "mypy (>=0.900,!=0.940)", "pytest-mypy-plugins", "cloudpickle"] - -[[package]] -name = "brotli" -version = "1.0.9" -description = "Python bindings for the Brotli compression library" -category = "main" -optional = false -python-versions = "*" - -[[package]] -name = "certifi" -version = "2022.9.24" -description = "Python package for providing Mozilla's CA Bundle." -category = "main" +category = "dev" optional = false python-versions = ">=3.6" -[[package]] -name = "cffi" -version = "1.15.1" -description = "Foreign Function Interface for Python calling C code." -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -pycparser = "*" - -[[package]] -name = "charset-normalizer" -version = "2.1.1" -description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." -category = "main" -optional = false -python-versions = ">=3.6.0" - [package.extras] -unicode_backport = ["unicodedata2"] +cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] +tests = ["attrs[tests-no-zope]", "zope.interface"] +tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] [[package]] name = "colorama" -version = "0.4.5" +version = "0.4.6" description = "Cross-platform colored terminal text." category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" - -[[package]] -name = "deepclean.base" -version = "0.0.1" -description = "Dummy base library for constraining versions" -category = "main" -optional = false -python-versions = "^3.8,<3.11" -develop = true - -[package.dependencies] -astropy = "<5.0" -numpy = "<1.23" -packaging = "<21.0" -scipy = "<1.8" - -[package.source] -type = "directory" -url = "../base" - -[[package]] -name = "deepclean.gwftools" -version = "0.0.1" -description = "Utilites for managing gwf file conventions" -category = "main" -optional = false -python-versions = "^3.8,<3.11" -develop = true - -[package.dependencies] -"deepclean.base" = {path = "../base", develop = true} - -[package.source] -type = "directory" -url = "../gwftools" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" [[package]] -name = "deepclean.signal" +name = "deepclean-utils" version = "0.0.1" -description = "DeepClean signal processing utilities" +description = "General deepclean utilities" category = "main" optional = false python-versions = "^3.8,<3.11" develop = true [package.dependencies] -numpy = "^1.21" scipy = "^1.7" [package.source] type = "directory" -url = "../signal" - -[[package]] -name = "frozenlist" -version = "1.3.1" -description = "A list-like structure which implements collections.abc.MutableSequence" -category = "main" -optional = false -python-versions = ">=3.7" - -[[package]] -name = "gevent" -version = "22.10.1" -description = "Coroutine-based network library" -category = "main" -optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5" - -[package.dependencies] -cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""} -greenlet = {version = ">=1.1.3,<2.0", markers = "platform_python_implementation == \"CPython\""} -setuptools = "*" -"zope.event" = "*" -"zope.interface" = "*" - -[package.extras] -dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] -docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] -monitor = ["psutil (>=5.7.0)"] -recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports.socketpair", "psutil (>=5.7.0)"] -test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports.socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] - -[[package]] -name = "geventhttpclient" -version = "2.0.8" -description = "http client library for gevent" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -brotli = "*" -certifi = "*" -gevent = ">=0.13" -six = "*" - -[[package]] -name = "greenlet" -version = "1.1.3.post0" -description = "Lightweight in-process concurrent programming" -category = "main" -optional = false -python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" - -[package.extras] -docs = ["sphinx"] - -[[package]] -name = "grpcio" -version = "1.41.0" -description = "HTTP/2-based RPC framework" -category = "main" -optional = false -python-versions = "*" - -[package.dependencies] -six = ">=1.5.2" - -[package.extras] -protobuf = ["grpcio-tools (>=1.41.0)"] - -[[package]] -name = "idna" -version = "3.4" -description = "Internationalized Domain Names in Applications (IDNA)" -category = "main" -optional = false -python-versions = ">=3.5" +url = "../utils" [[package]] name = "iniconfig" -version = "1.1.1" -description = "iniconfig: brain-dead simple config-ini parsing" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" category = "dev" optional = false -python-versions = "*" - -[[package]] -name = "multidict" -version = "6.0.2" -description = "multidict implementation" -category = "main" -optional = false python-versions = ">=3.7" [[package]] name = "numpy" -version = "1.22.4" -description = "NumPy is the fundamental package for array computing with Python." +version = "1.24.2" +description = "Fundamental package for array computing in Python" category = "main" optional = false python-versions = ">=3.8" [[package]] name = "packaging" -version = "20.9" +version = "23.0" description = "Core utilities for Python packages" -category = "main" +category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[package.dependencies] -pyparsing = ">=2.0.2" +python-versions = ">=3.7" [[package]] name = "pluggy" @@ -298,14 +81,6 @@ python-versions = ">=3.6" dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] -[[package]] -name = "protobuf" -version = "3.19.6" -description = "Protocol Buffers" -category = "main" -optional = false -python-versions = ">=3.5" - [[package]] name = "py" version = "1.11.0" @@ -314,40 +89,6 @@ category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" -[[package]] -name = "pycparser" -version = "2.21" -description = "C parser in Python" -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[[package]] -name = "pyerfa" -version = "2.0.0.1" -description = "Python bindings for ERFA" -category = "main" -optional = false -python-versions = ">=3.7" - -[package.dependencies] -numpy = ">=1.17" - -[package.extras] -docs = ["sphinx-astropy (>=1.3)"] -test = ["pytest", "pytest-doctestplus (>=0.7)"] - -[[package]] -name = "pyparsing" -version = "3.0.9" -description = "pyparsing module - Classes and methods to define and execute parsing grammars" -category = "main" -optional = false -python-versions = ">=3.6.8" - -[package.extras] -diagrams = ["railroad-diagrams", "jinja2"] - [[package]] name = "pytest" version = "6.2.5" @@ -369,45 +110,21 @@ toml = "*" [package.extras] testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] -[[package]] -name = "python-rapidjson" -version = "1.9" -description = "Python wrapper around rapidjson" -category = "main" -optional = false -python-versions = ">=3.6" - [[package]] name = "scipy" -version = "1.7.3" -description = "SciPy: Scientific Library for Python" +version = "1.10.1" +description = "Fundamental algorithms for scientific computing in Python" category = "main" optional = false -python-versions = ">=3.7,<3.11" +python-versions = "<3.12,>=3.8" [package.dependencies] -numpy = ">=1.16.5,<1.23.0" - -[[package]] -name = "setuptools" -version = "65.5.0" -description = "Easily download, build, install, upgrade, and uninstall Python packages" -category = "main" -optional = false -python-versions = ">=3.7" +numpy = ">=1.19.5,<1.27.0" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "mock", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"] -testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] - 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witness_data_dir: Path, - strain_data_dir: Path, + data_dir: Path, write_dir: Path, # Data args sample_rate: float, - kernel_length: float, + frame_length: float, inference_sampling_rate: float, + batch_size: int, inference_rate: float, channels: ChannelList, freq_low: Union[float, Iterable[float]], @@ -130,20 +131,18 @@ def main( only go to stdout. """ - configure_logging(log_file, verbose) + logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) - # create a frame crawler which will start at either the first - # first or last timestep available in the witness data directory - t0 = 0 if start_first else -1 - crawler = FrameCrawler(witness_data_dir, t0, timeout) - - # now an iterator that will load the filenames produced - # by the crawler and split them into stream-sized chunks - frame_it = frame_iterator( - crawler, channels[1:], sample_rate, inference_sampling_rate + witness_it, strain_it = get_data_generators( + data_dir, + channels, + sample_rate=sample_rate, + inference_sampling_rate=inference_sampling_rate, + batch_size=batch_size, + start_first=start_first, + timeout=timeout ) - # now create a file writer which takes the # responses returned by the server and turns them # into a timeseries of noise predictions which can @@ -154,18 +153,32 @@ def main( aggregation_steps = 0 else: aggregation_steps = int(max_latency * inference_sampling_rate) - writer = FrameWriter( - strain_data_dir, - write_dir, - channel_name=channels[0], - inference_sampling_rate=inference_sampling_rate, - sample_rate=sample_rate, - t0=crawler.t0, - postprocessor=BandpassFilter(freq_low, freq_high, sample_rate), - memory=memory, - look_ahead=look_ahead, - aggregation_steps=aggregation_steps, + + states = {} + for model in ["production", "canary"]: + # TODO: infer this from server + name = f"aggregation-{model}-output_stream" + state = State( + model, + frame_length, + memory=memory, + filter_pad=look_ahead, + sample_rate=sample_rate, + inference_sampling_rate=inference_sampling_rate, + batch_size=batch_size, + aggregation_steps=aggregation_steps + ) + states[name] = state + + cleaner = Cleaner( + frame_length, + sample_rate, + filter_pad=look_ahead, + freq_low=freq_low, + freq_high=freq_high ) + writer = Writer(write_dir, strain_it, cleaner) + callback = Callback(writer, **states) # finally create an inference client which will stream # the updates produced by the frame iterator to the @@ -175,13 +188,13 @@ def main( address=url, model_name=model_name, model_version=model_version, - callback=writer, + callback=callback, ) # start a streaming connection to the server that passes # server responses to the writer in a callback thread with client: - for request_id, (x, sequence_end) in enumerate(frame_it): + for request_id, (x, sequence_end) in enumerate(witness_it): client.infer( x, request_id, @@ -189,6 +202,8 @@ def main( sequence_start=request_id == 0, sequence_end=sequence_end, ) + if request_id == 0: + callback.block() time.sleep(0.95 / inference_rate) # check if any files have been written or if the diff --git a/projects/microservice/cleaner/cleaner/__init__.py b/projects/microservice/cleaner/cleaner/cleaner/__init__.py similarity index 100% rename from projects/microservice/cleaner/cleaner/__init__.py rename to projects/microservice/cleaner/cleaner/cleaner/__init__.py diff --git a/projects/microservice/cleaner/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner/cleaner.py new file mode 100644 index 0000000..400f0ff --- /dev/null +++ b/projects/microservice/cleaner/cleaner/cleaner/cleaner.py @@ -0,0 +1,218 @@ +import logging +import time +from pathlib import Path +from typing import Iterable, Optional, Union + +from cleaner.dataloader import get_data_generators +from cleaner.writer import Writer + +from deepclean.infer.callback import Callback, State +from deepclean.infer.clean import Cleaner +from deepclean.utils.channels import ChannelList, get_channels +from deepclean.logging import logger +from hermes.aeriel.client import InferenceClient +from hermes.typeo import typeo + + +@typeo +def main( + # IO args + data_dir: Path, + write_dir: Path, + # Data args + sample_rate: float, + frame_length: float, + inference_sampling_rate: float, + batch_size: int, + inference_rate: float, + channels: ChannelList, + freq_low: Union[float, Iterable[float]], + freq_high: Union[float, Iterable[float]], + # Triton args + url: str, + model_name: str, + model_version: int = -1, + sequence_id: int = 1001, + # Misc args + max_latency: Optional[float] = None, + start_first: bool = False, + timeout: Optional[float] = None, + memory: float = 10, + look_ahead: float = 0.5, + verbose: bool = False, + log_file: Optional[str] = None, +) -> None: + """ + Iterate through directories of witness and strain + data and produced DeepClean-ed frames of strain data + from them. Directories will be traversed in chronological + order, using the filenames to infer the timestamps of + each frame file. + + For true replay streams, directories + will be monitored for new files, eventually raising an error + after `timeout` seconds if no new files are created in the + directory and `timeout` is not `None`. + + Args: + witness_data_dir: + Directory containing frames of witness channels + strain_data_dir: + Directory containing frames of corresponding + strain channel data + write_dir: Directory to which to write cleaned frame files + sample_rate: + The rate at which to sample witness and strain data, in Hz + kernel_length: + The length of the input kernel passed to DeepClean in seconds + inference_sampling_rate: + The rate at which to sample kernels from the witness + and strain timeseries, in Hz. Must be less than or + equal to `sample_rate`. + inference_rate: + The maximum rate at which to send inference requests + to the server in Hz + channels: + Either a list of channels to leverage, with the strain + channel first, or a path to a file containing such a + list separated by newlines. + freq_low: + The low-end of each frequency band over which to + bandpass filter the noise estimates before subtracting + them from the strain channel, in Hz. If only a single + float is specified, this will be used as the low-end of a + single frequency band. If multiple are specified, they + must be sorted in ascending order and not overlap with + any other bands. + freq_high: + The high-end of each frequency band over which to + bandpass filter the noise estimates before subtracting + them from the strain channel, in Hz. If only a single + float is specified, this will be used as the high-end of + a single frequency band. The number of bands must match + those specified in `freq_low`, and the same restrictions + around ordering apply as well. + url: + Address at which server is accepting gRPC inference requests + model_name: + The name of the model to request for inference + model_version: + The version of the model to request for inference + sequence_id: + The ID to assign to this sequence of inference requests + to maintain a streaming state on the server + max_latency: + The maximum amount of latency that server-side aggregation + can introduce into the processing stream. I.e., the number + of aggregation steps will be + `floor(max_latency * inference_sampling_rate)`. If left + as `None`, it will be assumed that the server is performing + no aggregation. + start_first: + Whether to start with the first timestamped frame in the + witness and strain data directories, or the last. The latter + is useful for true streaming replays where getting a good + estimate of latency is important. + timeout: + Once the witness directory has been traversed, this specifies + the amount of time to wait for a new file to be generated + before raising an error. If left as `None`, the process will + wait indefinitely. + memory: + The amount of past data to keep in memory for postprocessing + the current frame. Useful for avoiding filtering edge effects. + look_ahead: + The amount of future data required for postprocessing the + current frame. Useful for avoiding filtering edge effects. + verbose: + Flag indicating whether to log at DEBUG or INFO levels + log_file: + Path to a file to write logs to. If left as `None`, logs will + only go to stdout. + """ + + logger.set_logger("DeepClean infer", log_file, verbose) + channels = get_channels(channels) + + witness_it, strain_it = get_data_generators( + data_dir, + channels, + sample_rate=sample_rate, + inference_sampling_rate=inference_sampling_rate, + batch_size=batch_size, + start_first=start_first, + timeout=timeout + ) + # now create a file writer which takes the + # responses returned by the server and turns them + # into a timeseries of noise predictions which can + # be subtracted from strain data. Use the initial + # timestamp utilized by the crawler to crawl through + # the strain data directory in the same order + if max_latency is None: + aggregation_steps = 0 + else: + aggregation_steps = int(max_latency * inference_sampling_rate) + + states = {} + for model in ["production", "canary"]: + # TODO: infer this from server + name = f"aggregation-{model}-output_stream" + state = State( + model, + frame_length, + memory=memory, + filter_pad=look_ahead, + sample_rate=sample_rate, + inference_sampling_rate=inference_sampling_rate, + batch_size=batch_size, + aggregation_steps=aggregation_steps + ) + states[name] = state + + cleaner = Cleaner( + frame_length, + sample_rate, + filter_pad=look_ahead, + freq_low=freq_low, + freq_high=freq_high + ) + writer = Writer(write_dir, strain_it, cleaner) + callback = Callback(writer, **states) + + # finally create an inference client which will stream + # the updates produced by the frame iterator to the + # inference server and pass the responses to the writer + # as a callback in a separate thread + client = InferenceClient( + address=url, + model_name=model_name, + model_version=model_version, + callback=callback, + ) + + # start a streaming connection to the server that passes + # server responses to the writer in a callback thread + with client: + for request_id, (x, sequence_end) in enumerate(witness_it): + client.infer( + x, + request_id, + sequence_id, + sequence_start=request_id == 0, + sequence_end=sequence_end, + ) + if request_id == 0: + callback.block() + time.sleep(0.95 / inference_rate) + + # check if any files have been written or if the + # client's callback thread has raised any exceptions + response = client.get() + if response is not None: + fname, latency = response + logging.info(f"Wrote file {fname} with latency {latency}s") + + +if __name__ == "__main__": + main() diff --git a/projects/microservice/cleaner/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/cleaner/dataloader.py new file mode 100644 index 0000000..fa45d7c --- /dev/null +++ b/projects/microservice/cleaner/cleaner/cleaner/dataloader.py @@ -0,0 +1,73 @@ +import time +from pathlib import Path +from queue import Empty, Queue +from typing import Iterable, List + +import numpy as np +from microservice.frames import FrameCrawler, load_frame + +from deepclean.logging import logger + + +def witness_iterator(it: Iterable, stride: int) -> np.ndarray: + try: + data = next(it) + except StopIteration: + raise ValueError("Frame crawler never returned any data") + + sequence_end, idx = False, 0 + while not sequence_end: + start = idx * stride + stop = (idx + 1) * stride + if (idx + 2) * stride > data.shape[-1]: + try: + frame = next(it) + except StopIteration: + sequence_end = True + else: + data = data[:, start:] + data = np.concatenate([data, frame], axis=-1) + idx, start, stop = 0, 0, stride + yield data[:, start: stop], sequence_end + + +def strain_iterator(q: Queue): + while True: + try: + response = q.get_nowait() + except Empty: + time.sleep(1e-3) + else: + if response is None: + break + yield response + + +def frame_iter(crawler, channels, sample_rate, q): + for fname in crawler: + logger.debug(f"Loading frame file {fname}") + frame = load_frame(fname, channels, sample_rate) + strain, witnesses = np.split(frame, [1], axis=0) + q.put((strain, fname)) + yield witnesses + q.put(None) + + +def get_data_generators( + witness_data_dir: Path, + channels: List[str], + sample_rate: float, + inference_sampling_rate: float, + batch_size: int, + start_first: bool = True, + timeout: float = 10 +): + t0 = 0 if start_first else -1 + crawler = FrameCrawler(witness_data_dir, t0, timeout) + strain_q = Queue() + it = frame_iter(crawler, channels, sample_rate, strain_q) + + stride = int(batch_size * sample_rate / inference_sampling_rate) + witness_it = witness_iterator(it, stride) + strain_it = strain_iterator(strain_q) + return witness_it, strain_it diff --git a/projects/microservice/cleaner/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/cleaner/writer.py new file mode 100644 index 0000000..71abc76 --- /dev/null +++ b/projects/microservice/cleaner/cleaner/cleaner/writer.py @@ -0,0 +1,36 @@ +from dataclasses import dataclass +from pathlib import Path +from typing import Callable, Iterator + +from gwpy.timeseries import TimeSeries, TimeSeriesDict +from microservice.frames import parse_frame_name + + +@dataclass +class Writer: + write_dir: Path + it: Iterator + cleaner: Callable + sample_rate: float + + def __post_init__(self): + self.write_dir.mkdir(parents=True, exist_ok=True) + + def __call__(self, **predictions): + strain, fname = next(self.it) + timestamp = parse_frame_name(fname.name)[1] + + tsd = TimeSeriesDict() + for key, noise in predictions.items(): + frame = self.cleaner(noise, strain.value) + + model = key.split("-")[1] + postfix = "DEEPCLEAN-" + model.upper() + tsd[key] = TimeSeries( + frame, + t0=timestamp, + sample_rate=self.sample_rate, + channel=strain.channel + "-" + postfix + ) + tsd.write(self.write_dir / fname) + return self.write_dir / fname \ No newline at end of file diff --git a/projects/microservice/cleaner/pyproject.toml b/projects/microservice/cleaner/pyproject.toml index 4f30b5a..dcf0343 100644 --- a/projects/microservice/cleaner/pyproject.toml +++ b/projects/microservice/cleaner/pyproject.toml @@ -6,22 +6,23 @@ authors = ["Alec Gunny "] # readme = "README.md" [tool.poetry.scripts] -clean = "cleaner:main" +clean = "cleaner.cleaner:main" [tool.poetry.dependencies] python = "^3.8,<3.11" -"hermes.typeo" = "^0.1.5" +typeo = {path = "../../../typeo", develop = true} "deepclean.logging" = {path = "../../../libs/logging", develop = true} "deepclean.infer" = {path = "../../../libs/infer", develop = true} +"deepclean.utils" = {path = "../../../libs/utils", develop = true} [tool.poetry.dependencies."hermes.aeriel"] path = "../../../hermes/hermes/hermes.aeriel" develop = true [tool.poetry.dev-dependencies] -pytest = "^6.2" +pytest = "^7.0" [build-system] -requires = ["poetry>=1.2"] -build-backend = "poetry.masonry.api" +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index 6596654..2dd4630 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -4,6 +4,7 @@ from typing import Optional import torch +import tritonclient.grpc.model_config_pb2 as model_config from ml4gw.transforms import ChannelWiseScaler from typeo.parser import make_parser @@ -84,6 +85,7 @@ def __post_init__(self): input_shapes={"witnesses": input_shape}, output_names=["normalized"] ) + self.set_version_policy(preproc) # now create a snapshotter instance # for this preprocessor, mapped to @@ -115,6 +117,7 @@ def __post_init__(self): input_shapes={"normalized": input_shape}, output_names=["noise_prediction"] ) + self.set_version_policy(self.repo.models["deepclean"]) keys = {"canary": -1, "production": 1} for key, version in keys.items(): @@ -135,6 +138,7 @@ def __post_init__(self): input_shapes={"noise_prediction": output_shape}, output_names=["unscaled"] ) + self.set_version_policy(postproc) for key, version in keys.items(): ensemble.pipe( self.repo.models["deepclean"].outputs["noise_prediction"], @@ -155,13 +159,13 @@ def __post_init__(self): ) ensemble.export_version(None) - def update_ensemble_versions(self, ensemble, version): - for step in ensemble.config.ensemble_scheduling.step: - if step.model_version == -1: - continue - if step.model_name not in ["snapshotter", "aggregator"]: - step.model_version = version - ensemble.config.write() + def set_version_policy(self, model: qv.Model): + model.config.version_policy.MergeFrom( + model_config.ModelVersionPolicy( + latest=model_config.ModelVersionPolicy.Latest(num_versions=2) + ) + ) + model.config.write() @property def preprocessor(self): @@ -175,12 +179,16 @@ def postprocessor(self): def deepclean(self): return self.model.deepclean - def export_version(self, train_dir: Path): - weights_path = train_dir / "weights.pt" - - state_dict = torch.load(weights_path) - self.deepclean.load_state_dict(state_dict) + def update_ensemble_versions(self, version): + ensemble = self.repo.models["deepclean-stream"] + for step in ensemble.config.steps: + if step.model_version == -1: + continue + if step.model_name not in ["snapshotter", "aggregator"]: + step.model_version = version + ensemble.config.write() + def export(self): self.repo.models["preprocessor"].export_version(self.preprocessor) self.repo.models["postprocessor"].export_version(self.postprocessor) @@ -191,9 +199,14 @@ def export_version(self, train_dir: Path): self.deepclean, **kwargs ) version = int(Path(export_path).parent.name) - self.update_ensemble_versions( - self.repo.models["deepclean-stream-canary"], version - ) + return version + + def export_weights(self, train_dir: Path): + weights_path = train_dir / "weights.pt" + + state_dict = torch.load(weights_path) + self.deepclean.load_state_dict(state_dict) + version = self.export() _, start, stop = train_dir.name.split("-") self.deepclean.config.parameters["versions"] += "{}={}-{},".format( From 613e15931483633b3479cc030f1fe2385d4dddc7 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 5 Apr 2023 13:47:02 -0500 Subject: [PATCH 12/31] getting inference app working --- libs/infer/deepclean/infer/callback.py | 38 +- libs/infer/deepclean/infer/clean.py | 6 +- libs/logging/deepclean/logging.py | 63 +- .../cleaner/cleaner/{cleaner => }/__init__.py | 0 .../microservice/cleaner/cleaner/cleaner.py | 11 +- .../cleaner/cleaner/cleaner/cleaner.py | 218 --- .../cleaner/{cleaner => }/dataloader.py | 6 + .../cleaner/cleaner/{cleaner => }/writer.py | 22 +- projects/microservice/cleaner/poetry.lock | 1733 +++++++++++------ projects/microservice/cleaner/pyproject.toml | 1 + .../exporter/exporter/__init__.py | 1 + .../exporter/exporter/exporter.py | 42 +- .../microservice/exporter/exporter/main.py | 13 +- projects/microservice/pyproject.toml | 77 +- .../microservice/utils/microservice/frames.py | 2 +- 15 files changed, 1322 insertions(+), 911 deletions(-) rename projects/microservice/cleaner/cleaner/{cleaner => }/__init__.py (100%) delete mode 100644 projects/microservice/cleaner/cleaner/cleaner/cleaner.py rename projects/microservice/cleaner/cleaner/{cleaner => }/dataloader.py (92%) rename projects/microservice/cleaner/cleaner/{cleaner => }/writer.py (61%) create mode 100644 projects/microservice/exporter/exporter/__init__.py diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index 9b12fbc..e62defc 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -1,10 +1,11 @@ -import logging import math import time from typing import Callable, Optional import numpy as np +from deepclean.logging import logger + class State: def __init__( @@ -32,9 +33,12 @@ def __init__( aggregation_steps, batch_size ) + self._zeros = np.zeros((self.frame_size,), dtype=np.float32) + self._state = np.zeros((0,), dtype=np.float32) + self._frame_idx = 0 - self._latest_seen = 0 - self.logger = logging.getLogger(f"Output state {name}") + self._latest_seen = -1 + self.logger = logger.get_logger(f"Output state {name}") def validate(self, response, request_id): # flatten out to 1D and verify that this @@ -96,13 +100,13 @@ def update(self, x, request_id): # now make sure that we have data to fill out, # otherwise extend the array - if (start_idx + len(x)) > len(self._noise): - self._noise = np.append(self._noise, self._zeros) + if (start_idx + len(x)) > len(self._state): + self._state = np.append(self._state, self._zeros) # now insert the response into the existing array # TODO: should we check that this is all 0s to # double check ourselves here? - self._noise[start_idx : start_idx + len(x)] = x + self._state[start_idx : start_idx + len(x)] = x # return the number of steps that have been completed step_idx = step_idx + self.batch_size @@ -112,17 +116,17 @@ def update(self, x, request_id): # cleanable index is past the end of the next frame div, rem = divmod(step_idx, self.steps_per_frame) if ( - (div == self._frame_idx and rem >= self.steps_ahead) - or div > self._frame_idx + (div == (self._frame_idx + 1) and rem >= self.steps_ahead) + or div > self._frame_idx + 1 ): frame_idx = self._frame_idx * self.frame_size idx = min(self.memory, frame_idx) idx += self.frame_size + self.samples_ahead - noise = self._noise[:idx] + noise = self._state[:idx] extra = len(noise) - self.samples_ahead - self.memory if extra > 0: - self._noise = self._noise[extra:] + self._state = self._state[extra:] self._frame_idx += 1 return noise return None @@ -130,14 +134,19 @@ def update(self, x, request_id): class Callback: def __init__( - self, postprocessor: Optional[Callable] = None, **states: State + self, postprocessor: Callable, **states: State ): self.postprocessor = postprocessor self.states = states + self.logger = logger.get_logger("Infer callback") + self.logger.debug("In callback") - def block(self, i: int = 1): + def block(self, i: int = 0, callback: Optional[Callable] = None): while any([state._latest_seen < i for state in self.states.values()]): - time.sleep(1e-3) + if callback is not None: + response = callback() + if response is not None: + return response def __call__(self, responses: np.ndarray, request_id: int, *arg): predictions = {} @@ -147,6 +156,7 @@ def __call__(self, responses: np.ndarray, request_id: int, *arg): if prediction is not None: predictions[state_name] = prediction - if predictions and self.postprocessor is not None: + if predictions: + self.logger.debug(f"Cleaning frame {state._frame_idx - 1}") return self.postprocessor(**predictions) return None diff --git a/libs/infer/deepclean/infer/clean.py b/libs/infer/deepclean/infer/clean.py index b78f941..d4d9601 100644 --- a/libs/infer/deepclean/infer/clean.py +++ b/libs/infer/deepclean/infer/clean.py @@ -3,7 +3,7 @@ import numpy as np from scipy.signal import windows -from deepclean.utils.filtering import FREQUENCY, BandpassFilter +from deepclean.utils.filtering import Frequency, BandpassFilter @dataclass @@ -11,8 +11,8 @@ class Cleaner: kernel_length: float sample_rate: float filter_pad: float - freq_low: FREQUENCY = None - freq_high: FREQUENCY = None + freq_low: Frequency = None + freq_high: Frequency = None def __post_init__(self): self.bandpass = BandpassFilter( diff --git a/libs/logging/deepclean/logging.py b/libs/logging/deepclean/logging.py index 82600fc..bdfd887 100644 --- a/libs/logging/deepclean/logging.py +++ b/libs/logging/deepclean/logging.py @@ -6,35 +6,66 @@ class Logger: def __init__(self): - self._logger = logging.getLogger() + self._logger = None self._formatter = logging.Formatter( "%(asctime)s - %(name)s - %(levelname)s - %(message)s" ) self._cache = [] - def set_logger( - self, name: str, filename: Optional[str] = None, verbose: bool = False - ) -> None: - self._logger = logging.getLogger(name) - if name in self._cache: - return self._logger - self._cache.append(name) - - level = logging.DEBUG if verbose else logging.INFO - self._logger.setLevel(level) + def configure_logger( + self, + logger, + filename: Optional[str] = None, + verbose: Optional[bool] = None, + add_stdout: bool = False + ): + if verbose is None: + level = self._logger.level + else: + level = logging.DEBUG if verbose else logging.INFO + logger.setLevel(level) - use_stdout = os.getenv("DEEPCLEAN_LOG_STDOUT", "True") - if eval(use_stdout): + if add_stdout: handler = logging.StreamHandler(stream=sys.stdout) handler.setFormatter(self._formatter) handler.setLevel(level) - self._logger.addHandler(handler) + logger.addHandler(handler) + if filename is not None: handler = logging.FileHandler(filename=filename, mode="w") handler.setFormatter(self._formatter) handler.setLevel(level) - self._logger.addHandler(handler) - return self._logger + logger.addHandler(handler) + return logger + + def get_logger( + self, + name: str, + filename: Optional[str] = None, + verbose: Optional[bool] = None, + add_stdout: bool = False + ): + logger = logging.getLogger(name) + if name in self._cache: + return logger + logger = self.configure_logger(logger, filename, verbose, add_stdout) + self._cache.append(name) + return logger + + def set_logger( + self, name: str, filename: Optional[str] = None, verbose: bool = False + ) -> None: + if self._logger is None: + logger = logging.getLogger() + use_stdout = os.getenv("DEEPCLEAN_LOG_STDOUT", "True") + use_stdout = eval(use_stdout) + self.configure_logger(logger, filename, verbose, use_stdout) + + logger = self.get_logger(name, verbose=verbose, add_stdout=False) + else: + logger = self.get_logger(name, filename, verbose) + self._logger = logger + return logger def __getattr__(self, name): logger = object.__getattribute__(self, "_logger") diff --git a/projects/microservice/cleaner/cleaner/cleaner/__init__.py b/projects/microservice/cleaner/cleaner/__init__.py similarity index 100% rename from projects/microservice/cleaner/cleaner/cleaner/__init__.py rename to projects/microservice/cleaner/cleaner/__init__.py diff --git a/projects/microservice/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner.py index ea5557b..5e8d241 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/cleaner.py @@ -1,4 +1,3 @@ -import logging import time from pathlib import Path from typing import Iterable, Optional, Union @@ -40,7 +39,7 @@ def main( memory: float = 10, look_ahead: float = 0.5, verbose: bool = False, - log_file: Optional[str] = None, + log_file: Optional[Path] = None, ) -> None: """ Iterate through directories of witness and strain @@ -131,6 +130,7 @@ def main( only go to stdout. """ + log_file.parent.mkdir(parents=True, exist_ok=True) logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) @@ -157,7 +157,7 @@ def main( states = {} for model in ["production", "canary"]: # TODO: infer this from server - name = f"aggregation-{model}-output_stream" + name = f"aggregator-{model}-output_stream" state = State( model, frame_length, @@ -195,6 +195,7 @@ def main( # server responses to the writer in a callback thread with client: for request_id, (x, sequence_end) in enumerate(witness_it): + logger.debug(f"Sending request {request_id}") client.infer( x, request_id, @@ -203,7 +204,7 @@ def main( sequence_end=sequence_end, ) if request_id == 0: - callback.block() + callback.block(0, client.get) time.sleep(0.95 / inference_rate) # check if any files have been written or if the @@ -211,7 +212,7 @@ def main( response = client.get() if response is not None: fname, latency = response - logging.info(f"Wrote file {fname} with latency {latency}s") + logger.info(f"Wrote file {fname} with latency {latency}s") if __name__ == "__main__": diff --git a/projects/microservice/cleaner/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner/cleaner.py deleted file mode 100644 index 400f0ff..0000000 --- a/projects/microservice/cleaner/cleaner/cleaner/cleaner.py +++ /dev/null @@ -1,218 +0,0 @@ -import logging -import time -from pathlib import Path -from typing import Iterable, Optional, Union - -from cleaner.dataloader import get_data_generators -from cleaner.writer import Writer - -from deepclean.infer.callback import Callback, State -from deepclean.infer.clean import Cleaner -from deepclean.utils.channels import ChannelList, get_channels -from deepclean.logging import logger -from hermes.aeriel.client import InferenceClient -from hermes.typeo import typeo - - -@typeo -def main( - # IO args - data_dir: Path, - write_dir: Path, - # Data args - sample_rate: float, - frame_length: float, - inference_sampling_rate: float, - batch_size: int, - inference_rate: float, - channels: ChannelList, - freq_low: Union[float, Iterable[float]], - freq_high: Union[float, Iterable[float]], - # Triton args - url: str, - model_name: str, - model_version: int = -1, - sequence_id: int = 1001, - # Misc args - max_latency: Optional[float] = None, - start_first: bool = False, - timeout: Optional[float] = None, - memory: float = 10, - look_ahead: float = 0.5, - verbose: bool = False, - log_file: Optional[str] = None, -) -> None: - """ - Iterate through directories of witness and strain - data and produced DeepClean-ed frames of strain data - from them. Directories will be traversed in chronological - order, using the filenames to infer the timestamps of - each frame file. - - For true replay streams, directories - will be monitored for new files, eventually raising an error - after `timeout` seconds if no new files are created in the - directory and `timeout` is not `None`. - - Args: - witness_data_dir: - Directory containing frames of witness channels - strain_data_dir: - Directory containing frames of corresponding - strain channel data - write_dir: Directory to which to write cleaned frame files - sample_rate: - The rate at which to sample witness and strain data, in Hz - kernel_length: - The length of the input kernel passed to DeepClean in seconds - inference_sampling_rate: - The rate at which to sample kernels from the witness - and strain timeseries, in Hz. Must be less than or - equal to `sample_rate`. - inference_rate: - The maximum rate at which to send inference requests - to the server in Hz - channels: - Either a list of channels to leverage, with the strain - channel first, or a path to a file containing such a - list separated by newlines. - freq_low: - The low-end of each frequency band over which to - bandpass filter the noise estimates before subtracting - them from the strain channel, in Hz. If only a single - float is specified, this will be used as the low-end of a - single frequency band. If multiple are specified, they - must be sorted in ascending order and not overlap with - any other bands. - freq_high: - The high-end of each frequency band over which to - bandpass filter the noise estimates before subtracting - them from the strain channel, in Hz. If only a single - float is specified, this will be used as the high-end of - a single frequency band. The number of bands must match - those specified in `freq_low`, and the same restrictions - around ordering apply as well. - url: - Address at which server is accepting gRPC inference requests - model_name: - The name of the model to request for inference - model_version: - The version of the model to request for inference - sequence_id: - The ID to assign to this sequence of inference requests - to maintain a streaming state on the server - max_latency: - The maximum amount of latency that server-side aggregation - can introduce into the processing stream. I.e., the number - of aggregation steps will be - `floor(max_latency * inference_sampling_rate)`. If left - as `None`, it will be assumed that the server is performing - no aggregation. - start_first: - Whether to start with the first timestamped frame in the - witness and strain data directories, or the last. The latter - is useful for true streaming replays where getting a good - estimate of latency is important. - timeout: - Once the witness directory has been traversed, this specifies - the amount of time to wait for a new file to be generated - before raising an error. If left as `None`, the process will - wait indefinitely. - memory: - The amount of past data to keep in memory for postprocessing - the current frame. Useful for avoiding filtering edge effects. - look_ahead: - The amount of future data required for postprocessing the - current frame. Useful for avoiding filtering edge effects. - verbose: - Flag indicating whether to log at DEBUG or INFO levels - log_file: - Path to a file to write logs to. If left as `None`, logs will - only go to stdout. - """ - - logger.set_logger("DeepClean infer", log_file, verbose) - channels = get_channels(channels) - - witness_it, strain_it = get_data_generators( - data_dir, - channels, - sample_rate=sample_rate, - inference_sampling_rate=inference_sampling_rate, - batch_size=batch_size, - start_first=start_first, - timeout=timeout - ) - # now create a file writer which takes the - # responses returned by the server and turns them - # into a timeseries of noise predictions which can - # be subtracted from strain data. Use the initial - # timestamp utilized by the crawler to crawl through - # the strain data directory in the same order - if max_latency is None: - aggregation_steps = 0 - else: - aggregation_steps = int(max_latency * inference_sampling_rate) - - states = {} - for model in ["production", "canary"]: - # TODO: infer this from server - name = f"aggregation-{model}-output_stream" - state = State( - model, - frame_length, - memory=memory, - filter_pad=look_ahead, - sample_rate=sample_rate, - inference_sampling_rate=inference_sampling_rate, - batch_size=batch_size, - aggregation_steps=aggregation_steps - ) - states[name] = state - - cleaner = Cleaner( - frame_length, - sample_rate, - filter_pad=look_ahead, - freq_low=freq_low, - freq_high=freq_high - ) - writer = Writer(write_dir, strain_it, cleaner) - callback = Callback(writer, **states) - - # finally create an inference client which will stream - # the updates produced by the frame iterator to the - # inference server and pass the responses to the writer - # as a callback in a separate thread - client = InferenceClient( - address=url, - model_name=model_name, - model_version=model_version, - callback=callback, - ) - - # start a streaming connection to the server that passes - # server responses to the writer in a callback thread - with client: - for request_id, (x, sequence_end) in enumerate(witness_it): - client.infer( - x, - request_id, - sequence_id, - sequence_start=request_id == 0, - sequence_end=sequence_end, - ) - if request_id == 0: - callback.block() - time.sleep(0.95 / inference_rate) - - # check if any files have been written or if the - # client's callback thread has raised any exceptions - response = client.get() - if response is not None: - fname, latency = response - logging.info(f"Wrote file {fname} with latency {latency}s") - - -if __name__ == "__main__": - main() diff --git a/projects/microservice/cleaner/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py similarity index 92% rename from projects/microservice/cleaner/cleaner/cleaner/dataloader.py rename to projects/microservice/cleaner/cleaner/dataloader.py index fa45d7c..c3e620f 100644 --- a/projects/microservice/cleaner/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -4,6 +4,7 @@ from typing import Iterable, List import numpy as np +from gwpy.timeseries import TimeSeries from microservice.frames import FrameCrawler, load_frame from deepclean.logging import logger @@ -28,7 +29,9 @@ def witness_iterator(it: Iterable, stride: int) -> np.ndarray: data = data[:, start:] data = np.concatenate([data, frame], axis=-1) idx, start, stop = 0, 0, stride + yield data[:, start: stop], sequence_end + idx += 1 def strain_iterator(q: Queue): @@ -48,6 +51,9 @@ def frame_iter(crawler, channels, sample_rate, q): logger.debug(f"Loading frame file {fname}") frame = load_frame(fname, channels, sample_rate) strain, witnesses = np.split(frame, [1], axis=0) + strain = TimeSeries( + strain[0], sample_rate=sample_rate, channel=channels[0] + ) q.put((strain, fname)) yield witnesses q.put(None) diff --git a/projects/microservice/cleaner/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py similarity index 61% rename from projects/microservice/cleaner/cleaner/cleaner/writer.py rename to projects/microservice/cleaner/cleaner/writer.py index 71abc76..1baca11 100644 --- a/projects/microservice/cleaner/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -1,3 +1,5 @@ +import os +import time from dataclasses import dataclass from pathlib import Path from typing import Callable, Iterator @@ -5,20 +7,23 @@ from gwpy.timeseries import TimeSeries, TimeSeriesDict from microservice.frames import parse_frame_name +from deepclean.logging import logger + @dataclass class Writer: write_dir: Path it: Iterator cleaner: Callable - sample_rate: float def __post_init__(self): self.write_dir.mkdir(parents=True, exist_ok=True) + self.logger = logger.get_logger("DeepClean writer") def __call__(self, **predictions): strain, fname = next(self.it) timestamp = parse_frame_name(fname.name)[1] + self.logger.debug(f"Cleaning strain from file {fname}") tsd = TimeSeriesDict() for key, noise in predictions.items(): @@ -29,8 +34,15 @@ def __call__(self, **predictions): tsd[key] = TimeSeries( frame, t0=timestamp, - sample_rate=self.sample_rate, - channel=strain.channel + "-" + postfix + sample_rate=strain.sample_rate.value, + channel=strain.channel.name + "-" + postfix ) - tsd.write(self.write_dir / fname) - return self.write_dir / fname \ No newline at end of file + + write_path = self.write_dir / fname.name + tsd.write(write_path) + + write_time = time.time() + tstamp = os.path.getmtime(fname) + latency = write_time - tstamp + + return write_path, latency diff --git a/projects/microservice/cleaner/poetry.lock b/projects/microservice/cleaner/poetry.lock index 988aa8b..47aec65 100644 --- a/projects/microservice/cleaner/poetry.lock +++ b/projects/microservice/cleaner/poetry.lock @@ -1,24 +1,56 @@ [[package]] -name = "atomicwrites" -version = "1.4.0" -description = "Atomic file writes." -category = "dev" +name = "aiohttp" +version = "3.8.4" +description = "Async http client/server framework (asyncio)" +category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<4.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "cchardet"] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = "aiosignal: a list of registered asynchronous callbacks" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +frozenlist = ">=1.1.0" + +[[package]] +name = "async-timeout" +version = "4.0.2" +description = "Timeout context manager for asyncio programs" +category = "main" +optional = false +python-versions = ">=3.6" [[package]] name = "attrs" -version = "21.4.0" +version = "22.2.0" description = "Classes Without Boilerplate" -category = "dev" +category = "main" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = ">=3.6" [package.extras] -dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "furo", "sphinx", "sphinx-notfound-page", "pre-commit", "cloudpickle"] -docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"] -tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"] +cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] +tests = ["attrs[tests-no-zope]", "zope.interface"] +tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] [[package]] name = "brotli" @@ -30,7 +62,7 @@ python-versions = "*" [[package]] name = "certifi" -version = "2022.6.15" +version = "2022.12.7" description = "Python package for providing Mozilla's CA Bundle." category = "main" optional = false @@ -38,7 +70,7 @@ python-versions = ">=3.6" [[package]] name = "cffi" -version = "1.15.0" +version = "1.15.1" description = "Foreign Function Interface for Python calling C code." category = "main" optional = false @@ -49,38 +81,22 @@ pycparser = "*" [[package]] name = "charset-normalizer" -version = "2.0.12" +version = "3.1.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." category = "main" optional = false -python-versions = ">=3.5.0" - -[package.extras] -unicode_backport = ["unicodedata2"] +python-versions = ">=3.7.0" [[package]] name = "colorama" -version = "0.4.5" +version = "0.4.6" description = "Cross-platform colored terminal text." category = "dev" optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" [[package]] -name = "deepclean.gwftools" -version = "0.0.1" -description = "Utilites for managing gwf file conventions" -category = "main" -optional = false -python-versions = "^3.8,<3.11" -develop = true - -[package.source] -type = "directory" -url = "../../../libs/gwftools" - -[[package]] -name = "deepclean.infer" +name = "deepclean-infer" version = "0.0.1" description = "DeepClean inference utilities" category = "main" @@ -89,18 +105,15 @@ python-versions = "^3.8,<3.11" develop = true [package.dependencies] -"deepclean.gwftools" = {path = "../gwftools", develop = true} -"deepclean.signal" = {path = "../signal", develop = true} -"hermes.stillwater" = {path = "../../hermes/hermes/hermes.stillwater", develop = true} -numpy = "^1.21" -tritonclient = {version = "^2.18.0", extras = ["all"]} +"deepclean.utils" = {path = "../utils", develop = true} +scipy = "^1.7" [package.source] type = "directory" url = "../../../libs/infer" [[package]] -name = "deepclean.logging" +name = "deepclean-logging" version = "0.0.1" description = "Utilities for standardizing logging practices" category = "main" @@ -113,25 +126,43 @@ type = "directory" url = "../../../libs/logging" [[package]] -name = "deepclean.signal" +name = "deepclean-utils" version = "0.0.1" -description = "DeepClean signal processing utilities" +description = "General deepclean utilities" category = "main" optional = false python-versions = "^3.8,<3.11" develop = true [package.dependencies] -numpy = "^1.21" scipy = "^1.7" [package.source] type = "directory" -url = "../../../libs/signal" +url = "../../../libs/utils" + +[[package]] +name = "exceptiongroup" +version = "1.1.1" +description = "Backport of PEP 654 (exception groups)" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.extras] +test = ["pytest (>=6)"] + +[[package]] +name = "frozenlist" +version = "1.3.3" +description = "A list-like structure which implements collections.abc.MutableSequence" +category = "main" +optional = false +python-versions = ">=3.7" [[package]] name = "gevent" -version = "21.12.0" +version = "22.10.2" description = "Coroutine-based network library" category = "main" optional = false @@ -139,7 +170,7 @@ python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5" [package.dependencies] cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""} -greenlet = {version = ">=1.1.0,<2.0", markers = "platform_python_implementation == \"CPython\""} +greenlet = {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\""} setuptools = "*" "zope.event" = "*" "zope.interface" = "*" @@ -148,12 +179,12 @@ setuptools = "*" dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] monitor = ["psutil (>=5.7.0)"] -recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports.socketpair", "psutil (>=5.7.0)"] -test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports.socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] +recommended = ["backports.socketpair", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)", "selectors2"] +test = ["backports.socketpair", "cffi (>=1.12.2)", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "dnspython (>=1.16.0,<2.0)", "futures", "idna", "mock", "objgraph", "psutil (>=5.7.0)", "requests", "selectors2"] [[package]] name = "geventhttpclient" -version = "1.5.4" +version = "2.0.2" description = "http client library for gevent" category = "main" optional = false @@ -167,32 +198,30 @@ six = "*" [[package]] name = "greenlet" -version = "1.1.2" +version = "2.0.2" description = "Lightweight in-process concurrent programming" category = "main" optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" [package.extras] -docs = ["sphinx"] +docs = ["Sphinx", "docutils (<0.18)"] +test = ["objgraph", "psutil"] [[package]] name = "grpcio" -version = "1.41.0" +version = "1.53.0" description = "HTTP/2-based RPC framework" category = "main" optional = false -python-versions = "*" - -[package.dependencies] -six = ">=1.5.2" +python-versions = ">=3.7" [package.extras] -protobuf = ["grpcio-tools (>=1.41.0)"] +protobuf = ["grpcio-tools (>=1.53.0)"] [[package]] -name = "hermes.aeriel" -version = "0.0.1" +name = "hermes-aeriel" +version = "0.1.0" description = "Triton wrapper utilities" category = "main" optional = false @@ -200,80 +229,68 @@ python-versions = ">=3.8,<3.11" develop = true [package.dependencies] -numpy = "<1.22.4" -tritonclient = {version = "^2.18", extras = ["all"]} +tritonclient = {version = "^2.22", extras = ["all"]} [package.extras] -serve = ["spython (>=0.1,<0.2)"] +client = ["numpy (>=1.22,<2.0)"] +serve = ["spython (>=0.2,<0.3)"] [package.source] type = "directory" url = "../../../hermes/hermes/hermes.aeriel" [[package]] -name = "hermes.stillwater" -version = "0.1.0" -description = "Tools for building asynchronous inference pipelines" +name = "idna" +version = "3.4" +description = "Internationalized Domain Names in Applications (IDNA)" category = "main" optional = false -python-versions = ">=3.8,<3.11" -develop = true - -[package.dependencies] -numpy = "<1.22.4" -requests = "^2.26.0" -tblib = "^1.7" -tritonclient = {version = "^2.18", extras = ["all"]} - -[package.source] -type = "directory" -url = "../../../hermes/hermes/hermes.stillwater" +python-versions = ">=3.5" [[package]] -name = "hermes.typeo" -version = "0.1.5" -description = "Utilities for running functions as scripts" -category = "main" +name = "iniconfig" +version = "2.0.0" +description = "brain-dead simple config-ini parsing" +category = "dev" optional = false -python-versions = ">=3.8,<4.0" - -[package.dependencies] -toml = ">=0.10.2,<0.11.0" +python-versions = ">=3.7" [[package]] -name = "idna" -version = "3.3" -description = "Internationalized Domain Names in Applications (IDNA)" +name = "microservice" +version = "0.0.1" +description = "Utilities for DeepClean production deployment" category = "main" optional = false -python-versions = ">=3.5" +python-versions = ">=3.8,<3.11" +develop = true + +[package.source] +type = "directory" +url = "../utils" [[package]] -name = "iniconfig" -version = "1.1.1" -description = "iniconfig: brain-dead simple config-ini parsing" -category = "dev" +name = "multidict" +version = "6.0.4" +description = "multidict implementation" +category = "main" optional = false -python-versions = "*" +python-versions = ">=3.7" [[package]] name = "numpy" -version = "1.22.3" -description = "NumPy is the fundamental package for array computing with Python." +version = "1.24.2" +description = "Fundamental package for array computing in Python" category = "main" optional = false python-versions = ">=3.8" [[package]] name = "packaging" -version = "21.3" +version = "23.0" description = "Core utilities for Python packages" -category = "dev" +category = "main" optional = false -python-versions = ">=3.6" - -[package.dependencies] -pyparsing = ">=2.0.2,<3.0.5 || >3.0.5" +python-versions = ">=3.7" [[package]] name = "pluggy" @@ -289,19 +306,11 @@ testing = ["pytest", "pytest-benchmark"] [[package]] name = "protobuf" -version = "3.19.4" +version = "3.20.3" description = "Protocol Buffers" category = "main" optional = false -python-versions = ">=3.5" - -[[package]] -name = "py" -version = "1.11.0" -description = "library with cross-python path, ini-parsing, io, code, log facilities" -category = "dev" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +python-versions = ">=3.7" [[package]] name = "pycparser" @@ -311,87 +320,62 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" -[[package]] -name = "pyparsing" -version = "3.0.9" -description = "pyparsing module - Classes and methods to define and execute parsing grammars" -category = "dev" -optional = false -python-versions = ">=3.6.8" - -[package.extras] -diagrams = ["railroad-diagrams", "jinja2"] - [[package]] name = "pytest" -version = "6.2.5" +version = "7.2.2" description = "pytest: simple powerful testing with Python" category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] -atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""} attrs = ">=19.2.0" colorama = {version = "*", markers = "sys_platform == \"win32\""} +exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""} iniconfig = "*" packaging = "*" pluggy = ">=0.12,<2.0" -py = ">=1.8.2" -toml = "*" +tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} [package.extras] -testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"] [[package]] name = "python-rapidjson" -version = "1.6" +version = "1.10" description = "Python wrapper around rapidjson" category = "main" optional = false python-versions = ">=3.6" [[package]] -name = "requests" -version = "2.28.0" -description = "Python HTTP for Humans." +name = "scipy" +version = "1.10.1" +description = "Fundamental algorithms for scientific computing in Python" category = "main" optional = false -python-versions = ">=3.7, <4" +python-versions = "<3.12,>=3.8" [package.dependencies] -certifi = ">=2017.4.17" -charset-normalizer = ">=2.0.0,<2.1.0" -idna = ">=2.5,<4" -urllib3 = ">=1.21.1,<1.27" +numpy = ">=1.19.5,<1.27.0" [package.extras] -socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use_chardet_on_py3 = ["chardet (>=3.0.2,<5)"] - -[[package]] -name = "scipy" -version = "1.8.1" -description = "SciPy: Scientific Library for Python" -category = "main" -optional = false -python-versions = ">=3.8,<3.11" - -[package.dependencies] -numpy = ">=1.17.3,<1.25.0" +dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"] +doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] +test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] name = "setuptools" -version = "62.6.0" +version = "67.6.1" description = "Easily download, build, install, upgrade, and uninstall Python packages" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "furo"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-enabler (>=1.0.1)", "pytest-perf", "mock", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)"] -testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] name = "six" @@ -401,14 +385,6 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" -[[package]] -name = "tblib" -version = "1.7.0" -description = "Traceback serialization library." -category = "main" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" - [[package]] name = "toml" version = "0.10.2" @@ -417,42 +393,67 @@ category = "main" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +[[package]] +name = "tomli" +version = "2.0.1" +description = "A lil' TOML parser" +category = "dev" +optional = false +python-versions = ">=3.7" + [[package]] name = "tritonclient" -version = "2.22.4" +version = "2.32.0" description = "Python client library and utilities for communicating with Triton Inference Server" category = "main" optional = false python-versions = "*" [package.dependencies] -geventhttpclient = {version = ">=1.4.4", optional = true, markers = "extra == \"all\""} 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b/projects/microservice/exporter/exporter/exporter.py index 2dd4630..a11e23b 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -7,6 +7,7 @@ import tritonclient.grpc.model_config_pb2 as model_config from ml4gw.transforms import ChannelWiseScaler from typeo.parser import make_parser +from typeo.actions import TypeoTomlAction import hermes.quiver as qv from deepclean.architectures import DeepCleanAE as architecture @@ -29,13 +30,34 @@ class Exporter: instances: Optional[int] = None @classmethod - def from_config(cls, config: Optional[Path] = None): + def from_config( + cls, + config: Optional[Path] = None, + script: Optional[str] = None + ): + args = ["--typeo"] if config is None: - config = Path(__file__).parent.parent / "pyproject.toml" + config = Path(__file__).parent.parent.parent / "pyproject.toml" + if script is None: + script = "exporter" + + args.append(str(config)) + if script is not None: + args.append(f"script={script}") + + typeo_parser = argparse.ArgumentParser() + typeo_parser.add_argument( + "--typeo", + bools={}, + subcommands={}, + nargs="*", + action=TypeoTomlAction + ) + args = typeo_parser.parse_args(args) parser = argparse.ArgumentParser() make_parser(cls, parser) - args = parser.parse_args(["--typeo", config]) + args = parser.parse_args(args.typeo) return cls(**vars(args)) def __post_init__(self): @@ -179,13 +201,21 @@ def postprocessor(self): def deepclean(self): return self.model.deepclean - def update_ensemble_versions(self, version): + def update_ensemble_versions(self, version: Optional[int] = None): ensemble = self.repo.models["deepclean-stream"] for step in ensemble.config.steps: + # don't update the version of the canary model, + # which will always point to the latest version if step.model_version == -1: continue - if step.model_name not in ["snapshotter", "aggregator"]: - step.model_version = version + elif step.model_name in ["snapshotter", "aggregator"]: + # these models don't need to be versioned + # because they don't have any parameters + continue + + if version is None: + version = step.model_version + 1 + step.model_version = version ensemble.config.write() def export(self): diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index 9d99131..1058816 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -1,10 +1,15 @@ from flask import Flask -from export.exporter import Exporter - +from exporter.exporter import Exporter app = Flask(__name__) exporter = Exporter.from_config() -@app.route("/") + +@app.route("/export/") def export_model(train_dir): - exporter.export_version(train_dir) + exporter.export_weights(train_dir) + + +@app.route("/increment") +def increment_ensemble(): + exporter.update_ensemble_version() diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 81fe39c..2972759 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -1,4 +1,8 @@ [tool.typeo.base] +# global paths +log_dir = "${PROJECT_DIR}/logs" + +# global data args channels = [ "H1:GDS-CALIB_STRAIN", "H1:GDS-CALIB_F_S_SQUARED", @@ -46,7 +50,16 @@ channels = [ # "H1:ASC-CSOFT_Y_INMON" # ] sample_rate = 4096 -verbose = true +freq_low = 55 +freq_high = 65 + +# global inference args +inference_sampling_rate = 512 +inference_aggregation_time = 0.5 +inference_batch_size = 256 # TODO: enforce that this is agg_time * inference_sampling_rate + +# global misc args +verbose = false [tool.typeo.scripts.train] # paths @@ -63,8 +76,8 @@ valid_frac = 0.25 # preprocessing parameters kernel_length = 8 kernel_stride = 0.0625 -freq_low = 55 -freq_high = 65 +freq_low = "${base.freq_low}" +freq_high = "${base.freq_high}" filter_order = 8 fftlength = 2 overlap = 1 @@ -88,30 +101,54 @@ verbose = "${base.verbose}" autoencoder = {} -[tool.typeo.scripts.clean] -url = "localhost:8001" -model_name = "deepclean-stream-0.5" -model_version = 1 - -witness_data_dir = "${DATA_DIR}/lldetchar" -strain_data_dir = "${DATA_DIR}/llhoft" -write_dir = "${PROJECT_DIR}/production/data" +[tool.typeo.scripts.exporter] +# paths +repository_directory = "${PROJECT_DIR}/model_repo" +# data args channels = "${base.channels}" kernel_length = 1 -inference_sampling_rate = 512 -inference_rate = 512 sample_rate = "${base.sample_rate}" -max_latency = 0.5 -freq_low = 55 -freq_high = 65 +# infer args +inference_sampling_rate = "${base.inference_sampling_rate}" +batch_size = "${base.inference_batch_size}" +aggregation_time = "${base.inference_aggregation_time}" + +# triton args +streams_per_gpu = 2 +platform = "tensorrt_plan" +instances = 4 + + +[tool.typeo.scripts.clean] +# paths +data_dir = "${DATA_DIR}/H1" +write_dir = "${PROJECT_DIR}/frames" + +# data args +sample_rate = "${base.sample_rate}" +channels = "${base.channels}" +freq_low = "${base.freq_low}" +freq_high = "${base.freq_high}" +frame_length = 1 memory = 10 look_ahead = 0.5 -start_first = true -timeout = 1 +# inference args +inference_sampling_rate = "${base.inference_sampling_rate}" +batch_size = "${base.inference_batch_size}" +inference_rate = 16 +max_latency = "${base.inference_aggregation_time}" + +# triton args +url = "localhost:8001" +model_name = "deepclean-stream" +model_version = -1 sequence_id = 1001 -verbose = false -log_file = "${PROJECT_DIR}/clean.log_" +# misc args +start_first = true +timeout = 3 +verbose = "${base.verbose}" +log_file = "${base.log_dir}/clean.log" diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 9f18bd9..3a86500 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -42,7 +42,7 @@ def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: if match is None: raise ValueError(f"Could not parse frame filename {fname}") - prefix, start, duration, _ = match.groups() + prefix, start, duration, *_ = match.groups() return prefix, int(start), int(duration) From 40117c9cfb199b47e4d38185a1682b05f09c478d Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Thu, 6 Apr 2023 16:18:36 -0700 Subject: [PATCH 13/31] getting cleaning running --- libs/infer/deepclean/infer/callback.py | 13 ++- .../microservice/cleaner/cleaner/cleaner.py | 13 +-- .../cleaner/cleaner/dataloader.py | 28 ++++-- .../microservice/cleaner/cleaner/writer.py | 15 +++- projects/microservice/environment.yaml | 2 +- projects/microservice/pyproject.toml | 89 +++++++++---------- .../microservice/utils/microservice/frames.py | 84 +++++++++++++++-- 7 files changed, 164 insertions(+), 80 deletions(-) diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index e62defc..fcf1984 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -17,7 +17,7 @@ def __init__( sample_rate: float, inference_sampling_rate: float, batch_size: int, - aggregation_steps: int + aggregation_steps: int, ) -> None: self.frame_size = int(sample_rate * frame_length) self.stride = int(sample_rate // inference_sampling_rate) @@ -116,9 +116,8 @@ def update(self, x, request_id): # cleanable index is past the end of the next frame div, rem = divmod(step_idx, self.steps_per_frame) if ( - (div == (self._frame_idx + 1) and rem >= self.steps_ahead) - or div > self._frame_idx + 1 - ): + div == (self._frame_idx + 1) and rem >= self.steps_ahead + ) or div > self._frame_idx + 1: frame_idx = self._frame_idx * self.frame_size idx = min(self.memory, frame_idx) idx += self.frame_size + self.samples_ahead @@ -133,13 +132,10 @@ def update(self, x, request_id): class Callback: - def __init__( - self, postprocessor: Callable, **states: State - ): + def __init__(self, postprocessor: Callable, **states: State): self.postprocessor = postprocessor self.states = states self.logger = logger.get_logger("Infer callback") - self.logger.debug("In callback") def block(self, i: int = 0, callback: Optional[Callable] = None): while any([state._latest_seen < i for state in self.states.values()]): @@ -147,6 +143,7 @@ def block(self, i: int = 0, callback: Optional[Callable] = None): response = callback() if response is not None: return response + time.sleep(1e-3) def __call__(self, responses: np.ndarray, request_id: int, *arg): predictions = {} diff --git a/projects/microservice/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner.py index 5e8d241..43c73ae 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/cleaner.py @@ -4,13 +4,13 @@ from cleaner.dataloader import get_data_generators from cleaner.writer import Writer -from typeo import scriptify from deepclean.infer.callback import Callback, State from deepclean.infer.clean import Cleaner -from deepclean.utils.channels import ChannelList, get_channels from deepclean.logging import logger +from deepclean.utils.channels import ChannelList, get_channels from hermes.aeriel.client import InferenceClient +from typeo import scriptify @scriptify @@ -38,6 +38,7 @@ def main( timeout: Optional[float] = None, memory: float = 10, look_ahead: float = 0.5, + max_files: int = 300, verbose: bool = False, log_file: Optional[Path] = None, ) -> None: @@ -141,7 +142,7 @@ def main( inference_sampling_rate=inference_sampling_rate, batch_size=batch_size, start_first=start_first, - timeout=timeout + timeout=timeout, ) # now create a file writer which takes the # responses returned by the server and turns them @@ -166,7 +167,7 @@ def main( sample_rate=sample_rate, inference_sampling_rate=inference_sampling_rate, batch_size=batch_size, - aggregation_steps=aggregation_steps + aggregation_steps=aggregation_steps, ) states[name] = state @@ -175,9 +176,9 @@ def main( sample_rate, filter_pad=look_ahead, freq_low=freq_low, - freq_high=freq_high + freq_high=freq_high, ) - writer = Writer(write_dir, strain_it, cleaner) + writer = Writer(write_dir, strain_it, cleaner, max_files) callback = Callback(writer, **states) # finally create an inference client which will stream diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index c3e620f..03adf87 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -30,7 +30,7 @@ def witness_iterator(it: Iterable, stride: int) -> np.ndarray: data = np.concatenate([data, frame], axis=-1) idx, start, stop = 0, 0, stride - yield data[:, start: stop], sequence_end + yield data[:, start:stop], sequence_end idx += 1 @@ -49,27 +49,37 @@ def strain_iterator(q: Queue): def frame_iter(crawler, channels, sample_rate, q): for fname in crawler: logger.debug(f"Loading frame file {fname}") - frame = load_frame(fname, channels, sample_rate) - strain, witnesses = np.split(frame, [1], axis=0) - strain = TimeSeries( - strain[0], sample_rate=sample_rate, channel=channels[0] - ) + # frame = load_frame(fname, channels, sample_rate) + # strain, witnesses = np.split(frame, [1], axis=0) + # strain = TimeSeries( + # strain[0], sample_rate=sample_rate, channel=channels[0] + # ) + + witnesses = load_frame(fname, channels[1:], sample_rate) + + strain_fname = fname.name.replace("Detchar", "HOFT") + strain_fname = fname.parent.parent / "llhoft" / strain_fname + strain = TimeSeries.read(strain_fname, channel=channels[0]) + if strain.sample_rate.value != sample_rate: + strain = strain.resample(sample_rate) + q.put((strain, fname)) yield witnesses q.put(None) def get_data_generators( - witness_data_dir: Path, + data_dir: Path, channels: List[str], sample_rate: float, inference_sampling_rate: float, batch_size: int, start_first: bool = True, - timeout: float = 10 + timeout: float = 10, ): t0 = 0 if start_first else -1 - crawler = FrameCrawler(witness_data_dir, t0, timeout) + # crawler = FrameCrawler(data_dir, t0, timeout) + crawler = FrameCrawler(data_dir / "lldetchar", t0, timeout) strain_q = Queue() it = frame_iter(crawler, channels, sample_rate, strain_q) diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index 1baca11..aa9b90c 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -5,7 +5,7 @@ from typing import Callable, Iterator from gwpy.timeseries import TimeSeries, TimeSeriesDict -from microservice.frames import parse_frame_name +from microservice.frames import lock, parse_frame_name from deepclean.logging import logger @@ -15,10 +15,12 @@ class Writer: write_dir: Path it: Iterator cleaner: Callable + max_files: int def __post_init__(self): self.write_dir.mkdir(parents=True, exist_ok=True) self.logger = logger.get_logger("DeepClean writer") + self._files = [] def __call__(self, **predictions): strain, fname = next(self.it) @@ -35,14 +37,21 @@ def __call__(self, **predictions): frame, t0=timestamp, sample_rate=strain.sample_rate.value, - channel=strain.channel.name + "-" + postfix + channel=strain.channel.name + "-" + postfix, ) write_path = self.write_dir / fname.name - tsd.write(write_path) + with lock: + tsd.write(write_path) write_time = time.time() tstamp = os.path.getmtime(fname) latency = write_time - tstamp + self._files.append(write_path) + if len(self._files) > self.max_files: + fname = self._files.pop(0) + self.logger.info(f"Removing file {fname} from replay directory") + fname.unlink() # TODO: write these somewhere else? + return write_path, latency diff --git a/projects/microservice/environment.yaml b/projects/microservice/environment.yaml index 2e60fb8..44f6db2 100644 --- a/projects/microservice/environment.yaml +++ b/projects/microservice/environment.yaml @@ -5,4 +5,4 @@ dependencies: - python=3.10 - gwpy<3.0 - astropy - - python-ldas-tools-framecpp<3.0 + - python-ldas-tools-framecpp=2.6.14 diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 2972759..d62fe0e 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -5,50 +5,49 @@ log_dir = "${PROJECT_DIR}/logs" # global data args channels = [ "H1:GDS-CALIB_STRAIN", - "H1:GDS-CALIB_F_S_SQUARED", - "H1:GDS-CALIB_F_S_SQUARED_NOGATE", - "H1:GDS-CALIB_SRC_Q_INVERSE", - "H1:GDS-CALIB_SRC_Q_INVERSE_NOGATE", - "H1:GDS-CALIB_F_CC", - "H1:GDS-CALIB_F_CC_NOGATE", - "H1:GDS-CALIB_KAPPA_C", - "H1:GDS-CALIB_KAPPA_C_NOGATE", - "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY", - "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY_NOGATE", - "H1:GDS-CALIB_KAPPA_PUM_REAL", - "H1:GDS-CALIB_KAPPA_PUM_REAL_NOGATE", - "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY", - "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY_NOGATE", - "H1:GDS-CALIB_KAPPA_UIM_REAL", - "H1:GDS-CALIB_KAPPA_UIM_REAL_NOGATE", - "H1:GDS-CALIB_KAPPA_TST_IMAGINARY", - "H1:GDS-CALIB_KAPPA_TST_IMAGINARY_NOGATE", - "H1:GDS-CALIB_KAPPA_TST_REAL", - "H1:GDS-CALIB_KAPPA_TST_REAL_NOGATE", - "H1:GDS-CALIB_STATE_VECTOR", +# "H1:GDS-CALIB_F_S_SQUARED", +# "H1:GDS-CALIB_F_S_SQUARED_NOGATE", +# "H1:GDS-CALIB_SRC_Q_INVERSE", +# "H1:GDS-CALIB_SRC_Q_INVERSE_NOGATE", +# "H1:GDS-CALIB_F_CC", +# "H1:GDS-CALIB_F_CC_NOGATE", +# "H1:GDS-CALIB_KAPPA_C", +# "H1:GDS-CALIB_KAPPA_C_NOGATE", +# "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY", +# "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY_NOGATE", +# "H1:GDS-CALIB_KAPPA_PUM_REAL", +# "H1:GDS-CALIB_KAPPA_PUM_REAL_NOGATE", +# "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY", +# "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY_NOGATE", +# "H1:GDS-CALIB_KAPPA_UIM_REAL", +# "H1:GDS-CALIB_KAPPA_UIM_REAL_NOGATE", +# "H1:GDS-CALIB_KAPPA_TST_IMAGINARY", +# "H1:GDS-CALIB_KAPPA_TST_IMAGINARY_NOGATE", +# "H1:GDS-CALIB_KAPPA_TST_REAL", +# "H1:GDS-CALIB_KAPPA_TST_REAL_NOGATE", +# "H1:GDS-CALIB_STATE_VECTOR", + "H1:PEM-CS_MAINSMON_EBAY_1_DQ", + "H1:ASC-INP1_P_INMON", + "H1:ASC-INP1_Y_INMON", + "H1:ASC-MICH_P_INMON", + "H1:ASC-MICH_Y_INMON", + "H1:ASC-PRC1_P_INMON", + "H1:ASC-PRC1_Y_INMON", + "H1:ASC-PRC2_P_INMON", + "H1:ASC-PRC2_Y_INMON", + "H1:ASC-SRC1_P_INMON", + "H1:ASC-SRC1_Y_INMON", + "H1:ASC-SRC2_P_INMON", + "H1:ASC-SRC2_Y_INMON", + "H1:ASC-DHARD_P_INMON", + "H1:ASC-DHARD_Y_INMON", + "H1:ASC-CHARD_P_INMON", + "H1:ASC-CHARD_Y_INMON", + "H1:ASC-DSOFT_P_INMON", + "H1:ASC-DSOFT_Y_INMON", + "H1:ASC-CSOFT_P_INMON", + "H1:ASC-CSOFT_Y_INMON" ] -# "H1:PEM-CS_MAINSMON_EBAY_1_DQ", -# "H1:ASC-INP1_P_INMON", -# "H1:ASC-INP1_Y_INMON", -# "H1:ASC-MICH_P_INMON", -# "H1:ASC-MICH_Y_INMON", -# "H1:ASC-PRC1_P_INMON", -# "H1:ASC-PRC1_Y_INMON", -# "H1:ASC-PRC2_P_INMON", -# "H1:ASC-PRC2_Y_INMON", -# "H1:ASC-SRC1_P_INMON", -# "H1:ASC-SRC1_Y_INMON", -# "H1:ASC-SRC2_P_INMON", -# "H1:ASC-SRC2_Y_INMON", -# "H1:ASC-DHARD_P_INMON", -# "H1:ASC-DHARD_Y_INMON", -# "H1:ASC-CHARD_P_INMON", -# "H1:ASC-CHARD_Y_INMON", -# "H1:ASC-DSOFT_P_INMON", -# "H1:ASC-DSOFT_Y_INMON", -# "H1:ASC-CSOFT_P_INMON", -# "H1:ASC-CSOFT_Y_INMON" -# ] sample_rate = 4096 freq_low = 55 freq_high = 65 @@ -123,7 +122,7 @@ instances = 4 [tool.typeo.scripts.clean] # paths -data_dir = "${DATA_DIR}/H1" +data_dir = "${DATA_DIR}" # /H1" write_dir = "${PROJECT_DIR}/frames" # data args @@ -138,7 +137,7 @@ look_ahead = 0.5 # inference args inference_sampling_rate = "${base.inference_sampling_rate}" batch_size = "${base.inference_batch_size}" -inference_rate = 16 +inference_rate = 8 max_latency = "${base.inference_aggregation_time}" # triton args diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 3a86500..44d89f2 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -3,10 +3,13 @@ import time from dataclasses import dataclass from pathlib import Path +from threading import Lock from typing import Iterable, Literal, Optional, Tuple, Union import numpy as np -from gwpy.timeseries import TimeSeries, TimeSeriesDict +from gwpy.timeseries import TimeSeries + +from deepclean.logging import logger PATH_LIKE = Union[str, Path] @@ -20,6 +23,8 @@ pattern = "{prefix}-{start}-{duration}.{suffix}".format(**groups) fname_re = re.compile(pattern) +lock = Lock() + def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: """Use the name of a frame file to infer its initial timestamp and length @@ -83,7 +88,7 @@ def _is_gwf(match): def get_prefix(data_dir: Path): if not data_dir.exists(): - raise FileNotFoundError("No data directory '{data_dir}'") + raise FileNotFoundError(f"No data directory '{data_dir}'") fnames = map(str, data_dir.iterdir()) matches = map(fname_re.search, fnames) @@ -158,6 +163,61 @@ def __next__(self): return fname +def resample(x: TimeSeries, sample_rate: float): + if x.sample_rate.value != sample_rate: + return x.resample(sample_rate).value + return x.value + + +def read_channel(fname, channel, sample_rate): + for i in range(3): + try: + with lock: + x = TimeSeries.read(fname, channel=channel) + except ValueError as e: + if str(e) == ( + "Cannot generate TimeSeries with 2-dimensional data" + ): + logger.warning( + "Channel {} from file {} got corrupted and was " + "read as 2D, attempting reread {}".format( + channel, fname, i + 1 + ) + ) + time.sleep(1e-1) + continue + else: + raise + except RuntimeError as e: + if str(e).startswith("Failed to read the core"): + logger.warning( + "Channel {} from file {} had corrupted header, " + "attempting reread {}".format(channel, fname, i + 1) + ) + time.sleep(2e-1) + continue + else: + raise + + x = resample(x, sample_rate) + if len(x) != sample_rate: + logger.warning( + "Channel {} in file {} got corrupted with " + "length {}, attempting reread {}".format( + channel, fname, len(x), i + 1 + ) + ) + del x + time.sleep(1e-1) + continue + + return x + else: + raise ValueError( + "Failed to read channel {} in file {}".format(channel, fname) + ) + + def load_frame( fname: str, channels: Union[str, Iterable[str]], sample_rate: float ) -> np.ndarray: @@ -165,14 +225,22 @@ def load_frame( if isinstance(channels, str): # if we don't have multiple channels, then just grab the data - data = TimeSeries.read(fname, channels) - data = data.resample(sample_rate) - data = data.value + data = read_channel(fname, channels, sample_rate) else: # otherwise stack the arrays - data = TimeSeriesDict.read(fname, channels) - data = data.resample(sample_rate) - data = np.stack([data[i].value for i in channels]) + # with lock: + # ts = TimeSeriesDict.read(fname, channels) + data = [] + for channel in channels: + # x = resample(ts[channel], sample_rate) + x = read_channel(fname, channel, sample_rate) + data.append(x) + + try: + data = np.stack(data) + except Exception: + print(fname, [(k, x.shape) for k, x in zip(channels, data)]) + raise # return as the expected type return data.astype("float32") From 53efea8ea3816e68ac5865ede424d1cd5b820036 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 12 Apr 2023 14:52:51 -0700 Subject: [PATCH 14/31] getting end-to-end deployment working --- libs/trainer/poetry.lock | 141 +- .../microservice/cleaner/cleaner/cleaner.py | 16 +- .../cleaner/cleaner/dataloader.py | 20 +- .../exporter/exporter/exporter.py | 73 +- .../microservice/exporter/exporter/main.py | 7 +- projects/microservice/exporter/poetry.lock | 161 ++- projects/microservice/exporter/pyproject.toml | 2 + projects/microservice/pyproject.toml | 14 +- projects/microservice/trainer/poetry.lock | 1225 ++++++----------- .../trainer/trainer/dataloader.py | 18 +- .../microservice/trainer/trainer/trainer.py | 34 +- .../utils/microservice/deployment.py | 50 + 12 files changed, 718 insertions(+), 1043 deletions(-) create mode 100644 projects/microservice/utils/microservice/deployment.py diff --git a/libs/trainer/poetry.lock b/libs/trainer/poetry.lock index 265abe7..b5db1dd 100644 --- a/libs/trainer/poetry.lock +++ b/libs/trainer/poetry.lock @@ -16,7 +16,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.0,<2.0" [package.extras] -speedups = ["Brotli", "aiodns", "cchardet"] +speedups = ["aiodns", "brotli", "cchardet"] [[package]] name = "aiosignal" @@ -44,11 +44,11 @@ pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["asdf (>=2.10.0)", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2022.8.2)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "mpmath", "pandas", "pyarrow (>=5.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2022.8.2)", "scipy (>=1.5)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"] -docs = ["Jinja2 (>=3.0)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "pytest (>=7.0)", "scipy (>=1.3)", "sphinx", "sphinx-astropy (>=1.6)", "sphinx-changelog (>=1.2.0)", "towncrier (<22.12.0)"] -recommended = ["matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"] -test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist"] -test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "sgp4 (>=2.3)", "skyfield (>=1.20)"] +all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] +docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "towncrier (<22.12.0)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] +recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] [[package]] name = "async-timeout" @@ -75,11 +75,12 @@ optional = false python-versions = ">=3.6" [package.extras] -cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] -dev = ["attrs[docs,tests]"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] -tests = ["attrs[tests-no-zope]", "zope.interface"] -tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope-interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] [[package]] name = "bilby" @@ -104,9 +105,9 @@ scipy = ">=1.5" tqdm = "*" [package.extras] -all = ["astropy (>=5)", "celerite", "cpnest (>=0.9.4)", "dnest4", "dynesty", "emcee", "george", "gwpy", "kombine", "lalsuite", "nessai (>=0.7.0)", "nestle", "nflows", "plotly", "ptemcee", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "pyfftw", "pymc (>=4.0.0)", "pymultinest", "schwimmbad", "scikit-learn", "tables", "ultranest (>=3.0.0)", "zeus-mcmc (>=2.3.0)"] -gw = ["astropy (>=5)", "gwpy", "lalsuite", "pyfftw", "tables"] -mcmc = ["nflows", "scikit-learn"] +all = ["cpnest (>=0.9.4)", "dynesty", "emcee", "nestle", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "ptemcee", "pymc (>=4.0.0)", "pymultinest", "kombine", "ultranest (>=3.0.0)", "dnest4", "nessai (>=0.7.0)", "schwimmbad", "zeus-mcmc (>=2.3.0)", "astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw", "scikit-learn", "nflows", "celerite", "george", "plotly"] +gw = ["astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw"] +mcmc = ["scikit-learn", "nflows"] [[package]] name = "bilby-cython" @@ -176,8 +177,8 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "chromedriver", "selenium"] docs = ["furo", "sphinx-copybutton"] -mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"] -test = ["Pillow", "matplotlib", "pytest"] +mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] +test = ["matplotlib", "pillow", "pytest"] test-no-images = ["pytest"] [[package]] @@ -193,9 +194,9 @@ matplotlib = ">=2.1" [package.extras] arviz = ["arviz (>=0.9)"] -dev = ["arviz (>=0.9)", "black", "flake8", "isort", "myst-nb", "pandoc", "pep517", "pre-commit", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "sphinx (>=1.7.5)", "sphinx-book-theme", "toml", "twine"] -docs = ["arviz (>=0.9)", "myst-nb", "pandoc", "sphinx (>=1.7.5)", "sphinx-book-theme"] -test = ["black", "isort", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "toml"] +dev = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml", "arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme", "pre-commit", "flake8", "pep517", "twine"] +docs = ["arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme"] +test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] name = "cryptography" @@ -210,11 +211,11 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] -docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] -pep8test = ["black", "check-manifest", "mypy", "ruff", "types-pytz", "types-requests"] +docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] +pep8test = ["black", "ruff", "mypy", "types-pytz", "types-requests", "check-manifest"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["hypothesis (>=1.11.4,!=3.79.2)", "iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist", "pytz"] +test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -308,7 +309,7 @@ numpy = "*" [package.extras] extras = ["h5py", "scipy"] -tests = ["coverage[toml]", "pytest", "pytest-cov"] +tests = ["pytest", "pytest-cov", "coverage"] [[package]] name = "fonttools" @@ -319,9 +320,9 @@ optional = false python-versions = ">=3.7" [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=14.0.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "scipy"] +interpolatable = ["scipy", "munkres"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -330,7 +331,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=14.0.0)"] -woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] [[package]] name = "frozenlist" @@ -357,10 +358,10 @@ setuptools = "*" [package.extras] dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] -docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] +docs = ["repoze-sphinx-autointerface", "sphinxcontrib-programoutput", "zope-schema"] monitor = ["psutil (>=5.7.0)"] -recommended = ["backports.socketpair", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)", "selectors2"] -test = ["backports.socketpair", "cffi (>=1.12.2)", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "dnspython (>=1.16.0,<2.0)", "futures", "idna", "mock", "objgraph", "psutil (>=5.7.0)", "requests", "selectors2"] +recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports-socketpair", "psutil (>=5.7.0)"] +test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports-socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] [[package]] name = "geventhttpclient" @@ -385,7 +386,7 @@ optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" [package.extras] -docs = ["Sphinx", "docutils (<0.18)"] +docs = ["sphinx", "docutils (<0.18)"] test = ["objgraph", "psutil"] [[package]] @@ -454,8 +455,8 @@ scipy = ">=1.2.0" tqdm = ">=4.10.0" [package.extras] -conda = ["python-framel (>=8.40.1)", "python-ldas-tools-framecpp", "python-nds2-client"] -dev = ["astropy (<5.1a0)", "ciecplib", "lalsuite", "lscsoft-glue", "maya", "pandas", "psycopg2", "pyRXP", "pycbc (>=1.13.4)", "pymysql", "python-ligo-lw (>=1.7.0)", "sqlalchemy", "uproot (>=3.11)", "uproot3"] +conda = ["python-framel (>=8.40.1)", "python-nds2-client", "python-ldas-tools-framecpp"] +dev = ["ciecplib", "maya", "pandas", "psycopg2", "pymysql", "pyrxp", "sqlalchemy", "uproot (>=3.11)", "uproot3", "astropy (<5.1a0)", "lalsuite", "lscsoft-glue", "pycbc (>=1.13.4)", "python-ligo-lw (>=1.7.0)"] docs = ["numpydoc (>=0.8.0)", "requests", "sphinx (>=4.0.0)", "sphinx-automodapi", "sphinx-material (>=0.0.32)", "sphinx-panels (>=0.6.0)", "sphinxcontrib-programoutput"] test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "pytest-cov (>=2.4.0)", "pytest-socket", "pytest-xdist", "requests-mock"] @@ -534,8 +535,8 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] [[package]] name = "iniconfig" @@ -776,9 +777,9 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface"] +tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] [[package]] name = "pyparsing" @@ -789,7 +790,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pytest" @@ -863,7 +864,7 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] +use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] [[package]] name = "safe-netrc" @@ -888,9 +889,9 @@ python-versions = "<3.12,>=3.8" numpy = ">=1.19.5,<1.27.0" [package.extras] -dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"] -doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] -test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"] [[package]] name = "scitokens" @@ -906,7 +907,7 @@ PyJWT = ">=1.6.1" setuptools = "*" [package.extras] -docs = ["Sphinx"] +docs = ["sphinx"] [[package]] name = "setuptools" @@ -917,9 +918,9 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] -testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)", "pytest-flake8"] +testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] [[package]] name = "setuptools-scm" @@ -1028,9 +1029,9 @@ protobuf = {version = ">=3.5.0,<4", optional = true, markers = "extra == \"all\" python-rapidjson = ">=0.9.1" [package.extras] -all = ["aiohttp (>=3.8.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] -grpc = ["grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] -http = ["aiohttp (>=3.8.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] +all = ["protobuf (>=3.5.0,<4)", "grpcio (>=1.41.0)", "packaging (>=14.1)", "numpy (>=1.19.1)", 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43c73ae..9ed35cc 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/cleaner.py @@ -4,6 +4,7 @@ from cleaner.dataloader import get_data_generators from cleaner.writer import Writer +from microservice.deployment import Deployment from deepclean.infer.callback import Callback, State from deepclean.infer.clean import Cleaner @@ -16,8 +17,8 @@ @scriptify def main( # IO args - data_dir: Path, - write_dir: Path, + run_directory: Path, + data_directory: Path, # Data args sample_rate: float, frame_length: float, @@ -40,7 +41,6 @@ def main( look_ahead: float = 0.5, max_files: int = 300, verbose: bool = False, - log_file: Optional[Path] = None, ) -> None: """ Iterate through directories of witness and strain @@ -126,17 +126,15 @@ def main( current frame. Useful for avoiding filtering edge effects. verbose: Flag indicating whether to log at DEBUG or INFO levels - log_file: - Path to a file to write logs to. If left as `None`, logs will - only go to stdout. """ - log_file.parent.mkdir(parents=True, exist_ok=True) + deployment = Deployment(run_directory) + log_file = deployment.log_directory / "infer.log" logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) witness_it, strain_it = get_data_generators( - data_dir, + data_directory, channels, sample_rate=sample_rate, inference_sampling_rate=inference_sampling_rate, @@ -178,6 +176,8 @@ def main( freq_low=freq_low, freq_high=freq_high, ) + + write_dir = deployment.frame_directory writer = Writer(write_dir, strain_it, cleaner, max_files) callback = Callback(writer, **states) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 03adf87..59a1c40 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -5,7 +5,8 @@ import numpy as np from gwpy.timeseries import TimeSeries -from microservice.frames import FrameCrawler, load_frame +from microservice.deployment import DataStream +from microservice.frames import load_frame from deepclean.logging import logger @@ -47,23 +48,14 @@ def strain_iterator(q: Queue): def frame_iter(crawler, channels, sample_rate, q): - for fname in crawler: + for fname, strain_fname in crawler: logger.debug(f"Loading frame file {fname}") - # frame = load_frame(fname, channels, sample_rate) - # strain, witnesses = np.split(frame, [1], axis=0) - # strain = TimeSeries( - # strain[0], sample_rate=sample_rate, channel=channels[0] - # ) - witnesses = load_frame(fname, channels[1:], sample_rate) - - strain_fname = fname.name.replace("Detchar", "HOFT") - strain_fname = fname.parent.parent / "llhoft" / strain_fname strain = TimeSeries.read(strain_fname, channel=channels[0]) if strain.sample_rate.value != sample_rate: strain = strain.resample(sample_rate) - q.put((strain, fname)) + q.put((strain, strain_fname)) yield witnesses q.put(None) @@ -78,8 +70,8 @@ def get_data_generators( timeout: float = 10, ): t0 = 0 if start_first else -1 - # crawler = FrameCrawler(data_dir, t0, timeout) - crawler = FrameCrawler(data_dir / "lldetchar", t0, timeout) + stream = DataStream(data_dir) + crawler = stream.crawl(t0, timeout) strain_q = Queue() it = frame_iter(crawler, channels, sample_rate, strain_q) diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index a11e23b..21ab99f 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -1,24 +1,27 @@ import argparse +import os from dataclasses import dataclass from pathlib import Path from typing import Optional import torch import tritonclient.grpc.model_config_pb2 as model_config -from ml4gw.transforms import ChannelWiseScaler -from typeo.parser import make_parser -from typeo.actions import TypeoTomlAction +from microservice.deployment import Deployment import hermes.quiver as qv from deepclean.architectures import DeepCleanAE as architecture -from deepclean.utils.channels import ChannelList, get_channels from deepclean.export import repo as repo_utils from deepclean.export.model import DeepClean +from deepclean.logging import logger +from deepclean.utils.channels import ChannelList, get_channels +from ml4gw.transforms import ChannelWiseScaler +from typeo.actions import TypeoTomlAction +from typeo.parser import make_parser @dataclass class Exporter: - repository_directory: str + run_directory: Path channels: ChannelList kernel_length: float sample_rate: float @@ -28,12 +31,11 @@ class Exporter: streams_per_gpu: int = 2 platform: qv.Platform = qv.Platform.ONNX instances: Optional[int] = None + verbose: bool = False @classmethod def from_config( - cls, - config: Optional[Path] = None, - script: Optional[str] = None + cls, config: Optional[Path] = None, script: Optional[str] = None ): args = ["--typeo"] if config is None: @@ -51,7 +53,7 @@ def from_config( bools={}, subcommands={}, nargs="*", - action=TypeoTomlAction + action=TypeoTomlAction, ) args = typeo_parser.parse_args(args) @@ -61,6 +63,12 @@ def from_config( return cls(**vars(args)) def __post_init__(self): + self.deployment = Deployment(self.run_directory) + self.logger = logger.get_logger( + "Deepclean export", + self.deployment.log_directory / "exporter.log", + verbose=self.verbose, + ) self.channels = get_channels(self.channels) self.num_witnesses = len(self.channels) - 1 @@ -87,11 +95,11 @@ def __post_init__(self): # initialize an empty repository with an # associated deepclean entry already in it self.repo = repo_utils.initialize_repo( - self.repository_directory, + str(self.deployment.repository_directory), self.channels[1:], self.platform, self.instances, - clean=True + clean=True, ) # initialize an ensemble that we'll add steps to @@ -105,7 +113,7 @@ def __post_init__(self): preproc.export_version( self.model.preprocessor, input_shapes={"witnesses": input_shape}, - output_names=["normalized"] + output_names=["normalized"], ) self.set_version_policy(preproc) @@ -118,7 +126,7 @@ def __post_init__(self): name="snapshotter", streams_per_gpu=self.streams_per_gpu, batch_size=self.batch_size, - versions=-1 + versions=-1, ) # now create a second mapping from the @@ -128,7 +136,7 @@ def __post_init__(self): ensemble.pipe( snapshotter.outputs["preprocessor.witnesses_snapshot"], preproc.inputs["witnesses"], - inbound_version=1 + inbound_version=1, ) # now export deepclean, and create mappings @@ -137,7 +145,7 @@ def __post_init__(self): self.repo.models["deepclean"].export_version( deepclean, input_shapes={"normalized": input_shape}, - output_names=["noise_prediction"] + output_names=["noise_prediction"], ) self.set_version_policy(self.repo.models["deepclean"]) @@ -148,7 +156,7 @@ def __post_init__(self): self.repo.models["deepclean"].inputs["normalized"], key=f"normalized-{key}", outbound_version=version, - inbound_version=version + inbound_version=version, ) # now do the same for a postprocessing model, @@ -158,7 +166,7 @@ def __post_init__(self): postproc.export_version( self.model.postprocessor, input_shapes={"noise_prediction": output_shape}, - output_names=["unscaled"] + output_names=["unscaled"], ) self.set_version_policy(postproc) for key, version in keys.items(): @@ -167,7 +175,7 @@ def __post_init__(self): postproc.inputs["noise_prediction"], key=f"noise_prediction-{key}", outbound_version=version, - inbound_version=version + inbound_version=version, ) ensemble.add_streaming_output( postproc.outputs["unscaled"], @@ -177,7 +185,7 @@ def __post_init__(self): name=f"aggregator-{key}", key=f"unscaled-{key}", streams_per_gpu=self.streams_per_gpu, - version=version + version=version, ) ensemble.export_version(None) @@ -216,6 +224,11 @@ def update_ensemble_versions(self, version: Optional[int] = None): if version is None: version = step.model_version + 1 step.model_version = version + + self.logger.info( + "Updating production model in ensemble " + "to use version {}".format(version) + ) ensemble.config.write() def export(self): @@ -231,15 +244,23 @@ def export(self): version = int(Path(export_path).parent.name) return version - def export_weights(self, train_dir: Path): + def export_weights(self, train_dir: str): + if not os.path.isabs(train_dir): + train_dir = self.deployment.train_directory / train_dir weights_path = train_dir / "weights.pt" + if not weights_path.exists(): + raise ValueError(f"No model weights {weights_path}") - state_dict = torch.load(weights_path) - self.deepclean.load_state_dict(state_dict) + self.logger.info(f"Exporting model with weights {weights_path}") + state_dict = torch.load(weights_path, map_location=torch.device("cpu")) + self.model.load_state_dict(state_dict) version = self.export() - _, start, stop = train_dir.name.split("-") - self.deepclean.config.parameters["versions"] += "{}={}-{},".format( - version, start, stop + self.logger.info( + f"Export of version {version} complete, " + "adding metadata to DeepClean config" ) - self.deepclean.config.write() + version_info = f"{version}={train_dir.name}," + deepclean = self.repo.models["deepclean"] + deepclean.config.parameters["versions"].string_value += version_info + deepclean.config.write() diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index 1058816..14a8c16 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -1,15 +1,18 @@ -from flask import Flask from exporter.exporter import Exporter +from flask import Flask -app = Flask(__name__) exporter = Exporter.from_config() +exporter.export_weights("1251331220-1251333268") +app = Flask(__name__) @app.route("/export/") def export_model(train_dir): + app.logger.info(f"Received export request for {train_dir}") exporter.export_weights(train_dir) @app.route("/increment") def increment_ensemble(): + app.logger.info("Received ensemble version increment request") exporter.update_ensemble_version() diff --git a/projects/microservice/exporter/poetry.lock b/projects/microservice/exporter/poetry.lock index ddde1ab..1511c61 100644 --- a/projects/microservice/exporter/poetry.lock +++ b/projects/microservice/exporter/poetry.lock @@ -16,7 +16,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.0,<2.0" [package.extras] -speedups = ["Brotli", "aiodns", "cchardet"] +speedups = ["aiodns", "brotli", "cchardet"] [[package]] name = "aiosignal" @@ -44,11 +44,11 @@ pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["asdf (>=2.10.0)", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2022.8.2)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "mpmath", "pandas", "pyarrow (>=5.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2022.8.2)", "scipy (>=1.5)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"] -docs = ["Jinja2 (>=3.0)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "pytest (>=7.0)", "scipy (>=1.3)", "sphinx", "sphinx-astropy (>=1.6)", "sphinx-changelog (>=1.2.0)"] -recommended = ["matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"] -test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist"] -test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "sgp4 (>=2.3)", "skyfield (>=1.20)"] +all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] +docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] +recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] +test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] +test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] [[package]] name = "async-timeout" @@ -67,11 +67,12 @@ optional = false python-versions = ">=3.6" [package.extras] -cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] -dev = ["attrs[docs,tests]"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] -tests = ["attrs[tests-no-zope]", "zope.interface"] -tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] +cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs"] +docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope-interface"] +tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] [[package]] name = "bilby" @@ -96,9 +97,9 @@ scipy = ">=1.5" tqdm = "*" [package.extras] -all = ["astropy (>=5)", "celerite", "cpnest (>=0.9.4)", "dnest4", "dynesty", "emcee", "george", "gwpy", "kombine", "lalsuite", "nessai (>=0.7.0)", "nestle", "nflows", "plotly", "ptemcee", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "pyfftw", "pymc (>=4.0.0)", "pymultinest", "schwimmbad", "scikit-learn", "tables", "ultranest (>=3.0.0)", "zeus-mcmc (>=2.3.0)"] -gw = ["astropy (>=5)", "gwpy", "lalsuite", "pyfftw", "tables"] -mcmc = ["nflows", "scikit-learn"] +all = ["cpnest (>=0.9.4)", "dynesty", "emcee", "nestle", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "ptemcee", "pymc (>=4.0.0)", "pymultinest", "kombine", "ultranest (>=3.0.0)", "dnest4", "nessai (>=0.7.0)", "schwimmbad", "zeus-mcmc (>=2.3.0)", "astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw", "scikit-learn", "nflows", "celerite", "george", "plotly"] +gw = ["astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw"] +mcmc = ["scikit-learn", "nflows"] [[package]] name = "bilby-cython" @@ -189,8 +190,8 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "chromedriver", "selenium"] docs = ["furo", "sphinx-copybutton"] -mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"] -test = ["Pillow", "matplotlib", "pytest"] +mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] +test = ["matplotlib", "pillow", "pytest"] test-no-images = ["pytest"] [[package]] @@ -206,9 +207,9 @@ matplotlib = ">=2.1" [package.extras] arviz = ["arviz (>=0.9)"] -dev = ["arviz (>=0.9)", "black", "flake8", "isort", "myst-nb", "pandoc", "pep517", "pre-commit", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "sphinx (>=1.7.5)", "sphinx-book-theme", "toml", "twine"] -docs = ["arviz (>=0.9)", "myst-nb", "pandoc", "sphinx (>=1.7.5)", "sphinx-book-theme"] -test = ["black", "isort", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "toml"] +dev = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml", "arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme", "pre-commit", "flake8", "pep517", "twine"] +docs = ["arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme"] +test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] name = "cryptography" @@ -223,11 +224,11 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] -docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] -pep8test = ["black", "check-manifest", "mypy", "ruff"] +docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] +pep8test = ["black", "ruff", "mypy", "check-manifest"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist"] +test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -350,7 +351,7 @@ numpy = "*" [package.extras] extras = ["h5py", "scipy"] -tests = ["coverage[toml]", "pytest", "pytest-cov"] +tests = ["pytest", "pytest-cov", "coverage"] [[package]] name = "exceptiongroup" @@ -391,9 +392,9 @@ optional = false python-versions = ">=3.8" [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=15.0.0)", "xattr"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "scipy"] +interpolatable = ["scipy", "munkres"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -402,7 +403,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.0.0)"] -woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] +woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] [[package]] name = "frozenlist" @@ -429,10 +430,10 @@ setuptools = "*" [package.extras] dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] -docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] +docs = ["repoze-sphinx-autointerface", "sphinxcontrib-programoutput", "zope-schema"] monitor = ["psutil (>=5.7.0)"] -recommended = ["backports.socketpair", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)", "selectors2"] -test = ["backports.socketpair", "cffi (>=1.12.2)", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "dnspython (>=1.16.0,<2.0)", "futures", "idna", "mock", "objgraph", "psutil (>=5.7.0)", "requests", "selectors2"] +recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports-socketpair", "psutil (>=5.7.0)"] +test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports-socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] [[package]] name = "geventhttpclient" @@ -457,7 +458,7 @@ optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" [package.extras] -docs = ["Sphinx", "docutils (<0.18)"] +docs = ["sphinx", "docutils (<0.18)"] test = ["objgraph", "psutil"] [[package]] @@ -526,8 +527,8 @@ scipy = ">=1.2.0" tqdm = ">=4.10.0" [package.extras] -conda = ["python-framel (>=8.40.1)", "python-ldas-tools-framecpp", "python-nds2-client"] -dev = ["astropy (<5.1a0)", "ciecplib", "lalsuite", "lscsoft-glue", "maya", "pandas", "psycopg2", "pyRXP", "pycbc (>=1.13.4)", "pymysql", "python-ligo-lw (>=1.7.0)", "sqlalchemy", "uproot (>=3.11)", "uproot3"] +conda = ["python-framel (>=8.40.1)", "python-nds2-client", "python-ldas-tools-framecpp"] +dev = ["ciecplib", "maya", "pandas", "psycopg2", "pymysql", "pyrxp", "sqlalchemy", "uproot (>=3.11)", "uproot3", "astropy (<5.1a0)", "lalsuite", "lscsoft-glue", "pycbc (>=1.13.4)", "python-ligo-lw (>=1.7.0)"] docs = ["numpydoc (>=0.8.0)", "requests", "sphinx (>=4.0.0)", "sphinx-automodapi", "sphinx-material (>=0.0.32)", "sphinx-panels (>=0.6.0)", "sphinxcontrib-programoutput"] test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "pytest-cov (>=2.4.0)", "pytest-socket", "pytest-xdist", "requests-mock"] @@ -611,9 +612,9 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] -testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl-flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] [[package]] name = "importlib-resources" @@ -627,8 +628,8 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] -testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] [[package]] name = "iniconfig" @@ -726,6 +727,19 @@ pyparsing = ">=2.3.1" python-dateutil = ">=2.7" setuptools_scm = ">=7" +[[package]] +name = "microservice" +version = "0.0.1" +description = "Utilities for DeepClean production deployment" +category = "main" +optional = false +python-versions = ">=3.8,<3.11" +develop = true + +[package.source] +type = "directory" +url = "../utils" + [[package]] name = "ml4gw" version = "0.1.1" @@ -880,9 +894,9 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface"] +tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] [[package]] name = "pyparsing" @@ -893,7 +907,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["jinja2", "railroad-diagrams"] +diagrams = ["railroad-diagrams", "jinja2"] [[package]] name = "pytest" @@ -966,7 +980,7 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] +use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] [[package]] name = "safe-netrc" @@ -991,9 +1005,9 @@ python-versions = "<3.12,>=3.8" numpy = ">=1.19.5,<1.27.0" [package.extras] -dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"] -doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] -test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"] +doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"] +dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"] [[package]] name = "scitokens" @@ -1009,7 +1023,7 @@ PyJWT = ">=1.6.1" setuptools = "*" [package.extras] -docs = ["Sphinx"] +docs = ["sphinx"] [[package]] name = "setuptools" @@ -1020,9 +1034,9 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] -testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] -testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] +docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)", "pytest-flake8"] +testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] [[package]] name = "setuptools-scm" @@ -1148,9 +1162,9 @@ protobuf = {version = ">=3.5.0,<4", optional = true, markers = "extra == \"all\" python-rapidjson = 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a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index d62fe0e..21d76a1 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -1,6 +1,6 @@ [tool.typeo.base] # global paths -log_dir = "${PROJECT_DIR}/logs" +run_directory = "${PROJECT_DIR}" # global data args channels = [ @@ -62,8 +62,8 @@ verbose = false [tool.typeo.scripts.train] # paths -output_directory = "${PROJECT_DIR}/train" -data_directory = "${DATA_DIR}/H1" +run_directory = "${base.run_directory}" +data_directory = "${DATA_DIR}" # data parameters channels = "${base.channels}" @@ -102,7 +102,7 @@ autoencoder = {} [tool.typeo.scripts.exporter] # paths -repository_directory = "${PROJECT_DIR}/model_repo" +run_directory = "${base.run_directory}" # data args channels = "${base.channels}" @@ -122,8 +122,8 @@ instances = 4 [tool.typeo.scripts.clean] # paths -data_dir = "${DATA_DIR}" # /H1" -write_dir = "${PROJECT_DIR}/frames" +run_directory = "${base.run_directory}" +data_directory = "${DATA_DIR}" # data args sample_rate = "${base.sample_rate}" @@ -150,4 +150,4 @@ sequence_id = 1001 start_first = true timeout = 3 verbose = "${base.verbose}" -log_file = "${base.log_dir}/clean.log" +max_files = 4096 diff --git a/projects/microservice/trainer/poetry.lock b/projects/microservice/trainer/poetry.lock index a40948e..7ce73e4 100644 --- a/projects/microservice/trainer/poetry.lock +++ b/projects/microservice/trainer/poetry.lock @@ -1,6 +1,37 @@ +[[package]] +name = "aiohttp" +version = "3.8.4" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<4.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["aiodns", "brotli", "cchardet"] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = 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(>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] +[[package]] +name = "async-timeout" +version = "4.0.2" +description = "Timeout context manager for asyncio programs" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "attrs" version = "22.2.0" @@ -30,8 +69,8 @@ python-versions = ">=3.6" [package.extras] cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] dev = ["attrs"] -docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["attrs", "zope-interface"] +docs = ["furo", "sphinx", "myst-parser", "zope.interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] +tests = ["attrs", "zope.interface"] tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] @@ -63,7 +102,7 @@ gw = ["astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw"] mcmc = ["scikit-learn", "nflows"] [[package]] -name = "bilby-cython" +name = "bilby.cython" version = "0.4.0" description = "Optimized functionality for Bilby" category = "main" @@ -73,6 +112,14 @@ python-versions = ">=3.8" [package.dependencies] numpy = "*" +[[package]] +name = "brotli" +version = "1.0.9" +description = "Python bindings for the Brotli compression library" +category = "main" +optional = false +python-versions = "*" + [[package]] name = "certifi" version = "2022.12.7" @@ -145,7 +192,7 @@ test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] [[package]] name = "cryptography" -version = "39.0.2" +version = "40.0.1" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." category = "main" optional = false @@ -157,10 +204,10 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "ruff", "mypy", "types-pytz", "types-requests", "check-manifest"] +pep8test = ["black", "ruff", "mypy", "check-manifest"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601", "pytz", "hypothesis (>=1.11.4,!=3.79.2)"] +test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -188,6 +235,23 @@ torch = "^1.10" type = "directory" url = "../../../libs/architectures" +[[package]] +name = "deepclean.export" +version = "0.0.1" +description = "IaaS export utilities" +category = "main" +optional = false +python-versions = "^3.8,<3.11" +develop = true + +[package.dependencies] +"hermes.quiver" = {path = "../../hermes/hermes/hermes.quiver", develop = true, extras = ["torch"]} +ml4gw = {path = "../../ml4gw", develop = true} + +[package.source] +type = "directory" +url = "../../../libs/export" + [[package]] name = "deepclean.logging" version = "0.0.1" @@ -211,9 +275,9 @@ python-versions = "^3.8,<3.11" develop = true [package.dependencies] +"deepclean.export" = {path = "../export", develop = true} "deepclean.utils" = {path = "../utils", develop = true} ml4gw = {path = "../../ml4gw", develop = true} -torch = "^1.10" [package.source] type = "directory" @@ -259,9 +323,9 @@ igwn-auth-utils = ">=0.3.1" ligo-segments = ">=1.0.0" [package.extras] -docs = ["sphinx", "sphinx-automodapi", "sphinx-immaterial"] -lint = ["flake8 (>=3.7.0)", "flake8-bandit"] -test = ["pytest (>=2.9.2)", "pytest-cov (>=2.5.1)", "requests-mock (>=1.5.0)"] +test = ["requests-mock (>=1.5.0)", "pytest-cov (>=2.5.1)", "pytest (>=2.9.2)"] +lint = ["flake8-bandit", "flake8 (>=3.7.0)"] +docs = ["sphinx-immaterial", "sphinx-automodapi", "sphinx"] [[package]] name = "dynesty" @@ -288,7 +352,7 @@ tests = ["pytest", "pytest-cov", "coverage"] [[package]] name = "fonttools" -version = "4.39.0" +version = "4.39.3" description = "Tools to manipulate font files" category = "main" optional = false @@ -308,6 +372,73 @@ ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.0.0)"] woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +[[package]] +name = "frozenlist" +version = "1.3.3" +description = "A list-like structure which implements collections.abc.MutableSequence" +category = "main" +optional = false +python-versions = ">=3.7" + +[[package]] +name = "gevent" +version = "22.10.2" +description = "Coroutine-based network library" +category = "main" +optional = false +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5" + +[package.dependencies] +cffi = {version = ">=1.12.2", markers = "platform_python_implementation == \"CPython\" and sys_platform == \"win32\""} +greenlet = {version = ">=2.0.0", markers = "platform_python_implementation == \"CPython\""} +setuptools = "*" +"zope.event" = "*" +"zope.interface" = "*" + +[package.extras] +dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] +docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] +monitor = ["psutil (>=5.7.0)"] +recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports.socketpair", "psutil (>=5.7.0)"] +test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports.socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] + +[[package]] +name = "geventhttpclient" +version = "2.0.2" +description = "http client library for gevent" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +brotli = "*" +certifi = "*" +gevent = ">=0.13" +six = "*" + +[[package]] +name = "greenlet" +version = "2.0.2" +description = "Lightweight in-process concurrent programming" +category = "main" +optional = false +python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" + +[package.extras] +docs = ["sphinx", "docutils (<0.18)"] +test = ["objgraph", "psutil"] + +[[package]] +name = "grpcio" +version = "1.53.0" +description = "HTTP/2-based RPC framework" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.extras] +protobuf = ["grpcio-tools (>=1.53.0)"] + [[package]] name = "gwdatafind" version = "1.1.3" @@ -327,7 +458,7 @@ test = ["pytest (>=2.8.0)", "pytest-cov", "requests-mock"] [[package]] name = "gwosc" -version = "0.6.1" +version = "0.7.0" description = "A python interface to the GW Open Science data archive" category = "main" optional = false @@ -338,6 +469,7 @@ requests = ">=1.0.0" [package.extras] docs = ["sphinx", "sphinx-automodapi", "sphinx-rtd-theme"] +lint = ["flake8 (<5.0.0a0)", "flake8-bandit"] test = ["pytest (>=2.7.0)", "pytest-cov", "pytest-socket", "requests-mock (>=1.5.0)"] [[package]] @@ -379,6 +511,31 @@ python-versions = ">=3.7" [package.dependencies] numpy = ">=1.14.5" +[[package]] +name = "hermes.quiver" +version = "0.1.0" +description = "Model export utilities" +category = "main" +optional = false +python-versions = ">=3.8,<3.11" +develop = true + +[package.dependencies] +protobuf = "^3.17" +requests = "^2.26.0" +torch = {version = "^1.7", optional = true} +tritonclient = {version = "^2.22", extras = ["all"]} + +[package.extras] +tensorflow = ["tensorflow (>=2.3,<3.0)"] +torch = ["torch (>=1.7,<2.0)"] +gcs = ["google-cloud-storage (>=1.38,<2.0)"] +tensorrt = ["nvidia-tensorrt (>=8.0,<9.0)"] + +[package.source] +type = "directory" +url = "../../../hermes/hermes/hermes.quiver" + [[package]] name = "idna" version = "3.4" @@ -408,7 +565,7 @@ test = ["pytest (>=3.9.1)", "pytest-cov", "requests (>=2.14)", "requests-mock", [[package]] name = "importlib-metadata" -version = "6.0.0" +version = "6.3.0" description = "Read metadata from Python packages" category = "main" optional = false @@ -420,7 +577,7 @@ zipp = ">=0.5" [package.extras] docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl-flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] +testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] [[package]] name = "importlib-resources" @@ -517,6 +674,14 @@ torchtyping = "^0.1" type = "directory" url = "../../../ml4gw" +[[package]] +name = "multidict" +version = "6.0.4" +description = "multidict implementation" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "numpy" version = "1.24.2" @@ -583,7 +748,7 @@ python-versions = ">=3.7" [[package]] name = "pandas" -version = "1.5.3" +version = "2.0.0" description = "Powerful data structures for data analysis, time series, and statistics" category = "main" optional = false @@ -594,24 +759,53 @@ numpy = [ {version = ">=1.20.3", markers = "python_version < \"3.10\""}, {version = ">=1.21.0", markers = "python_version >= \"3.10\""}, ] -python-dateutil = ">=2.8.1" +python-dateutil = ">=2.8.2" pytz = ">=2020.1" +tzdata = ">=2022.1" [package.extras] -test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"] +all = ["beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "PyQt5 (>=5.15.1)", "pyreadstat (>=1.1.2)", "pytest (>=7.0.0)", "pytest-xdist (>=2.2.0)", "pytest-asyncio (>=0.17.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "scipy (>=1.7.1)", "s3fs (>=2021.08.0)", "SQLAlchemy (>=1.4.16)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"] +aws = ["s3fs (>=2021.08.0)"] +clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"] +compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"] +computation = ["scipy (>=1.7.1)", "xarray (>=0.21.0)"] +excel = ["odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pyxlsb (>=1.0.8)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)"] +feather = ["pyarrow (>=7.0.0)"] +fss = ["fsspec (>=2021.07.0)"] +gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"] +hdf5 = ["tables (>=3.6.1)"] +html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"] +mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"] +output_formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"] +parquet = ["pyarrow (>=7.0.0)"] +performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"] +plot = ["matplotlib (>=3.6.1)"] +postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"] +spss = ["pyreadstat (>=1.1.2)"] +sql-other = ["SQLAlchemy (>=1.4.16)"] +test = ["hypothesis (>=6.34.2)", "pytest (>=7.0.0)", "pytest-xdist (>=2.2.0)", "pytest-asyncio (>=0.17.0)"] +xml = ["lxml (>=4.6.3)"] [[package]] name = "pillow" -version = "9.4.0" +version = "9.5.0" description = "Python Imaging Library (Fork)" category = "main" optional = false python-versions = ">=3.7" [package.extras] -docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinxext-opengraph"] +docs = ["furo", "olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-removed-in", "sphinxext-opengraph"] tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"] +[[package]] +name = "protobuf" +version = "3.20.3" +description = "Protocol Buffers" +category = "main" +optional = false +python-versions = ">=3.7" + [[package]] name = "pycparser" version = "2.21" @@ -622,7 +816,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" [[package]] name = "pyerfa" -version = "2.0.0.1" +version = "2.0.0.3" description = "Python bindings for ERFA" category = "main" optional = false @@ -645,8 +839,8 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] -docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope-interface"] +dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] [[package]] @@ -671,9 +865,17 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" [package.dependencies] six = ">=1.5" +[[package]] +name = "python-rapidjson" +version = "1.10" +description = "Python wrapper around rapidjson" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "pytz" -version = "2022.7.1" +version = "2023.3" description = "World timezone definitions, modern and historical" category = "main" optional = false @@ -750,7 +952,7 @@ docs = ["sphinx"] [[package]] name = "setuptools" -version = "67.6.0" +version = "67.6.1" description = "Easily download, build, install, upgrade, and uninstall Python packages" category = "main" optional = false @@ -850,9 +1052,31 @@ notebook = ["ipywidgets (>=6)"] slack = ["slack-sdk"] telegram = ["requests"] +[[package]] +name = "tritonclient" +version = "2.32.0" +description = "Python client library and utilities for communicating with Triton Inference Server" +category = "main" +optional = false +python-versions = "*" + +[package.dependencies] +aiohttp = {version = ">=3.8.1", optional = true, markers = "extra == \"all\""} +geventhttpclient = {version = ">=1.4.4,<=2.0.2", optional = true, markers = "extra == \"all\""} +grpcio = {version = ">=1.41.0", optional = true, markers = "extra == \"all\""} +numpy = ">=1.19.1" +packaging = {version = ">=14.1", optional = true, markers = "extra == \"all\""} +protobuf = {version = ">=3.5.0,<4", optional = true, markers = "extra == \"all\""} +python-rapidjson = ">=0.9.1" + +[package.extras] +all = ["protobuf (>=3.5.0,<4)", "grpcio (>=1.41.0)", "packaging (>=14.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "aiohttp (>=3.8.1)"] +grpc = ["protobuf (>=3.5.0,<4)", "grpcio (>=1.41.0)", "packaging (>=14.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] +http = ["geventhttpclient (>=1.4.4,<=2.0.2)", "aiohttp (>=3.8.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] + [[package]] name = "typeguard" -version = "3.0.0" +version = "3.0.2" description = "Run-time type checker for Python" category = "main" optional = false @@ -864,7 +1088,7 @@ typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.11\""} [package.extras] doc = ["packaging", "sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"] -test = ["pytest", "mypy (>=0.991)"] +test = ["pytest (>=7)", "mypy (>=0.991)"] [[package]] name = "typeo" @@ -890,6 +1114,14 @@ category = "main" optional = false python-versions = ">=3.7" +[[package]] +name = "tzdata" +version = "2023.3" +description = "Provider of IANA time zone data" +category = "main" +optional = false +python-versions = ">=2" + [[package]] name = "urllib3" version = "1.26.15" @@ -914,6 +1146,18 @@ python-versions = ">=3.7" [package.extras] test = ["pytest (>=6.0.0)"] +[[package]] +name = "yarl" +version = "1.8.2" +description = "Yet another URL library" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +idna = ">=2.0" +multidict = ">=4.0" + [[package]] name = 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a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -8,7 +8,8 @@ from typing import Optional import numpy as np -from microservice.frames import FrameCrawler, load_frame +from microservice.deployment import DataStream +from microservice.frames import load_frame from deepclean.logging import logger from deepclean.utils.channels import ChannelList, get_channels @@ -23,10 +24,11 @@ def collect_frames( sample_rate: float, timeout: Optional[float] = None, ) -> np.ndarray: + stream = DataStream(data_directory) + crawler = stream.crawl(t0=0, timeout=timeout) + channels = get_channels(channels) - crawler = FrameCrawler(data_directory, timeout=timeout) - crawler = iter(crawler) - start = crawler.t0 + 0 + start = None size = int(duration * sample_rate) edge_size = int(cadence * sample_rate) @@ -40,10 +42,12 @@ def collect_frames( next(crawler) while not event.is_set(): - fname = next(crawler) + fname, strain_fname = next(crawler) + if start is None: + start = int(fname.stem.split("-")[-2]) + witnesses = load_frame(fname, channels[1:], sample_rate) - strain = load_frame(fname, channels[0], sample_rate) - # logger.debug(f"Loaded {fname} with size {len(strain)}, {len(y)}/{size}") + strain = load_frame(strain_fname, channels[0], sample_rate) X = np.concatenate([X, witnesses], axis=1) y = np.concatenate([y, strain]) diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 47e23cd..76c01e0 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -2,6 +2,8 @@ from typing import Callable, Optional import numpy as np +import requests +from microservice.deployment import Deployment from trainer.dataloader import DataCollector from deepclean.architectures import architectures @@ -25,7 +27,7 @@ def _get_str(*times): def train_on_segment( X: np.ndarray, y: np.ndarray, - output_directory: Path, + deployment: Deployment, start: float, architecture: Callable, sample_rate: float, @@ -36,11 +38,11 @@ def train_on_segment( duration = len(y) / sample_rate str_rep = _get_str(start, start + duration) - log_file = output_directory / "log" / f"train-{str_rep}.log" + log_file = deployment.log_directory / f"train.{str_rep}.log" - output_directory = output_directory / "training" / str_rep - logger.info(f"Saving training outputs to directory {output_directory}") + output_directory = deployment.train_directory / str_rep output_directory.mkdir(parents=True, exist_ok=True) + logger.info(f"Saving training outputs to directory {output_directory}") # now set the logger to one specific to this training run logger.set_logger(f"DeepClean train {str_rep}", log_file, verbose) @@ -62,12 +64,15 @@ def train_on_segment( ) +exclude = ["X", "y", "architecture", "valid_data", "output_directory"] + + @scriptify( - kwargs=make_dummy(train, exclude=["X", "y", "architecture", "valid_data"]), + kwargs=make_dummy(train, exclude=exclude), architecture=architectures, ) def main( - output_directory: Path, + run_directory: Path, data_directory: Path, channels: ChannelList, architecture: Callable, @@ -79,15 +84,15 @@ def main( verbose: bool = False, **kwargs, ): - log_directory = output_directory / "log" - log_directory.mkdir(parents=True, exist_ok=True) + deployment = Deployment(run_directory) + log_file = deployment.log_directory / "train.root.log" root_logger = logger.set_logger( - "DeepClean trainer", log_directory / "root.log", verbose=verbose + "DeepClean trainer", log_file, verbose=verbose ) frame_collector = DataCollector( data_directory, - log_directory, + deployment.log_directory, channels, train_duration, retrain_cadence, @@ -115,7 +120,7 @@ def main( weights_path = train_on_segment( X, y, - output_directory=output_directory, + deployment=deployment, start=start, architecture=architecture, sample_rate=sample_rate, @@ -123,7 +128,6 @@ def main( verbose=verbose, **kwargs, ) - logger.set_logger("DeepClean train") logger.info( "Training on segment {}-{} complete, saved " @@ -132,6 +136,12 @@ def main( ) ) + logger.info("Making export request") + weights_dir = weights_path.parent.name + r = requests.get(f"http://localhost:5000/export/{weights_dir}") + r.raise_for_status() + logger.info("Export request completed") + # for trainings after the first, use the previous # optimized weights and reduce the learning rate kwargs["init_weights"] = weights_path diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py new file mode 100644 index 0000000..d28e8ee --- /dev/null +++ b/projects/microservice/utils/microservice/deployment.py @@ -0,0 +1,50 @@ +from dataclasses import dataclass +from pathlib import Path + +from microservice.frames import FrameCrawler + + +@dataclass +class Deployment: + root: Path + + def __post_init__(self): + for d in ["log", "train", "frame", "repository"]: + dirname = getattr(self, f"{d}_directory") + dirname.mkdir(exist_ok=True, parents=True) + + @property + def log_directory(self): + return self.root / "logs" + + @property + def train_directory(self): + return self.root / "train" + + @property + def frame_directory(self): + return self.root / "frames" + + @property + def repository_directory(self): + return self.root / "model_repo" + + +@dataclass(frozen=True) +class DataStream: + root: Path + + @property + def detchar(self): + return self.root / "lldetchar" + + @property + def hoft(self): + return self.root / "llhoft" + + def crawl(self, t0, timeout): + crawler = FrameCrawler(self.detchar, t0, timeout) + for fname in crawler: + strain_fname = fname.name.replace("Detchar", "HOFT") + strain_fname = self.hoft / strain_fname + yield fname, strain_fname From 10ce3f347bea687f492e6b1448df51659d377132 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 1 May 2023 19:57:43 -0500 Subject: [PATCH 15/31] getting rid of two frame lookahead during loading --- libs/infer/deepclean/infer/callback.py | 37 +++- libs/infer/deepclean/infer/clean.py | 4 +- libs/trainer/deepclean/trainer/trainer.py | 1 - .../microservice/cleaner/cleaner/cleaner.py | 19 +- .../cleaner/cleaner/dataloader.py | 15 +- .../microservice/cleaner/cleaner/writer.py | 195 ++++++++++++++++-- .../microservice/exporter/exporter/main.py | 1 - projects/microservice/pyproject.toml | 105 +++++----- .../trainer/trainer/dataloader.py | 5 +- .../microservice/trainer/trainer/trainer.py | 42 ++-- .../utils/microservice/deployment.py | 22 +- 11 files changed, 337 insertions(+), 109 deletions(-) diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index fcf1984..4e07226 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -1,10 +1,40 @@ +from dataclasses import dataclass import math import time from typing import Callable, Optional import numpy as np +from scipy.signal import windows from deepclean.logging import logger +from deepclean.utils.filtering import Frequency, BandpassFilter + + +@dataclass +class Cleaner: + kernel_length: float + sample_rate: float + filter_pad: float + freq_low: Frequency = None + freq_high: Frequency = None + + def __post_init__(self): + self.bandpass = BandpassFilter( + self.freq_low, self.freq_high, self.sample_rate + ) + self.pad_size = int(self.filter_pad * self.sample_rate) + self.kernel_size = int(self.kernel_length * self.sample_rate) + self.window = windows.hann(2 * self.pad_size) + + def __call__(self, noise: np.ndarray) -> np.ndarray: + noise[: self.pad_size] *= self.window[: self.pad_size] + noise[-self.pad_size :] *= self.window[-self.pad_size :] + noise = self.bandpass(noise) + + start = -self.pad_size - self.kernel_size + stop = -self.pad_size + noise = noise[start:stop] + return noise class State: @@ -18,6 +48,8 @@ def __init__( inference_sampling_rate: float, batch_size: int, aggregation_steps: int, + freq_low: Frequency, + freq_high: Frequency ) -> None: self.frame_size = int(sample_rate * frame_length) self.stride = int(sample_rate // inference_sampling_rate) @@ -38,6 +70,9 @@ def __init__( self._frame_idx = 0 self._latest_seen = -1 + self.cleaner = Cleaner( + frame_length, sample_rate, filter_pad, freq_low, freq_high + ) self.logger = logger.get_logger(f"Output state {name}") def validate(self, response, request_id): @@ -127,7 +162,7 @@ def update(self, x, request_id): if extra > 0: self._state = self._state[extra:] self._frame_idx += 1 - return noise + return self.cleaner(noise) return None diff --git a/libs/infer/deepclean/infer/clean.py b/libs/infer/deepclean/infer/clean.py index d4d9601..3f59ee7 100644 --- a/libs/infer/deepclean/infer/clean.py +++ b/libs/infer/deepclean/infer/clean.py @@ -22,7 +22,7 @@ def __post_init__(self): self.kernel_size = int(self.kernel_length * self.sample_rate) self.window = windows.hann(2 * self.pad_size) - def __call__(self, noise: np.ndarray, strain: np.ndarray) -> np.ndarray: + def __call__(self, noise: np.ndarray) -> np.ndarray: noise[: self.pad_size] *= self.window[: self.pad_size] noise[-self.pad_size :] *= self.window[-self.pad_size :] noise = self.bandpass(noise) @@ -30,4 +30,4 @@ def __call__(self, noise: np.ndarray, strain: np.ndarray) -> np.ndarray: start = -self.pad_size - self.kernel_size stop = -self.pad_size noise = noise[start:stop] - return strain - noise + return noise diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 0af4772..9e335f6 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -218,7 +218,6 @@ def train( load_weights(init_weights, model, input_scaler) # get rid of any remaining 0s in the preprocessing module input_scaler.std[input_scaler.std == 0] = 1 - print(input_scaler.std) # bandpass the strain up front so that we only # deal with the frequency ranges we care about diff --git a/projects/microservice/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner.py index 9ed35cc..edb3bb9 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/cleaner.py @@ -3,11 +3,10 @@ from typing import Iterable, Optional, Union from cleaner.dataloader import get_data_generators -from cleaner.writer import Writer +from cleaner.writer import Writer, ASDRMonitor from microservice.deployment import Deployment from deepclean.infer.callback import Callback, State -from deepclean.infer.clean import Cleaner from deepclean.logging import logger from deepclean.utils.channels import ChannelList, get_channels from hermes.aeriel.client import InferenceClient @@ -19,6 +18,7 @@ def main( # IO args run_directory: Path, data_directory: Path, + data_field: str, # Data args sample_rate: float, frame_length: float, @@ -135,6 +135,7 @@ def main( witness_it, strain_it = get_data_generators( data_directory, + data_field, channels, sample_rate=sample_rate, inference_sampling_rate=inference_sampling_rate, @@ -166,19 +167,19 @@ def main( inference_sampling_rate=inference_sampling_rate, batch_size=batch_size, aggregation_steps=aggregation_steps, + freq_low=freq_low, + freq_high=freq_high ) states[name] = state - cleaner = Cleaner( - frame_length, - sample_rate, - filter_pad=look_ahead, + write_dir = deployment.frame_directory + monitor = ASDRMonitor( + buffer_length=8, freq_low=freq_low, freq_high=freq_high, + fftlength=2 ) - - write_dir = deployment.frame_directory - writer = Writer(write_dir, strain_it, cleaner, max_files) + writer = Writer(write_dir, strain_it, monitor, max_files) callback = Callback(writer, **states) # finally create an inference client which will stream diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 59a1c40..b6e6a30 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -17,21 +17,22 @@ def witness_iterator(it: Iterable, stride: int) -> np.ndarray: except StopIteration: raise ValueError("Frame crawler never returned any data") - sequence_end, idx = False, 0 - while not sequence_end: + idx = 0 + while True: start = idx * stride stop = (idx + 1) * stride - if (idx + 2) * stride > data.shape[-1]: + if stop > data.shape[-1]: try: frame = next(it) except StopIteration: - sequence_end = True + yield np.zeros((stride,), dtype=np.float32), True + break else: data = data[:, start:] data = np.concatenate([data, frame], axis=-1) idx, start, stop = 0, 0, stride - yield data[:, start:stop], sequence_end + yield data[:, start:stop], False idx += 1 @@ -49,7 +50,6 @@ def strain_iterator(q: Queue): def frame_iter(crawler, channels, sample_rate, q): for fname, strain_fname in crawler: - logger.debug(f"Loading frame file {fname}") witnesses = load_frame(fname, channels[1:], sample_rate) strain = TimeSeries.read(strain_fname, channel=channels[0]) if strain.sample_rate.value != sample_rate: @@ -62,6 +62,7 @@ def frame_iter(crawler, channels, sample_rate, q): def get_data_generators( data_dir: Path, + data_field: str, channels: List[str], sample_rate: float, inference_sampling_rate: float, @@ -70,7 +71,7 @@ def get_data_generators( timeout: float = 10, ): t0 = 0 if start_first else -1 - stream = DataStream(data_dir) + stream = DataStream(data_dir, data_field) crawler = stream.crawl(t0, timeout) strain_q = Queue() it = frame_iter(crawler, channels, sample_rate, strain_q) diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index aa9b90c..1dec189 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -2,51 +2,214 @@ import time from dataclasses import dataclass from pathlib import Path -from typing import Callable, Iterator +from typing import Callable, Iterator, Optional from gwpy.timeseries import TimeSeries, TimeSeriesDict from microservice.frames import lock, parse_frame_name +from scipy.signal.windows import tukey from deepclean.logging import logger +from deepclean.utils.filtering import Frequency, normalize_frequencies + + +@dataclass +class ASDRMonitor: + buffer_length: float + freq_low: Frequency + freq_high: Frequency + fftlength: float = 2 + overlap: Optional[float] = None + + def __post_init__(self): + self.raw, self.clean, self.window = None, None, None + self.freq_low, self.freq_high = normalize_frequencies( + self.freq_low, self.freq_high + ) + self.freq_low = self.freq_low[0] + self.freq_high = self.freq_high[0] + + self.in_spec = True + self.logger = logger.get_logger("DeepClean monitor") + + def get_asd(self, ts: TimeSeries): + asd = ts.asd( + self.fftlength, + overlap=self.overlap, + window="hann", + method="median" + ) + return asd.crop(self.freq_low, self.freq_high) + + def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: + clean = raw - noise + if self.raw is None: + # special short-circuit for the first data we get + self.raw = raw + self.clean = clean + + # build a Tukey window for tapering + # the cleaned data stream in and out + sample_rate = raw.sample_rate.value + size = int(sample_rate * 2) + cutoff = int(sample_rate) + self.window = tukey(size, alpha=1)[-cutoff:] + return None + + self.raw = self.raw.append(raw, inplace=False) + self.clean = self.clean.append(clean, inplace=False) + + # crop off the second to last frame from + # each of our buffers to potentially write + # depending on if quality check are passed + t0, tf = self.raw.span + raw = self.raw.crop(tf - 2, tf - 1) + clean = self.clean.crop(tf - 2, tf - 1) + + # if we don't have enough data to measure the ASDR + # yet, then just return the buffered clean frame + if self.raw.duration.value < self.buffer_length: + return clean + + # check the average ASDR over the relevant + # frequency bins. TODO: do we want to do a + # more in-depth analysis than this? + raw_asd = self.get_asd(self.raw) + clean_asd = self.get_asd(self.clean) + asdr = (clean_asd / raw_asd).value.mean() + + self.logger.debug( + "Mean ASDR value over GPS times {}-{} " + "and frequency range {}-{}Hz is currently {}".format( + t0, tf, self.freq_low, self.freq_high, asdr + ) + ) + + if asdr > 1 and self.in_spec: + # we've gone out of spec, so reconstruct the + # noise from the buffered frame and taper it + # before re-subtracting it, then set the + # in_spec flag so that future frames will + # return the raw data until we've fixed the issue + self.logger.warning( + "Mean ASDR value in frequency range {}-{}Hz " + "is now {}, tapering noise predictions to 0 " + "until ASDR returns below 1".format( + self.freq_low, self.freq_high, asdr + ) + ) + + self.in_spec = False + noise = (raw - clean) * self.window + frame = raw - noise + elif asdr > 1 and not self.in_spec: + # we're currently out of spec and the most recent + # frame hasn't changed that, so continue to + # return the uncleaned data + frame = raw + elif asdr < 1 and not self.in_spec: + # we were out of spec, but the most recent frame + # brought us back in, so taper in the cleaned + # data again starting with the buffered frame + self.logger.info( + "Mean ASDR value in frequency range {}-{}Hz " + "is now {}, tapering noise subtraction back " + "into production data stream".format( + self.freq_low, self.freq_high, asdr + ) + ) + + self.in_spec = True + noise = (raw - clean) * self.window[::-1] + frame = raw - noise + + # finally, if we've reached our buffer length, + # crop off the earliest part of the buffer + if self.raw.duration.value >= self.buffer_length: + self.raw = self.raw.crop(t0 + 1, tf) + self.clean = self.clean.crop(t0 + 1, tf) + + return frame @dataclass class Writer: write_dir: Path - it: Iterator - cleaner: Callable + strain_generator: Iterator + monitor: ASDRMonitor max_files: int def __post_init__(self): self.write_dir.mkdir(parents=True, exist_ok=True) self.logger = logger.get_logger("DeepClean writer") + self.canary_buffer = None + self.timestamp_buffer = None self._files = [] def __call__(self, **predictions): - strain, fname = next(self.it) - timestamp = parse_frame_name(fname.name)[1] + strain, fname = next(self.strain_generator) self.logger.debug(f"Cleaning strain from file {fname}") + sample_rate = strain.sample_rate.value + ifo, field, timestamp, dur = fname.stem.split("-") + timestamp = int(timestamp) + t0 = timestamp - 1 + file_timestamp = os.path.getmtime(fname) + tsd = TimeSeriesDict() for key, noise in predictions.items(): - frame = self.cleaner(noise, strain.value) - + # parse out "canary" or "production" from the + # name of the prediction tensor and use it to + # construct the channel name for this model's + # predictions model = key.split("-")[1] - postfix = "DEEPCLEAN-" + model.upper() - tsd[key] = TimeSeries( - frame, - t0=timestamp, - sample_rate=strain.sample_rate.value, - channel=strain.channel.name + "-" + postfix, + postfix = "DEEPCLEAN_" + model.upper() + channel = strain.channel.name + "_" + postfix + + # the production model needs to be cleaned + # specially to ensure that the ASDR matches + # our expectations, otherwise we'll just return + # the raw frame + if model == "production": + noise = TimeSeries( + noise, + t0=timestamp, + sample_rate=sample_rate + ) + frame = self.monitor(strain, noise) + else: + # for the canary model, always clean normally + clean = strain - noise + frame = self.canary_buffer + self.canary_buffer = clean + + # short circuit for the first frame of each run + if frame is None: + continue + + frame = TimeSeries( + frame.value, + t0=t0, + sample_rate=sample_rate, + channel=channel ) + tsd[channel] = frame + + # same short circuit as above + if not tsd: + # record the timestamp of this first file + # to compute our write latency later + self.timestamp_buffer = file_timestamp + return None - write_path = self.write_dir / fname.name + write_name = f"{ifo}-{field}-{t0}-{dur}.gwf" + write_path = self.write_dir / write_name with lock: tsd.write(write_path) + self.logger.debug(f"Finished cleaning of file {write_path}") write_time = time.time() - tstamp = os.path.getmtime(fname) - latency = write_time - tstamp + latency = write_time - self.timestamp_buffer + self.timestamp_buffer = file_timestamp self._files.append(write_path) if len(self._files) > self.max_files: diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index 14a8c16..0ed684c 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -2,7 +2,6 @@ from flask import Flask exporter = Exporter.from_config() -exporter.export_weights("1251331220-1251333268") app = Flask(__name__) diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 21d76a1..f23c652 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -3,51 +3,54 @@ run_directory = "${PROJECT_DIR}" # global data args -channels = [ +hanford_60Hz_channels = [ "H1:GDS-CALIB_STRAIN", -# "H1:GDS-CALIB_F_S_SQUARED", -# "H1:GDS-CALIB_F_S_SQUARED_NOGATE", -# "H1:GDS-CALIB_SRC_Q_INVERSE", -# "H1:GDS-CALIB_SRC_Q_INVERSE_NOGATE", -# "H1:GDS-CALIB_F_CC", -# "H1:GDS-CALIB_F_CC_NOGATE", -# "H1:GDS-CALIB_KAPPA_C", -# "H1:GDS-CALIB_KAPPA_C_NOGATE", -# "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY", -# "H1:GDS-CALIB_KAPPA_PUM_IMAGINARY_NOGATE", -# "H1:GDS-CALIB_KAPPA_PUM_REAL", -# "H1:GDS-CALIB_KAPPA_PUM_REAL_NOGATE", -# "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY", -# "H1:GDS-CALIB_KAPPA_UIM_IMAGINARY_NOGATE", -# "H1:GDS-CALIB_KAPPA_UIM_REAL", -# "H1:GDS-CALIB_KAPPA_UIM_REAL_NOGATE", -# "H1:GDS-CALIB_KAPPA_TST_IMAGINARY", -# "H1:GDS-CALIB_KAPPA_TST_IMAGINARY_NOGATE", -# "H1:GDS-CALIB_KAPPA_TST_REAL", -# "H1:GDS-CALIB_KAPPA_TST_REAL_NOGATE", -# "H1:GDS-CALIB_STATE_VECTOR", "H1:PEM-CS_MAINSMON_EBAY_1_DQ", - "H1:ASC-INP1_P_INMON", - "H1:ASC-INP1_Y_INMON", - "H1:ASC-MICH_P_INMON", - "H1:ASC-MICH_Y_INMON", - "H1:ASC-PRC1_P_INMON", - "H1:ASC-PRC1_Y_INMON", - "H1:ASC-PRC2_P_INMON", - "H1:ASC-PRC2_Y_INMON", - "H1:ASC-SRC1_P_INMON", - "H1:ASC-SRC1_Y_INMON", - "H1:ASC-SRC2_P_INMON", - "H1:ASC-SRC2_Y_INMON", - "H1:ASC-DHARD_P_INMON", - "H1:ASC-DHARD_Y_INMON", - "H1:ASC-CHARD_P_INMON", - "H1:ASC-CHARD_Y_INMON", - "H1:ASC-DSOFT_P_INMON", - "H1:ASC-DSOFT_Y_INMON", - "H1:ASC-CSOFT_P_INMON", - "H1:ASC-CSOFT_Y_INMON" + "H1:ASC-INP1_P_OUT_DQ", + "H1:ASC-INP1_Y_OUT_DQ", + "H1:ASC-MICH_P_OUT_DQ", + "H1:ASC-MICH_Y_OUT_DQ", + "H1:ASC-PRC1_P_OUT_DQ", + "H1:ASC-PRC1_Y_OUT_DQ", + "H1:ASC-PRC2_P_OUT_DQ", + "H1:ASC-PRC2_Y_OUT_DQ", + "H1:ASC-SRC1_P_OUT_DQ", + "H1:ASC-SRC1_Y_OUT_DQ", + "H1:ASC-SRC2_P_OUT_DQ", + "H1:ASC-SRC2_Y_OUT_DQ", + "H1:ASC-DHARD_P_OUT_DQ", + "H1:ASC-DHARD_Y_OUT_DQ", + "H1:ASC-CHARD_P_OUT_DQ", + "H1:ASC-CHARD_Y_OUT_DQ", + "H1:ASC-DSOFT_P_OUT_DQ", + "H1:ASC-DSOFT_Y_OUT_DQ", + "H1:ASC-CSOFT_P_OUT_DQ", + "H1:ASC-CSOFT_Y_OUT_DQ" ] +livingston_60Hz_channels = [ + "L1:GDS-CALIB_STRAIN", + "L1:PEM-CS_MAINSMON_EBAY_1_DQ", + "L1:ASC-CHARD_P_OUT_DQ", + "L1:ASC-CHARD_Y_OUT_DQ", + "L1:ASC-CSOFT_P_OUT_DQ", + "L1:ASC-DHARD_P_OUT_DQ", + "L1:ASC-DHARD_Y_OUT_DQ", + "L1:ASC-DSOFT_P_OUT_DQ", + "L1:ASC-INP1_P_OUT_DQ", + "L1:ASC-MICH_P_OUT_DQ", + "L1:ASC-MICH_Y_OUT_DQ", + "L1:ASC-PRC1_P_OUT_DQ", + "L1:ASC-PRC1_Y_OUT_DQ", + "L1:ASC-PRC2_P_OUT_DQ", + "L1:ASC-PRC2_Y_OUT_DQ", + "L1:ASC-SRC1_P_OUT_DQ", + "L1:ASC-SRC1_Y_OUT_DQ", + "L1:ASC-SRC2_P_OUT_DQ", + "L1:ASC-SRC2_Y_OUT_DQ", +] +data_directory = "${DATA_DIR}" +data_field = "${DATA_FIELD}" + sample_rate = 4096 freq_low = 55 freq_high = 65 @@ -63,10 +66,11 @@ verbose = false [tool.typeo.scripts.train] # paths run_directory = "${base.run_directory}" -data_directory = "${DATA_DIR}" +data_directory = "${base.data_directory}" +data_field = "${base.data_field}" # data parameters -channels = "${base.channels}" +channels = "${base.livingston_60Hz_channels}" train_duration = 2048 retrain_cadence = 1024 sample_rate = "${base.sample_rate}" @@ -105,7 +109,7 @@ autoencoder = {} run_directory = "${base.run_directory}" # data args -channels = "${base.channels}" +channels = "${base.livingston_60Hz_channels}" kernel_length = 1 sample_rate = "${base.sample_rate}" @@ -123,11 +127,12 @@ instances = 4 [tool.typeo.scripts.clean] # paths run_directory = "${base.run_directory}" -data_directory = "${DATA_DIR}" +data_directory = "${base.data_directory}" +data_field = "${base.data_field}" # data args sample_rate = "${base.sample_rate}" -channels = "${base.channels}" +channels = "${base.livingston_60Hz_channels}" freq_low = "${base.freq_low}" freq_high = "${base.freq_high}" frame_length = 1 @@ -147,7 +152,7 @@ model_version = -1 sequence_id = 1001 # misc args -start_first = true +start_first = false timeout = 3 -verbose = "${base.verbose}" -max_files = 4096 +verbose = true # "${base.verbose}" +max_files = 300 diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index 29a30e5..0d2b5d8 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -18,13 +18,14 @@ def collect_frames( event: Event, data_directory: Path, + data_field: str, channels: ChannelList, duration: float, cadence: float, sample_rate: float, timeout: Optional[float] = None, ) -> np.ndarray: - stream = DataStream(data_directory) + stream = DataStream(data_directory, data_field) crawler = stream.crawl(t0=0, timeout=timeout) channels = get_channels(channels) @@ -78,6 +79,7 @@ def collect_frames( @dataclass class DataCollector: data_directory: Path + data_field: str log_directory: Path channels: ChannelList duration: float @@ -155,6 +157,7 @@ def __call__(self): it = collect_frames( self.event, self.data_directory, + self.data_field, self.channels, self.duration, self.cadence, diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 76c01e0..0923466 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -5,11 +5,12 @@ import requests from microservice.deployment import Deployment from trainer.dataloader import DataCollector +from trainer.monitor import validate_csd from deepclean.architectures import architectures from deepclean.logging import logger from deepclean.trainer.trainer import train -from deepclean.utils.channels import ChannelList +from deepclean.utils.channels import ChannelList, get_channels from typeo import scriptify from typeo.utils import make_dummy @@ -24,6 +25,16 @@ def _get_str(*times): return "-".join(map(_intify, times)) +def export(weights_path: Path): + weights_dir = weights_path.parent.name + url = f"http://localhost:5000/export/{weights_dir}" + logger.info(f"Making export request to {url}") + + r = requests.get(url) + r.raise_for_status() + logger.info("Export request completed") + + def train_on_segment( X: np.ndarray, y: np.ndarray, @@ -74,6 +85,7 @@ def train_on_segment( def main( run_directory: Path, data_directory: Path, + data_field: str, channels: ChannelList, architecture: Callable, train_duration: float, @@ -90,8 +102,10 @@ def main( "DeepClean trainer", log_file, verbose=verbose ) + channels = get_channels(channels) frame_collector = DataCollector( data_directory, + data_field, deployment.log_directory, channels, train_duration, @@ -104,6 +118,12 @@ def main( last_start = None with frame_collector as data_it: for X, y, start in data_it: + span = _get_str(start, start + train_duration) + csd_fname = deployment.csd_directory / f"{span}.h5" + validate_csd( + X, y, channels, sample_rate, fftlength=8, fname=csd_fname + ) + if last_start is not None: expected_start = last_start + retrain_cadence if start > expected_start: @@ -111,12 +131,7 @@ def main( # training differently on a new lock segment pass - root_logger.info( - "Launching training on segment {}-{}".format( - start, start + train_duration - ) - ) - + root_logger.info(f"Launching training on segment {span}") weights_path = train_on_segment( X, y, @@ -130,17 +145,10 @@ def main( ) logger.set_logger("DeepClean train") logger.info( - "Training on segment {}-{} complete, saved " - "optimized weights to {}".format( - start, start + train_duration, weights_path - ) + "Training on segment {} complete, saved " + "optimized weights to {}".format(span, weights_path) ) - - logger.info("Making export request") - weights_dir = weights_path.parent.name - r = requests.get(f"http://localhost:5000/export/{weights_dir}") - r.raise_for_status() - logger.info("Export request completed") + export(weights_path) # for trainings after the first, use the previous # optimized weights and reduce the learning rate diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index d28e8ee..1f833a0 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -1,3 +1,4 @@ +import time from dataclasses import dataclass from pathlib import Path @@ -9,7 +10,7 @@ class Deployment: root: Path def __post_init__(self): - for d in ["log", "train", "frame", "repository"]: + for d in ["log", "train", "csd", "frame", "repository"]: dirname = getattr(self, f"{d}_directory") dirname.mkdir(exist_ok=True, parents=True) @@ -21,6 +22,10 @@ def log_directory(self): def train_directory(self): return self.root / "train" + @property + def csd_directory(self): + return self.train_directory / "csds" + @property def frame_directory(self): return self.root / "frames" @@ -33,18 +38,27 @@ def repository_directory(self): @dataclass(frozen=True) class DataStream: root: Path + field: str @property def detchar(self): - return self.root / "lldetchar" + return self.root / "lldetchar" / self.field @property def hoft(self): - return self.root / "llhoft" + return self.root / "kafka" / self.field def crawl(self, t0, timeout): crawler = FrameCrawler(self.detchar, t0, timeout) for fname in crawler: - strain_fname = fname.name.replace("Detchar", "HOFT") + strain_fname = fname.name.replace("lldetchar", "llhoft") strain_fname = self.hoft / strain_fname + + start_time = time.time() + while not strain_fname.exists(): + if (time.time() - start_time) > 5: + raise FileNotFoundError( + f"Strain file {strain_fname} does not exist" + ) + yield fname, strain_fname From 36569f4534ce0f5cef8342f10b3bcbda6dc0790d Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 3 May 2023 17:59:42 -0500 Subject: [PATCH 16/31] getting full end-to-end pipeline running --- .pre-commit-config.yaml | 2 +- libs/export/deepclean/export/model.py | 5 +- libs/export/deepclean/export/repo.py | 12 ++-- libs/infer/deepclean/infer/callback.py | 6 +- libs/infer/deepclean/infer/clean.py | 2 +- libs/logging/deepclean/logging.py | 12 +++- libs/trainer/deepclean/trainer/trainer.py | 9 ++- .../microservice/cleaner/cleaner/cleaner.py | 63 ++++++++++++++++--- .../cleaner/cleaner/dataloader.py | 1 + .../microservice/cleaner/cleaner/writer.py | 19 +++--- .../microservice/exporter/exporter/main.py | 4 +- .../exporter/tests/test_exporter.py | 10 ++- projects/microservice/pyproject.toml | 1 - .../microservice/trainer/trainer/trainer.py | 9 +++ .../utils/microservice/deployment.py | 2 +- 15 files changed, 108 insertions(+), 49 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 169f57a..42b0000 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,6 +1,6 @@ repos: - repo: https://github.com/pycqa/isort - rev: 5.8.0 + rev: 5.12.0 hooks: - id: isort name: isort (python) diff --git a/libs/export/deepclean/export/model.py b/libs/export/deepclean/export/model.py index fd41661..5a87ac6 100644 --- a/libs/export/deepclean/export/model.py +++ b/libs/export/deepclean/export/model.py @@ -1,4 +1,5 @@ import torch + from ml4gw.transforms import ChannelWiseScaler @@ -25,7 +26,7 @@ def __init__( self, preprocessor: ChannelWiseScaler, deepclean: torch.nn.Module, - postprocessor: ChannelWiseScaler + postprocessor: ChannelWiseScaler, ) -> None: super().__init__() self.preprocessor = Preprocessor(preprocessor) @@ -36,4 +37,4 @@ def forward(self, X: torch.Tensor) -> torch.Tensor: X = self.preprocessor(X) X = self.deepclean(X) X = self.postprocessor(X) - return X \ No newline at end of file + return X diff --git a/libs/export/deepclean/export/repo.py b/libs/export/deepclean/export/repo.py index df58204..404e0e4 100644 --- a/libs/export/deepclean/export/repo.py +++ b/libs/export/deepclean/export/repo.py @@ -1,13 +1,11 @@ from typing import List, Optional -from hermes import quiver as qv from tritonclient.grpc import model_config_pb2 as model_config +from hermes import quiver as qv + -def configure_stateful( - config: model_config.ModelConfig, - idle_time: float -): +def configure_stateful(config: model_config.ModelConfig, idle_time: float): idle_time = int(idle_time * 10**6) config.sequence_batching.max_sequence_idle_microseconds = idle_time @@ -17,7 +15,7 @@ def initialize_repo( channels: List[str], platform: qv.Platform, instances: Optional[int] = None, - clean: bool = False + clean: bool = False, ): repo = qv.ModelRepository(repo_dir, clean=clean) try: @@ -31,4 +29,4 @@ def initialize_repo( deepclean.config.scale_instance_group(count=instances) deepclean.config.parameters["channels"].string_value = ",".join(channels) - return repo \ No newline at end of file + return repo diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index 4e07226..53a9430 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -1,13 +1,13 @@ -from dataclasses import dataclass import math import time +from dataclasses import dataclass from typing import Callable, Optional import numpy as np from scipy.signal import windows from deepclean.logging import logger -from deepclean.utils.filtering import Frequency, BandpassFilter +from deepclean.utils.filtering import BandpassFilter, Frequency @dataclass @@ -49,7 +49,7 @@ def __init__( batch_size: int, aggregation_steps: int, freq_low: Frequency, - freq_high: Frequency + freq_high: Frequency, ) -> None: self.frame_size = int(sample_rate * frame_length) self.stride = int(sample_rate // inference_sampling_rate) diff --git a/libs/infer/deepclean/infer/clean.py b/libs/infer/deepclean/infer/clean.py index 3f59ee7..4c50cae 100644 --- a/libs/infer/deepclean/infer/clean.py +++ b/libs/infer/deepclean/infer/clean.py @@ -3,7 +3,7 @@ import numpy as np from scipy.signal import windows -from deepclean.utils.filtering import Frequency, BandpassFilter +from deepclean.utils.filtering import BandpassFilter, Frequency @dataclass diff --git a/libs/logging/deepclean/logging.py b/libs/logging/deepclean/logging.py index bdfd887..3367f98 100644 --- a/libs/logging/deepclean/logging.py +++ b/libs/logging/deepclean/logging.py @@ -17,7 +17,7 @@ def configure_logger( logger, filename: Optional[str] = None, verbose: Optional[bool] = None, - add_stdout: bool = False + add_stdout: bool = False, ): if verbose is None: level = self._logger.level @@ -43,7 +43,7 @@ def get_logger( name: str, filename: Optional[str] = None, verbose: Optional[bool] = None, - add_stdout: bool = False + add_stdout: bool = False, ): logger = logging.getLogger(name) if name in self._cache: @@ -76,3 +76,11 @@ def __getattr__(self, name): logger = Logger() + + +def excepthook(exc_type, exc_value, exc_tb): + exc_info = exc_type, exc_value, exc_tb + logger.error("Unhandled exception:", exc_info=exc_info) + + +sys.excepthook = excepthook diff --git a/libs/trainer/deepclean/trainer/trainer.py b/libs/trainer/deepclean/trainer/trainer.py index 9e335f6..e299ced 100644 --- a/libs/trainer/deepclean/trainer/trainer.py +++ b/libs/trainer/deepclean/trainer/trainer.py @@ -4,8 +4,8 @@ import numpy as np import torch -from deepclean.logging import logger from deepclean.export.model import DeepClean +from deepclean.logging import logger from deepclean.trainer import CompositePSDLoss from deepclean.trainer.analysis import analyze_model from deepclean.trainer.utils import Checkpointer, Trainer @@ -27,9 +27,7 @@ def get_weights(state_dict: Dict, prefix: str): def load_weights( - init_weights: Path, - model: torch.nn.Module, - preprocessor: ChannelWiseScaler + init_weights: Path, model: torch.nn.Module, preprocessor: ChannelWiseScaler ): logger.info(f"Initializing model from checkpoint '{init_weights}'") state_dict = torch.load(init_weights) @@ -43,7 +41,8 @@ def load_weights( mask = preprocessor.std == 0 for param_name in ["mean", "std"]: param = getattr(preprocessor, param_name) - param[mask] = preproc_params[param_name][mask] + key = f"scaler.{param_name}" + param[mask] = preproc_params[key][mask] def train( diff --git a/projects/microservice/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/cleaner.py index edb3bb9..fab2029 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/cleaner.py @@ -2,9 +2,11 @@ from pathlib import Path from typing import Iterable, Optional, Union +import tritonclient.grpc as triton from cleaner.dataloader import get_data_generators -from cleaner.writer import Writer, ASDRMonitor +from cleaner.writer import ASDRMonitor, Writer from microservice.deployment import Deployment +from tritonclient.utils import InferenceServerException from deepclean.infer.callback import Callback, State from deepclean.logging import logger @@ -13,6 +15,53 @@ from typeo import scriptify +def wait(url): + client = triton.InferenceServerClient(url) + + # first wait for server to come online + start_time = time.time() + while True: + try: + live = client.is_server_live() + except InferenceServerException: + time.sleep(1) + else: + if live: + break + + elapsed = (time.time() - start_time) // 1 + if not elapsed % 10: + logger.info( + f"Waiting for server to come online, {elapsed}s elapsed" + ) + + start_time = time.time() + while not client.is_model_ready("deepclean-stream"): + time.sleep(1) + + elapsed = (time.time() - start_time) // 1 + if not elapsed % 10: + logger.info( + "Waiting for streaming model to " + f"come online, {elapsed}s elapsed" + ) + + start_time = time.time() + while True: + metadata = client.get_model_metadata("deepclean") + versions = list(map(int, metadata.versions)) + if max(versions) > 1: + break + + time.sleep(1) + elapsed = (time.time() - start_time) // 1 + if not elapsed % 10: + logger.info( + "Waiting for first DeepClean model to " + f"come online, {elapsed}s elapsed" + ) + + @scriptify def main( # IO args @@ -31,7 +80,6 @@ def main( # Triton args url: str, model_name: str, - model_version: int = -1, sequence_id: int = 1001, # Misc args max_latency: Optional[float] = None, @@ -133,6 +181,8 @@ def main( logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) + wait(url) + witness_it, strain_it = get_data_generators( data_directory, data_field, @@ -168,16 +218,13 @@ def main( batch_size=batch_size, aggregation_steps=aggregation_steps, freq_low=freq_low, - freq_high=freq_high + freq_high=freq_high, ) states[name] = state write_dir = deployment.frame_directory monitor = ASDRMonitor( - buffer_length=8, - freq_low=freq_low, - freq_high=freq_high, - fftlength=2 + buffer_length=8, freq_low=freq_low, freq_high=freq_high, fftlength=2 ) writer = Writer(write_dir, strain_it, monitor, max_files) callback = Callback(writer, **states) @@ -189,7 +236,7 @@ def main( client = InferenceClient( address=url, model_name=model_name, - model_version=model_version, + model_version=-1, callback=callback, ) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index b6e6a30..64408e0 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -50,6 +50,7 @@ def strain_iterator(q: Queue): def frame_iter(crawler, channels, sample_rate, q): for fname, strain_fname in crawler: + logger.debug(f"Loading frame files {fname}, {strain_fname}") witnesses = load_frame(fname, channels[1:], sample_rate) strain = TimeSeries.read(strain_fname, channel=channels[0]) if strain.sample_rate.value != sample_rate: diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index 1dec189..a6431ed 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -2,10 +2,10 @@ import time from dataclasses import dataclass from pathlib import Path -from typing import Callable, Iterator, Optional +from typing import Iterator, Optional from gwpy.timeseries import TimeSeries, TimeSeriesDict -from microservice.frames import lock, parse_frame_name +from microservice.frames import lock from scipy.signal.windows import tukey from deepclean.logging import logger @@ -36,7 +36,7 @@ def get_asd(self, ts: TimeSeries): self.fftlength, overlap=self.overlap, window="hann", - method="median" + method="median", ) return asd.crop(self.freq_low, self.freq_high) @@ -114,13 +114,15 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: "Mean ASDR value in frequency range {}-{}Hz " "is now {}, tapering noise subtraction back " "into production data stream".format( - self.freq_low, self.freq_high, asdr + self.freq_low, self.freq_high, asdr ) ) self.in_spec = True noise = (raw - clean) * self.window[::-1] frame = raw - noise + else: + frame = raw - noise # finally, if we've reached our buffer length, # crop off the earliest part of the buffer @@ -171,9 +173,7 @@ def __call__(self, **predictions): # the raw frame if model == "production": noise = TimeSeries( - noise, - t0=timestamp, - sample_rate=sample_rate + noise, t0=timestamp, sample_rate=sample_rate ) frame = self.monitor(strain, noise) else: @@ -187,10 +187,7 @@ def __call__(self, **predictions): continue frame = TimeSeries( - frame.value, - t0=t0, - sample_rate=sample_rate, - channel=channel + frame.value, t0=t0, sample_rate=sample_rate, channel=channel ) tsd[channel] = frame diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index 0ed684c..34a2778 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -9,9 +9,11 @@ def export_model(train_dir): app.logger.info(f"Received export request for {train_dir}") exporter.export_weights(train_dir) + return "", 200 @app.route("/increment") def increment_ensemble(): app.logger.info("Received ensemble version increment request") - exporter.update_ensemble_version() + exporter.update_ensemble_versions() + return "", 200 diff --git a/projects/microservice/exporter/tests/test_exporter.py b/projects/microservice/exporter/tests/test_exporter.py index 58b2062..367a2e3 100644 --- a/projects/microservice/exporter/tests/test_exporter.py +++ b/projects/microservice/exporter/tests/test_exporter.py @@ -1,8 +1,6 @@ from exporter.exporter import Exporter - -import pytest -from tritonclient.grpc import model_config_pb2 as model_config from google.protobuf import text_format +from tritonclient.grpc import model_config_pb2 as model_config def load_config(model): @@ -15,7 +13,7 @@ def load_config(model): class TestExporter: def test_init(self, tmp_path): tmp_path.mkdir(exist_ok=True, parents=True) - exporter = Exporter( + _ = Exporter( tmp_path, channels=list("DCAEB"), kernel_length=1, @@ -24,7 +22,7 @@ def test_init(self, tmp_path): batch_size=128, aggregation_time=0.25, streams_per_gpu=2, - instances=4 + instances=4, ) deepclean_path = tmp_path / "deepclean" @@ -37,4 +35,4 @@ def test_init(self, tmp_path): assert deepclean_config.instance_group[0].count == 4 assert deepclean_config.parameters["channels"].string_value == ( "A,B,C,E" - ) \ No newline at end of file + ) diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index f23c652..f920b32 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -148,7 +148,6 @@ max_latency = "${base.inference_aggregation_time}" # triton args url = "localhost:8001" model_name = "deepclean-stream" -model_version = -1 sequence_id = 1001 # misc args diff --git a/projects/microservice/trainer/trainer/trainer.py b/projects/microservice/trainer/trainer/trainer.py index 0923466..3d843ed 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/trainer.py @@ -35,6 +35,13 @@ def export(weights_path: Path): logger.info("Export request completed") +def increment(): + logger.info("Incrementing production DeepClean version") + r = requests.get("http://localhost:5000/increment") + r.raise_for_status() + logger.info("Version incrementing complete") + + def train_on_segment( X: np.ndarray, y: np.ndarray, @@ -149,6 +156,8 @@ def main( "optimized weights to {}".format(span, weights_path) ) export(weights_path) + if last_start is None: + increment() # for trainings after the first, use the previous # optimized weights and reduce the learning rate diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index 1f833a0..89b9455 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -56,7 +56,7 @@ def crawl(self, t0, timeout): start_time = time.time() while not strain_fname.exists(): - if (time.time() - start_time) > 5: + if (time.time() - start_time) > 10: raise FileNotFoundError( f"Strain file {strain_fname} does not exist" ) From 71cf27ffa98d7d90739b6aac17e0883d56618c18 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 3 May 2023 18:06:16 -0500 Subject: [PATCH 17/31] adding training monitor code and Dockerfile --- projects/microservice/Dockerfile | 7 ++++ .../microservice/trainer/trainer/monitor.py | 33 +++++++++++++++++++ 2 files changed, 40 insertions(+) create mode 100644 projects/microservice/Dockerfile create mode 100644 projects/microservice/trainer/trainer/monitor.py diff --git a/projects/microservice/Dockerfile b/projects/microservice/Dockerfile new file mode 100644 index 0000000..c4ce89e --- /dev/null +++ b/projects/microservice/Dockerfile @@ -0,0 +1,7 @@ +FROM ghcr.io/ml4gw/pinto:latest +ENV DEEPCLEAN=/opt/deepclean +COPY . $DEEPCLEAN + +ARG project +ENV FLASK_APP=$DEEPCLEAN/projects/microservice/$project/$project +RUN pinto -p $DEEPCLEAN/projects/microservice/$project build diff --git a/projects/microservice/trainer/trainer/monitor.py b/projects/microservice/trainer/trainer/monitor.py new file mode 100644 index 0000000..f27fb8e --- /dev/null +++ b/projects/microservice/trainer/trainer/monitor.py @@ -0,0 +1,33 @@ +from pathlib import Path +from typing import List + +import h5py +import numpy as np +from gwpy.timeseries import TimeSeries + +from deepclean.logging import logger + + +def validate_csd( + X: np.ndarray, + y: np.ndarray, + channels: List[str], + sample_rate: float, + fftlength: float, + fname: Path, +): + logger.info( + "Evaluating CSD of strain channel with witnesses " + "and saving outputs to {}".format(fname) + ) + + y = TimeSeries(y, sample_rate=sample_rate) + with h5py.File(fname, "w") as f: + f.attrs["sample_rate"] = sample_rate + f.attrs["df"] = 1 / fftlength + csds = f.create_group("csd") + + for x, channel in zip(X, channels[1:]): + x = TimeSeries(x, sample_rate=sample_rate) + csd = y.csd(x, fftlength=fftlength, window="hann") + csds[channel] = csd.value From 91f14b021ef4778464a4dff62a9ccc3c99a54b44 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 9 May 2023 11:59:27 -0500 Subject: [PATCH 18/31] adding monitoring project and refactoring things for better interaction --- projects/microservice/Dockerfile | 4 +- projects/microservice/cleaner/Dockerfile | 7 - .../cleaner/cleaner/{cleaner.py => main.py} | 12 +- .../microservice/cleaner/cleaner/writer.py | 119 +- projects/microservice/cleaner/pyproject.toml | 4 +- .../exporter/exporter/exporter.py | 6 + .../microservice/exporter/exporter/main.py | 14 + .../microservice/monitor/monitor/__init__.py | 0 projects/microservice/monitor/monitor/main.py | 157 ++ projects/microservice/monitor/poetry.lock | 310 +++ .../{utils => monitor}/poetry.toml | 0 projects/microservice/monitor/pyproject.toml | 20 + projects/microservice/pyproject.toml | 4 +- projects/microservice/trainer/poetry.lock | 1698 +++++++++++++++-- projects/microservice/trainer/pyproject.toml | 2 +- .../trainer/trainer/{trainer.py => main.py} | 8 +- .../utils/microservice/deployment.py | 56 +- .../microservice/utils/microservice/frames.py | 32 +- projects/microservice/utils/poetry.lock | 165 +- projects/microservice/utils/pyproject.toml | 2 + 20 files changed, 2405 insertions(+), 215 deletions(-) delete mode 100644 projects/microservice/cleaner/Dockerfile rename projects/microservice/cleaner/cleaner/{cleaner.py => main.py} (95%) create mode 100644 projects/microservice/monitor/monitor/__init__.py create mode 100644 projects/microservice/monitor/monitor/main.py create mode 100644 projects/microservice/monitor/poetry.lock rename projects/microservice/{utils => monitor}/poetry.toml (100%) create mode 100644 projects/microservice/monitor/pyproject.toml rename projects/microservice/trainer/trainer/{trainer.py => main.py} (95%) diff --git a/projects/microservice/Dockerfile b/projects/microservice/Dockerfile index c4ce89e..7399a30 100644 --- a/projects/microservice/Dockerfile +++ b/projects/microservice/Dockerfile @@ -3,5 +3,5 @@ ENV DEEPCLEAN=/opt/deepclean COPY . $DEEPCLEAN ARG project -ENV FLASK_APP=$DEEPCLEAN/projects/microservice/$project/$project -RUN pinto -p $DEEPCLEAN/projects/microservice/$project build +ENV PROJECT_DIR=$DEEPCLEAN/projects/microservice/$project +RUN pinto -p $PROJECT_DIR build diff --git a/projects/microservice/cleaner/Dockerfile b/projects/microservice/cleaner/Dockerfile deleted file mode 100644 index fd6ebf5..0000000 --- a/projects/microservice/cleaner/Dockerfile +++ /dev/null @@ -1,7 +0,0 @@ -FROM ghcr.io/ml4gw/pinto:main - -ENV DEEPCLEAN=/opt/deepclean -ADD . $DEEPCLEAN -WORKDIR $DEEPCLEAN/projects/microservice/cleaner - -RUN pinto build . diff --git a/projects/microservice/cleaner/cleaner/cleaner.py b/projects/microservice/cleaner/cleaner/main.py similarity index 95% rename from projects/microservice/cleaner/cleaner/cleaner.py rename to projects/microservice/cleaner/cleaner/main.py index fab2029..202c454 100644 --- a/projects/microservice/cleaner/cleaner/cleaner.py +++ b/projects/microservice/cleaner/cleaner/main.py @@ -68,6 +68,7 @@ def main( run_directory: Path, data_directory: Path, data_field: str, + export_endpoint: str, # Data args sample_rate: float, frame_length: float, @@ -78,7 +79,7 @@ def main( freq_low: Union[float, Iterable[float]], freq_high: Union[float, Iterable[float]], # Triton args - url: str, + triton_endpoint: str, model_name: str, sequence_id: int = 1001, # Misc args @@ -87,7 +88,6 @@ def main( timeout: Optional[float] = None, memory: float = 10, look_ahead: float = 0.5, - max_files: int = 300, verbose: bool = False, ) -> None: """ @@ -181,8 +181,7 @@ def main( logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) - wait(url) - + wait(triton_endpoint) witness_it, strain_it = get_data_generators( data_directory, data_field, @@ -193,6 +192,7 @@ def main( start_first=start_first, timeout=timeout, ) + # now create a file writer which takes the # responses returned by the server and turns them # into a timeseries of noise predictions which can @@ -226,7 +226,7 @@ def main( monitor = ASDRMonitor( buffer_length=8, freq_low=freq_low, freq_high=freq_high, fftlength=2 ) - writer = Writer(write_dir, strain_it, monitor, max_files) + writer = Writer(write_dir, strain_it, monitor, export_endpoint) callback = Callback(writer, **states) # finally create an inference client which will stream @@ -234,7 +234,7 @@ def main( # inference server and pass the responses to the writer # as a callback in a separate thread client = InferenceClient( - address=url, + address=triton_endpoint, model_name=model_name, model_version=-1, callback=callback, diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index a6431ed..66d6089 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -5,7 +5,8 @@ from typing import Iterator, Optional from gwpy.timeseries import TimeSeries, TimeSeriesDict -from microservice.frames import lock +from microservice.deployment import ExportClient +from microservice.frames import DataProducts from scipy.signal.windows import tukey from deepclean.logging import logger @@ -28,7 +29,9 @@ def __post_init__(self): self.freq_low = self.freq_low[0] self.freq_high = self.freq_high[0] - self.in_spec = True + # always start out of spec until we can verify + # that our cleaned data is good + self.in_spec = False self.logger = logger.get_logger("DeepClean monitor") def get_asd(self, ts: TimeSeries): @@ -53,7 +56,7 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: size = int(sample_rate * 2) cutoff = int(sample_rate) self.window = tukey(size, alpha=1)[-cutoff:] - return None + return None, None self.raw = self.raw.append(raw, inplace=False) self.clean = self.clean.append(clean, inplace=False) @@ -66,9 +69,9 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: clean = self.clean.crop(tf - 2, tf - 1) # if we don't have enough data to measure the ASDR - # yet, then just return the buffered clean frame + # yet, then just return the buffered raw data if self.raw.duration.value < self.buffer_length: - return clean + return raw, raw # check the average ASDR over the relevant # frequency bins. TODO: do we want to do a @@ -122,7 +125,7 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: noise = (raw - clean) * self.window[::-1] frame = raw - noise else: - frame = raw - noise + frame = clean # finally, if we've reached our buffer length, # crop off the earliest part of the buffer @@ -130,7 +133,7 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: self.raw = self.raw.crop(t0 + 1, tf) self.clean = self.clean.crop(t0 + 1, tf) - return frame + return frame, clean @dataclass @@ -138,18 +141,26 @@ class Writer: write_dir: Path strain_generator: Iterator monitor: ASDRMonitor - max_files: int + export_endpoint: str def __post_init__(self): self.write_dir.mkdir(parents=True, exist_ok=True) self.logger = logger.get_logger("DeepClean writer") + self.export_client = ExportClient(self.export_endpoint) + self.strain_buffer = None self.canary_buffer = None self.timestamp_buffer = None - self._files = [] def __call__(self, **predictions): strain, fname = next(self.strain_generator) - self.logger.debug(f"Cleaning strain from file {fname}") + + # check which version of the model cleaned this frame + # TODO: incorporate this as metadata somehow + version = self.export_client.production_version() + self.logger.info( + "Cleaning strain from file {} using predictions" + "made by DeepClean version {}".format(fname, version) + ) sample_rate = strain.sample_rate.value ifo, field, timestamp, dur = fname.stem.split("-") @@ -157,41 +168,72 @@ def __call__(self, **predictions): t0 = timestamp - 1 file_timestamp = os.path.getmtime(fname) + data_products = DataProducts(strain.channel.name) tsd = TimeSeriesDict() for key, noise in predictions.items(): # parse out "canary" or "production" from the - # name of the prediction tensor and use it to - # construct the channel name for this model's - # predictions + # name of the prediction tensor model = key.split("-")[1] - postfix = "DEEPCLEAN_" + model.upper() - channel = strain.channel.name + "_" + postfix - - # the production model needs to be cleaned - # specially to ensure that the ASDR matches - # our expectations, otherwise we'll just return - # the raw frame - if model == "production": + if model == "production" and version == 1: + # if the production model is still the + # randomly initialized version of DeepClean, + # then ignore the noise prediction altogether + # and just write the plain strain data + if self.strain_buffer is None: + continue + tsd[data_products.out_dq] = TimeSeries( + strain.value, + t0=t0, + sample_rate=sample_rate, + channel=data_products.out_dq, + ) + tsd[data_products.production] = TimeSeries( + strain.value, + t0=t0, + sample_rate=sample_rate, + channel=data_products.production, + ) + elif model == "production": + # the production model needs to be cleaned + # specially to ensure that the ASDR matches + # our expectations, otherwise we'll just return + # the raw frame noise = TimeSeries( noise, t0=timestamp, sample_rate=sample_rate ) - frame = self.monitor(strain, noise) - else: + dq, clean = self.monitor(strain, noise) + if clean is None: + continue + + tsd[data_products.out_dq] = TimeSeries( + dq.value, + t0=t0, + sample_rate=sample_rate, + channel=data_products.out_dq, + ) + tsd[data_products.production] = TimeSeries( + clean.value, + t0=t0, + sample_rate=sample_rate, + channel=data_products.production, + ) + elif model == "canary": # for the canary model, always clean normally clean = strain - noise - frame = self.canary_buffer + if self.canary_buffer is not None: + tsd[data_products.canary] = TimeSeries( + self.canary_buffer.value, + t0=t0, + sample_rate=sample_rate, + channel=data_products.canary, + ) self.canary_buffer = clean + else: + raise ValueError("Unrecognized model {model}") - # short circuit for the first frame of each run - if frame is None: - continue - - frame = TimeSeries( - frame.value, t0=t0, sample_rate=sample_rate, channel=channel - ) - tsd[channel] = frame - - # same short circuit as above + # short circuit if we didn't add anything + # to our timeseries dict + self.strain_buffer = strain if not tsd: # record the timestamp of this first file # to compute our write latency later @@ -200,18 +242,11 @@ def __call__(self, **predictions): write_name = f"{ifo}-{field}-{t0}-{dur}.gwf" write_path = self.write_dir / write_name - with lock: - tsd.write(write_path) + tsd.write(write_path) self.logger.debug(f"Finished cleaning of file {write_path}") write_time = time.time() latency = write_time - self.timestamp_buffer self.timestamp_buffer = file_timestamp - self._files.append(write_path) - if len(self._files) > self.max_files: - fname = self._files.pop(0) - self.logger.info(f"Removing file {fname} from replay directory") - fname.unlink() # TODO: write these somewhere else? - return write_path, latency diff --git a/projects/microservice/cleaner/pyproject.toml b/projects/microservice/cleaner/pyproject.toml index c8b51ee..ab1b3ec 100644 --- a/projects/microservice/cleaner/pyproject.toml +++ b/projects/microservice/cleaner/pyproject.toml @@ -6,15 +6,15 @@ authors = ["Alec Gunny "] # readme = "README.md" [tool.poetry.scripts] -clean = "cleaner.cleaner:main" +clean = "cleaner.main:main" [tool.poetry.dependencies] python = "^3.8,<3.11" typeo = {path = "../../../typeo", develop = true} microservice = {path = "../utils", develop = true} -"deepclean.logging" = {path = "../../../libs/logging", develop = true} "deepclean.infer" = {path = "../../../libs/infer", develop = true} +"deepclean.logging" = {path = "../../../libs/logging", develop = true} "deepclean.utils" = {path = "../../../libs/utils", develop = true} [tool.poetry.dependencies."hermes.aeriel"] diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index 21ab99f..95f7615 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -209,6 +209,12 @@ def postprocessor(self): def deepclean(self): return self.model.deepclean + def get_production_version(self): + ensemble = self.repo.models["deepclean-stream"] + for step in ensemble.config.steps: + if step.model_name == "deepclean": + return step.model_version + def update_ensemble_versions(self, version: Optional[int] = None): ensemble = self.repo.models["deepclean-stream"] for step in ensemble.config.steps: diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index 34a2778..ff2c44d 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -17,3 +17,17 @@ def increment_ensemble(): app.logger.info("Received ensemble version increment request") exporter.update_ensemble_versions() return "", 200 + + +@app.route("/production-version") +def get_production_version(): + version = exporter.get_production_version() + app.logger.info(f"Reporting production DeepClean version {version}") + return str(version), 200 + + +@app.route("/latest-version") +def get_latest_version(): + version = max(exporter.repo.models["deepclean"].versions) + app.logger.info(f"Reporting latest DeepClean version {version}") + return str(version), 200 diff --git a/projects/microservice/monitor/monitor/__init__.py b/projects/microservice/monitor/monitor/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py new file mode 100644 index 0000000..d91e156 --- /dev/null +++ b/projects/microservice/monitor/monitor/main.py @@ -0,0 +1,157 @@ +import time +from pathlib import Path + +from gwpy.timeseries import TimeSeriesDict +from microservice.deployment import DataStream, Deployment, ExportClient +from microservice.frames import DataProducts, FrameCrawler, load_frame + +from deepclean.logging import logger +from deepclean.utils.channels import ChannelList, get_channels +from deepclean.utils.filtering import Frequency, normalize_frequencies +from typeo import scriptify + + +@scriptify +def main( + data_directory: Path, + data_field: str, + run_directory: Path, + export_endpoint: str, + asd_segment_length: float, + monitor_period: float, + freq_low: Frequency, + freq_high: Frequency, + max_files: int, + write_cadence: int, + channels: ChannelList, + sample_rate: float, + fftlength: float = 8, + verbose: bool = False, +): + deployment = Deployment(run_directory) + log_file = deployment.log_directory / "monitor.log" + logger.set_logger("DeepClean infer", log_file, verbose) + export_client = ExportClient(export_endpoint) + + channels = get_channels(channels) + strain_channel = channels[0] + data_products = DataProducts(strain_channel) + + freq_low, freq_high = normalize_frequencies(freq_low, freq_high) + freq_low, freq_high = freq_low[0], freq_high[0] + + # wait for inference service to begin writing frames + start_time = time.time() + while not list(deployment.frame_directory.iterdir()): + time.sleep(1) + div, mod = divmod(time.time() - start_time, 10) + if not mod: + logger.info( + "Waiting for first frame to be written, " + "{}s elapsed".format(div) + ) + + # now start iterating through written frames and + # 1. Periodically move them to larger files in cold storage + # 2. Remove files to keep frame directory at fixed max length + # 3. Monitor frames cleaned by the canary model to ensure that + # it's in spec before moving it into production + stream = DataStream(data_directory, data_field) + files = [] + buffer, t0 = TimeSeriesDict(), None + + latest_version = 1 + validating = False + in_spec_for = 0 + + for fname in FrameCrawler(deployment.frame_directory, t0=None): + ifo, field, timestamp, *_ = fname.stem.split("-") + timestamp = int(timestamp) + if t0 is None: + t0 = timestamp + + # grab all of the data products from the current + # frame as well as its associate raw data + strain_fname = stream.hoft / fname.name + frame = load_frame(fname, data_products.channels, sample_rate) + raw = load_frame(strain_fname, strain_channel, sample_rate) + + frame[strain_channel] = raw + buffer.append(frame) + + # see if we've aggregated enough data to move + # into cold storage in our output directory + if (timestamp - t0) == write_cadence: + # slice out the current segment for writing, ignoring + # any data from the past that we might have had to hold + # on to for canary validation + output = buffer.crop(t0, timestamp) + write_fname = f"{ifo}-{field}-{output.t0}-{write_cadence}.h5" + write_path = deployment.storage_directory / write_fname + + logger.info( + "Accumulated {}s of frame data, writing to {}".format( + write_cadence, write_path + ) + ) + output.write(write_path) + + # now slough off the data from the frame we just + # wrote, saving just enough to be able to keep + # doing canary model validation. Increment t0 to mathc + start = timestamp - asd_segment_length + buffer = buffer.crop(start, timestamp) + t0 = timestamp + + files.append(fname) + if len(files) > max_files: + removed = files.pop(0) + logger.info(f"Removing frame file {removed}") + removed.unlink() + + # if we don't have enough data to validate the ASD, move on + if buffer.duration < asd_segment_length: + continue + + # if we're not currently validating, see if there is a + # newer version of the model that needs validation + if not validating: + version = export_client.latest_version() + if version > latest_version: + latest_version = version + validating = True + in_spec_for = 0 + else: + # if we're still on the same version, move on + continue + + # crop out the last asd_segment_length seconds worth + # of data from both the raw and canary-cleaned timeseries. + # Compute their ASDs over the relevant frequency range + start = timestamp - asd_segment_length + + raw = buffer[strain_channel].crop(start, timestamp) + raw = raw.asd(fftlength, method="median", window="hann") + raw = raw.crop(freq_low, freq_high) + + clean = buffer[data_products.canary].crop(start, timestamp) + clean = clean.asd(fftlength, method="median", window="hann") + clean = clean.crop(freq_low, freq_high) + + # measure the ratio of these ASDs and increment + # our counter if it's in spec. + # TODO: write this to some sort of Prometheus + # dashboard or something + asdr = clean / raw + if asdr.mean() <= 1: + in_spec_for += 1 + else: + in_spec_for = 0 + + # if we've been in spec for the right period, + # then increment the production version of + # DeepClean and reset our validation counter + if in_spec_for >= monitor_period: + export_client.increment() + validating = False + in_spec_for = 0 diff --git a/projects/microservice/monitor/poetry.lock b/projects/microservice/monitor/poetry.lock new file mode 100644 index 0000000..e1ec4b5 --- /dev/null +++ b/projects/microservice/monitor/poetry.lock @@ -0,0 +1,310 @@ +[[package]] +name = "certifi" +version = "2023.5.7" +description = "Python package for providing Mozilla's CA Bundle." +category = "main" +optional = false +python-versions = ">=3.6" + +[[package]] +name = "charset-normalizer" +version = "3.1.0" +description = "The Real First Universal Charset Detector. 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true} +microservice = {path = "../utils", develop = true} + +"deepclean.logging" = {path = "../../../libs/logging", develop = true} +"deepclean.utils" = {path = "../../../libs/utils", develop = true} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index f920b32..2e8bda4 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -129,6 +129,7 @@ instances = 4 run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" data_field = "${base.data_field}" +export_endpoint = "localhost:5000" # data args sample_rate = "${base.sample_rate}" @@ -146,7 +147,7 @@ inference_rate = 8 max_latency = "${base.inference_aggregation_time}" # triton args -url = "localhost:8001" +triton_endpoint = "localhost:8001" model_name = "deepclean-stream" sequence_id = 1001 @@ -154,4 +155,3 @@ sequence_id = 1001 start_first = false timeout = 3 verbose = true # "${base.verbose}" -max_files = 300 diff --git a/projects/microservice/trainer/poetry.lock b/projects/microservice/trainer/poetry.lock index 7ce73e4..967f758 100644 --- a/projects/microservice/trainer/poetry.lock +++ b/projects/microservice/trainer/poetry.lock @@ -16,7 +16,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.0,<2.0" [package.extras] -speedups = ["aiodns", "brotli", "cchardet"] +speedups = ["Brotli", "aiodns", "cchardet"] [[package]] name = "aiosignal" @@ -44,11 +44,11 @@ pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] -docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", 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[[package]] name = "cryptography" @@ -203,11 +202,11 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] -docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "ruff", "mypy", "check-manifest"] +docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] +pep8test = ["black", "check-manifest", "mypy", "ruff"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601"] +test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -323,9 +322,9 @@ igwn-auth-utils = ">=0.3.1" ligo-segments = ">=1.0.0" [package.extras] -test = ["requests-mock (>=1.5.0)", "pytest-cov 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(>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -370,7 +369,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.0.0)"] -woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "frozenlist" @@ -399,8 +398,8 @@ setuptools = "*" dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] monitor = ["psutil (>=5.7.0)"] -recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports.socketpair", "psutil (>=5.7.0)"] -test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports.socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] +recommended = ["backports.socketpair", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)", "selectors2"] +test = ["backports.socketpair", "cffi (>=1.12.2)", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "dnspython (>=1.16.0,<2.0)", "futures", "idna", "mock", "objgraph", "psutil (>=5.7.0)", "requests", "selectors2"] [[package]] name = "geventhttpclient" @@ -425,7 +424,7 @@ optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" [package.extras] -docs = ["sphinx", "docutils (<0.18)"] +docs = ["Sphinx", "docutils (<0.18)"] test = ["objgraph", "psutil"] [[package]] @@ -495,8 +494,8 @@ scipy = ">=1.2.0" tqdm = ">=4.10.0" [package.extras] -conda = ["python-framel (>=8.40.1)", "python-nds2-client", "python-ldas-tools-framecpp"] -dev = ["ciecplib", "maya", "pandas", "psycopg2", "pymysql", "pyrxp", "sqlalchemy", "uproot (>=3.11)", "uproot3", "astropy (<5.1a0)", "lalsuite", "lscsoft-glue", "pycbc (>=1.13.4)", "python-ligo-lw (>=1.7.0)"] +conda = ["python-framel (>=8.40.1)", "python-ldas-tools-framecpp", "python-nds2-client"] +dev = ["astropy (<5.1a0)", "ciecplib", "lalsuite", "lscsoft-glue", "maya", "pandas", "psycopg2", "pyRXP", "pycbc (>=1.13.4)", "pymysql", "python-ligo-lw (>=1.7.0)", "sqlalchemy", "uproot (>=3.11)", "uproot3"] docs = ["numpydoc (>=0.8.0)", "requests", "sphinx (>=4.0.0)", "sphinx-automodapi", "sphinx-material (>=0.0.32)", "sphinx-panels (>=0.6.0)", "sphinxcontrib-programoutput"] test = ["coverage[toml] (>=5.0)", "freezegun (>=0.3.12)", "pytest (>=3.9.1)", "pytest-cov (>=2.4.0)", "pytest-socket", "pytest-xdist", "requests-mock"] @@ -527,10 +526,10 @@ torch = {version = "^1.7", optional = true} tritonclient = {version = "^2.22", extras = ["all"]} [package.extras] -tensorflow = ["tensorflow (>=2.3,<3.0)"] -torch = ["torch (>=1.7,<2.0)"] gcs = ["google-cloud-storage (>=1.38,<2.0)"] +tensorflow = ["tensorflow (>=2.3,<3.0)"] tensorrt = ["nvidia-tensorrt (>=8.0,<9.0)"] +torch = ["torch (>=1.7,<2.0)"] [package.source] type = "directory" @@ -575,9 +574,9 @@ python-versions = ">=3.7" zipp = ">=0.5" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] perf = ["ipython"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8", "importlib-resources (>=1.3)"] +testing = ["flake8 (<5)", "flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)"] [[package]] name = "importlib-resources" @@ -591,8 +590,8 @@ python-versions = ">=3.7" zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""} [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "pytest-flake8"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"] [[package]] name = "kiwisolver" @@ -764,7 +763,7 @@ pytz = ">=2020.1" tzdata = ">=2022.1" [package.extras] -all = ["beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "PyQt5 (>=5.15.1)", "pyreadstat (>=1.1.2)", "pytest (>=7.0.0)", "pytest-xdist (>=2.2.0)", "pytest-asyncio (>=0.17.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "scipy (>=1.7.1)", "s3fs (>=2021.08.0)", "SQLAlchemy (>=1.4.16)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"] +all = ["PyQt5 (>=5.15.1)", "SQLAlchemy (>=1.4.16)", "beautifulsoup4 (>=4.9.3)", "bottleneck (>=1.3.2)", "brotlipy (>=0.7.0)", "fastparquet (>=0.6.3)", "fsspec (>=2021.07.0)", "gcsfs (>=2021.07.0)", "html5lib (>=1.1)", "hypothesis (>=6.34.2)", "jinja2 (>=3.0.0)", "lxml (>=4.6.3)", "matplotlib (>=3.6.1)", "numba (>=0.53.1)", "numexpr (>=2.7.3)", "odfpy (>=1.4.1)", "openpyxl (>=3.0.7)", "pandas-gbq (>=0.15.0)", "psycopg2 (>=2.8.6)", "pyarrow (>=7.0.0)", "pymysql (>=1.0.2)", "pyreadstat (>=1.1.2)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)", "python-snappy (>=0.6.0)", "pyxlsb (>=1.0.8)", "qtpy (>=2.2.0)", "s3fs (>=2021.08.0)", "scipy (>=1.7.1)", "tables (>=3.6.1)", "tabulate (>=0.8.9)", "xarray (>=0.21.0)", "xlrd (>=2.0.1)", "xlsxwriter (>=1.4.3)", "zstandard (>=0.15.2)"] aws = ["s3fs (>=2021.08.0)"] clipboard = ["PyQt5 (>=5.15.1)", "qtpy (>=2.2.0)"] compression = ["brotlipy (>=0.7.0)", "python-snappy (>=0.6.0)", "zstandard (>=0.15.2)"] @@ -776,14 +775,14 @@ gcp = ["gcsfs (>=2021.07.0)", "pandas-gbq (>=0.15.0)"] hdf5 = ["tables (>=3.6.1)"] html = ["beautifulsoup4 (>=4.9.3)", "html5lib (>=1.1)", "lxml (>=4.6.3)"] mysql = ["SQLAlchemy (>=1.4.16)", "pymysql (>=1.0.2)"] -output_formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"] +output-formatting = ["jinja2 (>=3.0.0)", "tabulate (>=0.8.9)"] parquet = ["pyarrow (>=7.0.0)"] performance = ["bottleneck (>=1.3.2)", "numba (>=0.53.1)", "numexpr (>=2.7.1)"] plot = ["matplotlib (>=3.6.1)"] postgresql = ["SQLAlchemy (>=1.4.16)", "psycopg2 (>=2.8.6)"] spss = ["pyreadstat (>=1.1.2)"] sql-other = ["SQLAlchemy (>=1.4.16)"] -test = ["hypothesis (>=6.34.2)", "pytest (>=7.0.0)", "pytest-xdist (>=2.2.0)", "pytest-asyncio (>=0.17.0)"] +test = ["hypothesis (>=6.34.2)", "pytest (>=7.0.0)", "pytest-asyncio (>=0.17.0)", "pytest-xdist (>=2.2.0)"] xml = ["lxml (>=4.6.3)"] [[package]] @@ -839,9 +838,9 @@ python-versions = ">=3.7" [package.extras] crypto = ["cryptography (>=3.4.0)"] -dev = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface", "cryptography (>=3.4.0)", "pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)", "pre-commit"] +dev = ["coverage[toml] (==5.0.4)", "cryptography (>=3.4.0)", "pre-commit", "pytest (>=6.0.0,<7.0.0)", "sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] docs = ["sphinx (>=4.5.0,<5.0.0)", "sphinx-rtd-theme", "zope.interface"] -tests = ["pytest (>=6.0.0,<7.0.0)", "coverage[toml] (==5.0.4)"] +tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] [[package]] name = "pyparsing" @@ -852,7 +851,7 @@ optional = false python-versions = ">=3.6.8" [package.extras] -diagrams = ["railroad-diagrams", "jinja2"] +diagrams = ["jinja2", "railroad-diagrams"] [[package]] name = "python-dateutil" @@ -905,7 +904,7 @@ urllib3 = ">=1.21.1,<1.27" [package.extras] socks = ["PySocks (>=1.5.6,!=1.5.7)"] -use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"] +use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] [[package]] name = "safe-netrc" @@ -930,9 +929,9 @@ python-versions = "<3.12,>=3.8" numpy = ">=1.19.5,<1.27.0" [package.extras] -test = ["pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "asv", "mpmath", "gmpy2", "threadpoolctl", "scikit-umfpack", "pooch"] -doc = ["sphinx (!=4.1.0)", "pydata-sphinx-theme (==0.9.0)", "sphinx-design (>=0.2.0)", "matplotlib (>2)", "numpydoc"] -dev = ["mypy", "typing-extensions", "pycodestyle", "flake8", "rich-click", "click", "doit (>=0.36.0)", "pydevtool"] +dev = ["click", "doit (>=0.36.0)", "flake8", "mypy", "pycodestyle", "pydevtool", "rich-click", "typing_extensions"] +doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"] +test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] name = "scitokens" @@ -948,7 +947,7 @@ PyJWT = ">=1.6.1" setuptools = "*" [package.extras] -docs = ["sphinx"] +docs = ["Sphinx"] [[package]] name = "setuptools" @@ -959,9 +958,9 @@ optional = false python-versions = ">=3.7" [package.extras] -docs = ["sphinx (>=3.5)", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "furo", "sphinx-lint", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "sphinx-favicon", "sphinx-inline-tabs", "sphinx-reredirects", "sphinxcontrib-towncrier", "sphinx-notfound-page (==0.8.3)", "sphinx-hoverxref (<2)"] -testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "flake8 (<5)", "pytest-enabler (>=1.3)", "pytest-perf", "flake8-2020", "virtualenv (>=13.0.0)", "wheel", "pip (>=19.1)", "jaraco.envs (>=2.2)", "pytest-xdist", "jaraco.path (>=3.2.0)", "build", "filelock (>=3.4.0)", "pip-run (>=8.8)", "ini2toml[lite] (>=0.9)", "tomli-w (>=1.0.0)", "pytest-timeout", "pytest-black (>=0.3.7)", "pytest-cov", "pytest-mypy (>=0.9.1)", "pytest-flake8"] -testing-integration = ["pytest", "pytest-xdist", "pytest-enabler", "virtualenv (>=13.0.0)", "tomli", "wheel", "jaraco.path (>=3.2.0)", "jaraco.envs (>=2.2)", "build", "filelock (>=3.4.0)"] +docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (==0.8.3)", "sphinx-reredirects", "sphinxcontrib-towncrier"] +testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8 (<5)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pip-run (>=8.8)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"] +testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"] [[package]] name = "setuptools-scm" @@ -1070,9 +1069,9 @@ protobuf = {version = ">=3.5.0,<4", optional = true, markers = "extra == \"all\" python-rapidjson = ">=0.9.1" [package.extras] -all = ["protobuf (>=3.5.0,<4)", "grpcio (>=1.41.0)", "packaging (>=14.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "aiohttp (>=3.8.1)"] -grpc = ["protobuf (>=3.5.0,<4)", "grpcio (>=1.41.0)", "packaging (>=14.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] -http = ["geventhttpclient (>=1.4.4,<=2.0.2)", "aiohttp (>=3.8.1)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] +all = ["aiohttp (>=3.8.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] +grpc = ["grpcio (>=1.41.0)", "numpy (>=1.19.1)", "packaging (>=14.1)", "protobuf (>=3.5.0,<4)", "python-rapidjson (>=0.9.1)"] +http = ["aiohttp (>=3.8.1)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy (>=1.19.1)", "python-rapidjson (>=0.9.1)"] [[package]] name = "typeguard" @@ -1087,8 +1086,8 @@ 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rename to projects/microservice/trainer/trainer/main.py index 3d843ed..aaf0c44 100644 --- a/projects/microservice/trainer/trainer/trainer.py +++ b/projects/microservice/trainer/trainer/main.py @@ -3,7 +3,7 @@ import numpy as np import requests -from microservice.deployment import Deployment +from microservice.deployment import Deployment, ExportClient from trainer.dataloader import DataCollector from trainer.monitor import validate_csd @@ -101,6 +101,7 @@ def main( valid_frac: float, fine_tune_decay: Optional[float] = None, verbose: bool = False, + export_endpoint: str = "http://localhost:5005", **kwargs, ): deployment = Deployment(run_directory) @@ -108,6 +109,7 @@ def main( root_logger = logger.set_logger( "DeepClean trainer", log_file, verbose=verbose ) + export_client = ExportClient(export_endpoint) channels = get_channels(channels) frame_collector = DataCollector( @@ -155,9 +157,7 @@ def main( "Training on segment {} complete, saved " "optimized weights to {}".format(span, weights_path) ) - export(weights_path) - if last_start is None: - increment() + export_client.export(weights_path) # for trainings after the first, use the previous # optimized weights and reduce the learning rate diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index 89b9455..dfc88f8 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -2,15 +2,18 @@ from dataclasses import dataclass from pathlib import Path +import requests from microservice.frames import FrameCrawler +from deepclean.logging import logger + @dataclass class Deployment: root: Path def __post_init__(self): - for d in ["log", "train", "csd", "frame", "repository"]: + for d in ["log", "train", "csd", "frame", "repository", "storage"]: dirname = getattr(self, f"{d}_directory") dirname.mkdir(exist_ok=True, parents=True) @@ -26,13 +29,21 @@ def train_directory(self): def csd_directory(self): return self.train_directory / "csds" + @property + def repository_directory(self): + return self.root / "model_repo" + + @property + def infer_directory(self): + return self.root / "infer" + @property def frame_directory(self): - return self.root / "frames" + return self.infer_directory / "frames" @property - def repository_directory(self): - return self.root / "model_repo" + def storage_directory(self): + return self.infer_directory / "storage" @dataclass(frozen=True) @@ -62,3 +73,40 @@ def crawl(self, t0, timeout): ) yield fname, strain_fname + + +@dataclass +class ExportClient: + endpoint: str + + def __post_init__(self): + self.logger = logger.get_logger("Export client") + self._make_request("alive") + + def _make_request(self, target, *args: str): + url = f"{self.endpoint}/{target}" + if args: + url += "/" + "/".join(args) + + self.logger.debug(f"Making request to {url}") + r = requests.get(url) + r.raise_for_status() + self.logger.debug(f"Request to {url} successful") + return r.content + + def export(self, weights_path: Path) -> None: + weights_dir = weights_path.parent.name + self._make_request("export", weights_dir) + return None + + def increment(self) -> None: + self._make_request("increment") + return None + + def production_version(self) -> int: + version = self._make_request("production-version") + return int(version) + + def latest_version(self) -> int: + version = self._make_request("latest-version") + return int(version) diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 44d89f2..50566a5 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -227,20 +227,32 @@ def load_frame( # if we don't have multiple channels, then just grab the data data = read_channel(fname, channels, sample_rate) else: - # otherwise stack the arrays - # with lock: - # ts = TimeSeriesDict.read(fname, channels) data 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logging and documentation --- projects/microservice/cleaner/cleaner/main.py | 55 ++----------------- .../microservice/cleaner/cleaner/writer.py | 23 +++++++- projects/microservice/monitor/monitor/main.py | 12 ++-- projects/microservice/pyproject.toml | 35 ++++++++++-- projects/microservice/trainer/trainer/main.py | 20 +------ .../utils/microservice/deployment.py | 2 +- 6 files changed, 67 insertions(+), 80 deletions(-) diff --git a/projects/microservice/cleaner/cleaner/main.py b/projects/microservice/cleaner/cleaner/main.py index 202c454..dd94f1b 100644 --- a/projects/microservice/cleaner/cleaner/main.py +++ b/projects/microservice/cleaner/cleaner/main.py @@ -2,11 +2,10 @@ from pathlib import Path from typing import Iterable, Optional, Union -import tritonclient.grpc as triton from cleaner.dataloader import get_data_generators +from cleaner.utils import wait_for_server from cleaner.writer import ASDRMonitor, Writer from microservice.deployment import Deployment -from tritonclient.utils import InferenceServerException from deepclean.infer.callback import Callback, State from deepclean.logging import logger @@ -15,53 +14,6 @@ from typeo import scriptify -def wait(url): - client = triton.InferenceServerClient(url) - - # first wait for server to come online - start_time = time.time() - while True: - try: - live = client.is_server_live() - except InferenceServerException: - time.sleep(1) - else: - if live: - break - - elapsed = (time.time() - start_time) // 1 - if not elapsed % 10: - logger.info( - f"Waiting for server to come online, {elapsed}s elapsed" - ) - - start_time = time.time() - while not client.is_model_ready("deepclean-stream"): - time.sleep(1) - - elapsed = (time.time() - start_time) // 1 - if not elapsed % 10: - logger.info( - "Waiting for streaming model to " - f"come online, {elapsed}s elapsed" - ) - - start_time = time.time() - while True: - metadata = client.get_model_metadata("deepclean") - versions = list(map(int, metadata.versions)) - if max(versions) > 1: - break - - time.sleep(1) - elapsed = (time.time() - start_time) // 1 - if not elapsed % 10: - logger.info( - "Waiting for first DeepClean model to " - f"come online, {elapsed}s elapsed" - ) - - @scriptify def main( # IO args @@ -181,7 +133,10 @@ def main( logger.set_logger("DeepClean infer", log_file, verbose) channels = get_channels(channels) - wait(triton_endpoint) + # don't start loading data until the server + # is online and a trained version of DeepClean + # has become available + wait_for_server(triton_endpoint) witness_it, strain_it = get_data_generators( data_directory, data_field, diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index 66d6089..2d4e88a 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -32,7 +32,7 @@ def __post_init__(self): # always start out of spec until we can verify # that our cleaned data is good self.in_spec = False - self.logger = logger.get_logger("DeepClean monitor") + self.logger = logger.get_logger("DeepClean online monitor") def get_asd(self, ts: TimeSeries): asd = ts.asd( @@ -105,6 +105,9 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: noise = (raw - clean) * self.window frame = raw - noise elif asdr > 1 and not self.in_spec: + self.logger.debug( + "Mean ASDR is still out of spec, writing raw strain" + ) # we're currently out of spec and the most recent # frame hasn't changed that, so continue to # return the uncleaned data @@ -125,6 +128,9 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: noise = (raw - clean) * self.window[::-1] frame = raw - noise else: + self.logger.debug( + "Mean ASDR is still in spec, writing cleaned strain" + ) frame = clean # finally, if we've reached our buffer length, @@ -162,14 +168,24 @@ def __call__(self, **predictions): "made by DeepClean version {}".format(fname, version) ) + # extract some metadata about the frame we just got sample_rate = strain.sample_rate.value ifo, field, timestamp, dur = fname.stem.split("-") + + # the timestamp of the frame we _write_ will be + # one second behind the timestamp of this frame. + # Record the actual time the frame got written + # for the purposes of measuring latency timestamp = int(timestamp) t0 = timestamp - 1 file_timestamp = os.path.getmtime(fname) + # our frames will contain three channels as their + # data products, each one appending something + # different to the name of the strain channel data_products = DataProducts(strain.channel.name) tsd = TimeSeriesDict() + for key, noise in predictions.items(): # parse out "canary" or "production" from the # name of the prediction tensor @@ -179,8 +195,13 @@ def __call__(self, **predictions): # randomly initialized version of DeepClean, # then ignore the noise prediction altogether # and just write the plain strain data + self.logger.debug( + "Production model still randomly initialized, " + "skipping production clean" + ) if self.strain_buffer is None: continue + tsd[data_products.out_dq] = TimeSeries( strain.value, t0=t0, diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py index d91e156..71b8d76 100644 --- a/projects/microservice/monitor/monitor/main.py +++ b/projects/microservice/monitor/monitor/main.py @@ -13,19 +13,23 @@ @scriptify def main( + # paths + run_directory: Path, data_directory: Path, data_field: str, - run_directory: Path, export_endpoint: str, + # data/analysis args + channels: ChannelList, + sample_rate: float, + fftlength: float, asd_segment_length: float, monitor_period: float, freq_low: Frequency, freq_high: Frequency, + # storage args max_files: int, write_cadence: int, - channels: ChannelList, - sample_rate: float, - fftlength: float = 8, + # misc args verbose: bool = False, ): deployment = Deployment(run_directory) diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 2e8bda4..93a0a21 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -50,6 +50,8 @@ livingston_60Hz_channels = [ ] data_directory = "${DATA_DIR}" data_field = "${DATA_FIELD}" +export_endpoint = "localhost:5000" +triton_endpoint = "localhost:8001" sample_rate = 4096 freq_low = 55 @@ -68,6 +70,7 @@ verbose = false run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" data_field = "${base.data_field}" +export_endpoint = "${base.export_endpoint}" # data parameters channels = "${base.livingston_60Hz_channels}" @@ -99,9 +102,8 @@ factor = 0.1 device = "cuda:0" verbose = "${base.verbose}" -# architectures -[tool.typeo.scripts.train.architecture] -autoencoder = {} +# architecture parameters +architecture.autoencoder = {} [tool.typeo.scripts.exporter] @@ -129,7 +131,7 @@ instances = 4 run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" data_field = "${base.data_field}" -export_endpoint = "localhost:5000" +export_endpoint = "${base.export_endpoint}" # data args sample_rate = "${base.sample_rate}" @@ -147,7 +149,7 @@ inference_rate = 8 max_latency = "${base.inference_aggregation_time}" # triton args -triton_endpoint = "localhost:8001" +triton_endpoint = "${base.triton_endpoint}" model_name = "deepclean-stream" sequence_id = 1001 @@ -155,3 +157,26 @@ sequence_id = 1001 start_first = false timeout = 3 verbose = true # "${base.verbose}" + +[tool.typeo.scripts.monitor] +# paths +run_directory = "${base.run_directory}" +data_directory = "${base.data_directory}" +data_field = "${base.data_field}" +export_endpoint = "${base.export_endpoint}" + +# analysis args +channels = "${base.livingston_60Hz_channels}" +sample_rate = "${base.sample_rate}" +asd_segment_length = 100 +fftlength = 8 +monitor_period = 100 +freq_low = "${base.freq_low}" +freq_high = "${base.freq_high}" + +# storage args +max_files = 300 +write_cadence = 1024 + +# misc args +verbose = true # "${base.verbose}" diff --git a/projects/microservice/trainer/trainer/main.py b/projects/microservice/trainer/trainer/main.py index aaf0c44..717fb5a 100644 --- a/projects/microservice/trainer/trainer/main.py +++ b/projects/microservice/trainer/trainer/main.py @@ -2,7 +2,6 @@ from typing import Callable, Optional import numpy as np -import requests from microservice.deployment import Deployment, ExportClient from trainer.dataloader import DataCollector from trainer.monitor import validate_csd @@ -25,23 +24,6 @@ def _get_str(*times): return "-".join(map(_intify, times)) -def export(weights_path: Path): - weights_dir = weights_path.parent.name - url = f"http://localhost:5000/export/{weights_dir}" - logger.info(f"Making export request to {url}") - - r = requests.get(url) - r.raise_for_status() - logger.info("Export request completed") - - -def increment(): - logger.info("Incrementing production DeepClean version") - r = requests.get("http://localhost:5000/increment") - r.raise_for_status() - logger.info("Version incrementing complete") - - def train_on_segment( X: np.ndarray, y: np.ndarray, @@ -93,6 +75,7 @@ def main( run_directory: Path, data_directory: Path, data_field: str, + export_endpoint: str, channels: ChannelList, architecture: Callable, train_duration: float, @@ -101,7 +84,6 @@ def main( valid_frac: float, fine_tune_decay: Optional[float] = None, verbose: bool = False, - export_endpoint: str = "http://localhost:5005", **kwargs, ): deployment = Deployment(run_directory) diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index dfc88f8..712e442 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -84,7 +84,7 @@ def __post_init__(self): self._make_request("alive") def _make_request(self, target, *args: str): - url = f"{self.endpoint}/{target}" + url = f"http://{self.endpoint}/{target}" if args: url += "/" + "/".join(args) From 1e8d522ba437af60214e82d8725f9a21f059d824 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 9 May 2023 13:23:31 -0500 Subject: [PATCH 20/31] adding utils file --- .../microservice/cleaner/cleaner/utils.py | 53 +++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 projects/microservice/cleaner/cleaner/utils.py diff --git a/projects/microservice/cleaner/cleaner/utils.py b/projects/microservice/cleaner/cleaner/utils.py new file mode 100644 index 0000000..4f38b96 --- /dev/null +++ b/projects/microservice/cleaner/cleaner/utils.py @@ -0,0 +1,53 @@ +import time + +from tritonclient import grpc as triton +from tritonclient.utils import InferenceServerException + +from deepclean.logging import logger + + +def _wait_on_condition(func, msg, interval): + start_time = time.time() + while True: + if func(): + break + time.sleep(1) + + elapsed = int(time.time() - start_time) + if not elapsed % interval: + logger.info(f"{msg}, {elapsed}s elapsed") + + +def wait_for_server(url): + client = triton.InferenceServerClient(url) + + # first wait for server to come online + def _is_online(): + try: + return client.is_server_live() + except InferenceServerException: + return False + + msg = "Waiting for server to come online" + _wait_on_condition(_is_online, msg, 10) + + # now wait for the deepclean-stream model to + # be ready, which means all its constituent + # models are ready as well + def _models_loaded(): + return client.is_model_ready("deepclean-stream") + + msg = "Waiting for streaming model to come online" + _wait_on_condition(_models_loaded, msg, 10) + + # finally, wait for the latest version of + # the model to move beyond the initial version, + # which means we're ready to start doing inference + # to validate this newly exported model + def _is_trained_model_ready(): + metadata = client.get_model_metadata("deepclean") + versions = list(map(int, metadata.versions)) + return max(versions) > 1 + + msg = "Waiting for first trained DeepClean model to come online" + _wait_on_condition(_is_trained_model_ready, msg, 10) From 854a4a2463f4006fe45cb77e923e94a3800e3bbb Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Wed, 10 May 2023 12:28:21 -0500 Subject: [PATCH 21/31] adding specific version-setting API to exporter --- .../cleaner/cleaner/dataloader.py | 40 ++++++++++-- .../microservice/cleaner/cleaner/writer.py | 6 +- .../exporter/exporter/exporter.py | 64 ++++++++++++------- .../microservice/exporter/exporter/main.py | 20 ++++-- projects/microservice/monitor/monitor/main.py | 59 ++++++++++++----- projects/microservice/pyproject.toml | 30 +++++---- .../trainer/trainer/dataloader.py | 25 +++++++- projects/microservice/trainer/trainer/main.py | 2 +- .../utils/microservice/deployment.py | 18 +++--- .../microservice/utils/microservice/frames.py | 31 ++++++--- 10 files changed, 208 insertions(+), 87 deletions(-) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 64408e0..070738c 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -7,29 +7,47 @@ from gwpy.timeseries import TimeSeries from microservice.deployment import DataStream from microservice.frames import load_frame +from scipy.signal.windows import hann from deepclean.logging import logger +def extend(data, frame, stride, zeroed): + if frame is None: + shape = (len(data), stride) + frame = np.zeros(shape, dtype=np.float32) + if not zeroed: + taper = hann(2 * stride)[-stride:] + data[:, -stride:] *= taper + zeroed = True + elif zeroed: + taper = hann(2 * stride)[:stride] + frame[:, :stride] *= taper + zeroed = False + + data = np.concatenate([data, frame], axis=-1) + return data, zeroed + + def witness_iterator(it: Iterable, stride: int) -> np.ndarray: try: data = next(it) except StopIteration: raise ValueError("Frame crawler never returned any data") - idx = 0 + idx, zeroed = 0, False while True: start = idx * stride stop = (idx + 1) * stride - if stop > data.shape[-1]: + if (idx + 2) * stride > data.shape[-1]: try: frame = next(it) except StopIteration: - yield np.zeros((stride,), dtype=np.float32), True + yield data[:, start:stop], True break else: data = data[:, start:] - data = np.concatenate([data, frame], axis=-1) + data, zeroed = extend(data, frame, stride, zeroed) idx, start, stop = 0, 0, stride yield data[:, start:stop], False @@ -50,8 +68,18 @@ def strain_iterator(q: Queue): def frame_iter(crawler, channels, sample_rate, q): for fname, strain_fname in crawler: - logger.debug(f"Loading frame files {fname}, {strain_fname}") - witnesses = load_frame(fname, channels[1:], sample_rate) + if not fname.exists(): + logger.warning( + "Witness frame {} was dropped, attempting " + "to load corresponding strain frame {}".format( + fname, strain_fname + ) + ) + witnesses = None + else: + logger.debug(f"Loading frame files {fname}, {strain_fname}") + witnesses = load_frame(fname, channels[1:], sample_rate) + strain = TimeSeries.read(strain_fname, channel=channels[0]) if strain.sample_rate.value != sample_rate: strain = strain.resample(sample_rate) diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index 2d4e88a..8773c69 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -7,7 +7,7 @@ from gwpy.timeseries import TimeSeries, TimeSeriesDict from microservice.deployment import ExportClient from microservice.frames import DataProducts -from scipy.signal.windows import tukey +from scipy.signal.windows import hann from deepclean.logging import logger from deepclean.utils.filtering import Frequency, normalize_frequencies @@ -55,7 +55,7 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: sample_rate = raw.sample_rate.value size = int(sample_rate * 2) cutoff = int(sample_rate) - self.window = tukey(size, alpha=1)[-cutoff:] + self.window = hann(size)[-cutoff:] return None, None self.raw = self.raw.append(raw, inplace=False) @@ -162,7 +162,7 @@ def __call__(self, **predictions): # check which version of the model cleaned this frame # TODO: incorporate this as metadata somehow - version = self.export_client.production_version() + version = self.export_client.get_production_version() self.logger.info( "Cleaning strain from file {} using predictions" "made by DeepClean version {}".format(fname, version) diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index 95f7615..2e16ba1 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -31,7 +31,9 @@ class Exporter: streams_per_gpu: int = 2 platform: qv.Platform = qv.Platform.ONNX instances: Optional[int] = None + max_versions: int = 5 verbose: bool = False + clean: bool = True @classmethod def from_config( @@ -50,7 +52,7 @@ def from_config( typeo_parser = argparse.ArgumentParser() typeo_parser.add_argument( "--typeo", - bools={}, + bools={"verbose": False, "clean": True}, subcommands={}, nargs="*", action=TypeoTomlAction, @@ -99,8 +101,10 @@ def __post_init__(self): self.channels[1:], self.platform, self.instances, - clean=True, + clean=self.clean, ) + if not self.clean: + return # initialize an ensemble that we'll add steps to ensemble = self.repo.add( @@ -192,7 +196,9 @@ def __post_init__(self): def set_version_policy(self, model: qv.Model): model.config.version_policy.MergeFrom( model_config.ModelVersionPolicy( - latest=model_config.ModelVersionPolicy.Latest(num_versions=2) + latest=model_config.ModelVersionPolicy.Latest( + num_versions=self.max_versions + ) ) ) model.config.write() @@ -209,33 +215,47 @@ def postprocessor(self): def deepclean(self): return self.model.deepclean - def get_production_version(self): + @property + def latest_version(self) -> int: + return max(self.repo.models["deepclean"].versions) + + def get_production_version(self) -> int: ensemble = self.repo.models["deepclean-stream"] for step in ensemble.config.steps: - if step.model_name == "deepclean": + if step.model_name == "deepclean" and step.model_version > 0: return step.model_version def update_ensemble_versions(self, version: Optional[int] = None): + available_versions = self.repo.models["deepclean"].versions + if version is not None and version not in available_versions: + raise ValueError( + "Cannot set version of models in ensemble to {}, " + "only versions {} are available".format( + version, ", ".join(available_versions) + ) + ) + elif version is None: + version = self.latest_version + ensemble = self.repo.models["deepclean-stream"] + whitelist = ["snapshotter", "aggregator"] for step in ensemble.config.steps: - # don't update the version of the canary model, - # which will always point to the latest version - if step.model_version == -1: - continue - elif step.model_name in ["snapshotter", "aggregator"]: - # these models don't need to be versioned - # because they don't have any parameters - continue - - if version is None: - version = step.model_version + 1 - step.model_version = version - - self.logger.info( - "Updating production model in ensemble " - "to use version {}".format(version) - ) + # only versioned production models + # with non-negative versions need + # to be updated + if step.model_version > 0 and step.model_name not in whitelist: + self.logger.debug( + "Updating production version of model {} " + "to use version {} from version {}".format( + step.model_name, version, step.model_version + ) + ) + step.model_version = version + + # write the config once it's been updated + # for the server to pick up ensemble.config.write() + return version def export(self): self.repo.models["preprocessor"].export_version(self.preprocessor) diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index ff2c44d..a89e663 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -5,6 +5,11 @@ app = Flask(__name__) +@app.route("/alive") +def alive(): + return "", 200 + + @app.route("/export/") def export_model(train_dir): app.logger.info(f"Received export request for {train_dir}") @@ -12,11 +17,14 @@ def export_model(train_dir): return "", 200 -@app.route("/increment") -def increment_ensemble(): - app.logger.info("Received ensemble version increment request") - exporter.update_ensemble_versions() - return "", 200 +@app.route("/production-version/set/") +def set_production_version(version): + version = int(version) + app.logger.info(f"Setting production DeepClean to version {version}") + if version == -1: + version = None + version = exporter.update_ensemble_versions(version) + return str(version), 200 @app.route("/production-version") @@ -28,6 +36,6 @@ def get_production_version(): @app.route("/latest-version") def get_latest_version(): - version = max(exporter.repo.models["deepclean"].versions) + version = exporter.latest_version app.logger.info(f"Reporting latest DeepClean version {version}") return str(version), 200 diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py index 71b8d76..be5e25c 100644 --- a/projects/microservice/monitor/monitor/main.py +++ b/projects/microservice/monitor/monitor/main.py @@ -3,7 +3,7 @@ from gwpy.timeseries import TimeSeriesDict from microservice.deployment import DataStream, Deployment, ExportClient -from microservice.frames import DataProducts, FrameCrawler, load_frame +from microservice.frames import DataProducts, FrameCrawler, read_channel from deepclean.logging import logger from deepclean.utils.channels import ChannelList, get_channels @@ -64,7 +64,7 @@ def main( files = [] buffer, t0 = TimeSeriesDict(), None - latest_version = 1 + latest_version = export_client.get_latest_version() validating = False in_spec_for = 0 @@ -75,22 +75,23 @@ def main( t0 = timestamp # grab all of the data products from the current - # frame as well as its associate raw data + # frame as well as its associated raw data strain_fname = stream.hoft / fname.name - frame = load_frame(fname, data_products.channels, sample_rate) - raw = load_frame(strain_fname, strain_channel, sample_rate) - - frame[strain_channel] = raw + frame = TimeSeriesDict() + for channel in data_products.channels: + frame[channel] = read_channel(fname, channel, sample_rate) + strain = read_channel(strain_fname, strain_channel, sample_rate) + frame[strain_channel] = strain buffer.append(frame) # see if we've aggregated enough data to move # into cold storage in our output directory - if (timestamp - t0) == write_cadence: + if (timestamp + 1 - t0) == write_cadence: # slice out the current segment for writing, ignoring # any data from the past that we might have had to hold # on to for canary validation - output = buffer.crop(t0, timestamp) - write_fname = f"{ifo}-{field}-{output.t0}-{write_cadence}.h5" + output = buffer.crop(t0, timestamp + 1, copy=True) + write_fname = f"{ifo}-{field}-{t0}-{write_cadence}.h5" write_path = deployment.storage_directory / write_fname logger.info( @@ -102,10 +103,10 @@ def main( # now slough off the data from the frame we just # wrote, saving just enough to be able to keep - # doing canary model validation. Increment t0 to mathc + # doing canary model validation. Increment t0 to match start = timestamp - asd_segment_length - buffer = buffer.crop(start, timestamp) - t0 = timestamp + buffer = buffer.crop(start, timestamp + 1, copy=True) + t0 = timestamp + 1 files.append(fname) if len(files) > max_files: @@ -114,14 +115,22 @@ def main( removed.unlink() # if we don't have enough data to validate the ASD, move on - if buffer.duration < asd_segment_length: + start, stop = buffer.span + if (stop - start) < asd_segment_length: continue # if we're not currently validating, see if there is a # newer version of the model that needs validation if not validating: - version = export_client.latest_version() + # TODO: should we check for new versions even + # if we're validating and interrupt to start + # validating those instead? + version = export_client.get_latest_version() if version > latest_version: + logger.info( + f"Beginning validation of new DeepClean version {version}" + ) + latest_version = version validating = True in_spec_for = 0 @@ -147,15 +156,31 @@ def main( # TODO: write this to some sort of Prometheus # dashboard or something asdr = clean / raw - if asdr.mean() <= 1: + mean_asdr = asdr.mean() + if mean_asdr <= 1: in_spec_for += 1 + logger.info( + "Canary DeepClean version {} been in-spec for " + "{} seconds".format(latest_version, in_spec_for) + ) else: + logger.debug( + "Canary DeepClean version {} has fallen " + "out of spec after {}s with asdr {}, " + "resetting validation counter".format( + latest_version, in_spec_for, mean_asdr + ) + ) in_spec_for = 0 # if we've been in spec for the right period, # then increment the production version of # DeepClean and reset our validation counter if in_spec_for >= monitor_period: - export_client.increment() + logger.info( + "Canary DeepClean version {} has passed " + "validation, moving to production".format(latest_version) + ) + export_client.set_production_version(latest_version) validating = False in_spec_for = 0 diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 93a0a21..ba7deb3 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -1,6 +1,11 @@ [tool.typeo.base] # global paths run_directory = "${PROJECT_DIR}" +data_directory = "${DATA_DIR}" +data_field = "${DATA_FIELD}" + +export_endpoint = "localhost:5000" +triton_endpoint = "localhost:8001" # global data args hanford_60Hz_channels = [ @@ -48,23 +53,20 @@ livingston_60Hz_channels = [ "L1:ASC-SRC2_P_OUT_DQ", "L1:ASC-SRC2_Y_OUT_DQ", ] -data_directory = "${DATA_DIR}" -data_field = "${DATA_FIELD}" -export_endpoint = "localhost:5000" -triton_endpoint = "localhost:8001" sample_rate = 4096 -freq_low = 55 -freq_high = 65 +freq_low = 58 +freq_high = 62 # global inference args inference_sampling_rate = 512 inference_aggregation_time = 0.5 -inference_batch_size = 256 # TODO: enforce that this is agg_time * inference_sampling_rate +inference_batch_size = 128 # global misc args verbose = false + [tool.typeo.scripts.train] # paths run_directory = "${base.run_directory}" @@ -121,10 +123,14 @@ batch_size = "${base.inference_batch_size}" aggregation_time = "${base.inference_aggregation_time}" # triton args -streams_per_gpu = 2 +streams_per_gpu = 1 platform = "tensorrt_plan" instances = 4 +# misc args +clean = true +verbose = "${base.verbose}" + [tool.typeo.scripts.clean] # paths @@ -155,7 +161,7 @@ sequence_id = 1001 # misc args start_first = false -timeout = 3 +timeout = 2 verbose = true # "${base.verbose}" [tool.typeo.scripts.monitor] @@ -168,15 +174,15 @@ export_endpoint = "${base.export_endpoint}" # analysis args channels = "${base.livingston_60Hz_channels}" sample_rate = "${base.sample_rate}" -asd_segment_length = 100 +asd_segment_length = 10 fftlength = 8 -monitor_period = 100 +monitor_period = 10 freq_low = "${base.freq_low}" freq_high = "${base.freq_high}" # storage args max_files = 300 -write_cadence = 1024 +write_cadence = 128 # misc args verbose = true # "${base.verbose}" diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index 0d2b5d8..d00ef6d 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -10,6 +10,7 @@ import numpy as np from microservice.deployment import DataStream from microservice.frames import load_frame +from scipy.signal.windows import hann from deepclean.logging import logger from deepclean.utils.channels import ChannelList, get_channels @@ -35,6 +36,8 @@ def collect_frames( edge_size = int(cadence * sample_rate) X = np.zeros((len(channels) - 1, 0)) y = np.zeros((0,)) + zeroed = False + taper = hann(int(2 * sample_rate))[: int(sample_rate)] # clear the first couple filenames to avoid # a race condition where the first file @@ -47,10 +50,28 @@ def collect_frames( if start is None: start = int(fname.stem.split("-")[-2]) - witnesses = load_frame(fname, channels[1:], sample_rate) - strain = load_frame(strain_fname, channels[0], sample_rate) + if not fname.exists(): + logger.warning( + "Witness frame {} was dropped, attempting " + "to load corresponding strain frame {}".format( + fname, strain_fname + ) + ) + witnesses = np.zeros( + (len(channels) - 1, int(sample_rate)), + ) + if not zeroed: + X[:, -int(sample_rate) :] *= taper[::-1] + zeroed = True + else: + logger.debug(f"Loading frame files {fname}, {strain_fname}") + witnesses = load_frame(fname, channels[1:], sample_rate) + if zeroed: + witnesses[:, : int(sample_rate)] *= taper + zeroed = False X = np.concatenate([X, witnesses], axis=1) + strain = load_frame(strain_fname, channels[0], sample_rate) y = np.concatenate([y, strain]) # TODO: insert some checks here about data quality, diff --git a/projects/microservice/trainer/trainer/main.py b/projects/microservice/trainer/trainer/main.py index 717fb5a..9304245 100644 --- a/projects/microservice/trainer/trainer/main.py +++ b/projects/microservice/trainer/trainer/main.py @@ -102,7 +102,7 @@ def main( train_duration, retrain_cadence, sample_rate, - timeout=60, + timeout=3, verbose=verbose, ) diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index 712e442..3807f96 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -1,6 +1,7 @@ import time from dataclasses import dataclass from pathlib import Path +from typing import Optional import requests from microservice.frames import FrameCrawler @@ -57,10 +58,10 @@ def detchar(self): @property def hoft(self): - return self.root / "kafka" / self.field + return self.root / "kafka" / self.field.replace("replay", "") def crawl(self, t0, timeout): - crawler = FrameCrawler(self.detchar, t0, timeout) + crawler = FrameCrawler(self.detchar, t0, timeout, max_dropped_frames=5) for fname in crawler: strain_fname = fname.name.replace("lldetchar", "llhoft") strain_fname = self.hoft / strain_fname @@ -99,14 +100,15 @@ def export(self, weights_path: Path) -> None: self._make_request("export", weights_dir) return None - def increment(self) -> None: - self._make_request("increment") - return None - - def production_version(self) -> int: + def get_production_version(self) -> int: version = self._make_request("production-version") return int(version) - def latest_version(self) -> int: + def set_production_version(self, version: Optional[int] = None): + version = version or -1 + version = self._make_request("production-version/set", str(version)) + return int(version) + + def get_latest_version(self) -> int: version = self._make_request("latest-version") return int(version) diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 50566a5..5a733b1 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -115,11 +115,16 @@ def get_prefix(data_dir: Path): return list(prefixes)[0], int(list(durations)[0]) +class DroppedFrames(Exception): + pass + + @dataclass class FrameCrawler: data_dir: Path t0: Optional[float] = None timeout: Optional[float] = None + max_dropped_frames: int = 0 def __post_init__(self): prefix, self.length = get_prefix(self.data_dir) @@ -133,6 +138,7 @@ def __post_init__(self): self.t0 = sorted(starts, reverse=self.t0 == -1)[0] def __iter__(self): + self._dropped_frames = 0 return self def __next__(self): @@ -155,9 +161,14 @@ def __next__(self): elif self.timeout == 0: raise StopIteration elif (time.time() - start_time) > self.timeout: - raise RuntimeError( - f"No frame file {fname} after {self.timeout}s" - ) + self._dropped_frames += 1 + if self._dropped_frames > self.max_dropped_frames: + raise DroppedFrames( + f"No frame file {fname} after {self.timeout}s" + ) + break + else: + self._dropped_frames = 0 self.t0 += self.length return fname @@ -165,8 +176,8 @@ def __next__(self): def resample(x: TimeSeries, sample_rate: float): if x.sample_rate.value != sample_rate: - return x.resample(sample_rate).value - return x.value + return x.resample(sample_rate) + return x def read_channel(fname, channel, sample_rate): @@ -225,12 +236,12 @@ def load_frame( if isinstance(channels, str): # if we don't have multiple channels, then just grab the data - data = read_channel(fname, channels, sample_rate) + data = read_channel(fname, channels, sample_rate).value else: data = [] for channel in channels: x = read_channel(fname, channel, sample_rate) - data.append(x) + data.append(x.value) data = np.stack(data) # return as the expected type @@ -243,15 +254,15 @@ class DataProducts: @property def out_dq(self): - return self.channel + "DEEPCLEAN_PRODUCTION_OUT_DQ" + return self.channel + "_DEEPCLEAN_PRODUCTION_OUT_DQ" @property def production(self): - return self.channel + "DEEPCLEAN_PRODUCTION" + return self.channel + "_DEEPCLEAN_PRODUCTION" @property def canary(self): - return self.channel + "DEEPCLEAN_CANARY" + return self.channel + "_DEEPCLEAN_CANARY" @property def channels(self): From 22c896842f83ccd45ca8d5cdca99dab71bf6ca00 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Thu, 11 May 2023 21:24:48 -0500 Subject: [PATCH 22/31] simplifying deployment object and switching from csd to coherence monitor --- projects/microservice/README.md | 35 ++++++++ projects/microservice/monitor/monitor/main.py | 11 ++- projects/microservice/pyproject.toml | 6 +- projects/microservice/run.sh | 10 +++ .../microservice/trainer/trainer/checks.py | 61 +++++++++++++ .../trainer/trainer/dataloader.py | 86 +++++++++++++++---- projects/microservice/trainer/trainer/main.py | 40 +++++++-- .../microservice/trainer/trainer/monitor.py | 33 ------- .../utils/microservice/deployment.py | 11 +-- 9 files changed, 226 insertions(+), 67 deletions(-) create mode 100644 projects/microservice/README.md create mode 100644 projects/microservice/run.sh create mode 100644 projects/microservice/trainer/trainer/checks.py delete mode 100644 projects/microservice/trainer/trainer/monitor.py diff --git a/projects/microservice/README.md b/projects/microservice/README.md new file mode 100644 index 0000000..6e0c887 --- /dev/null +++ b/projects/microservice/README.md @@ -0,0 +1,35 @@ +# DeepClean Production Microservice +This project builds a complete end-to-end application for iteratively training, validating, and deploying DeepClean for subtracting 60Hz noise from low-latency frames. +Each component functions as a standalone service with a dedicated environment and purpose. +At the moment, those environments are built locally using Conda, but in the near future will be fully containerized and deployed via Singularity. +The services contained here are: +- `trainer`: Trains a DeepClean model on live data collected from low latency frames at some fixed cadence. All strain data used for training must have the `DMT-ANALYSIS_READY` flag turned on. +- `exporter`: Flask application that takes newly trained versions of DeepClean, accelerates them using TensorRT, and adds them to a Triton model repository. +- `cleaner`: Responsible for sending inference requests to DeepClean to compute noise predictions and subtract them from low-latency strain. +- `monitor`: Monitors the cleaned data produced by the latest version of DeepClean in order to validate it and assess whether it's ready to move into production. + +Additionally, this project contains a `utils` library for defining utility functions shared by the various projects, and leverages a Triton inference server instance to handle serving DeepClean for inference and moving new models into production. +Each project is discussed in more detail [below](#project-overviews). + +## Build and run instructions +On a node with at least 2 enterprise-grade GPUs, begin by installing the `pinto` environment management utility by following the instructions [here](https://github.com/ML4GW/pinto). +If you chose to go with the virtual environment installation of `pinto`, be sure to activate that environment (`conda activate pinto`) before proceeding with the instructions below. + +First, begin by using `pinto` to build all of the projects' corresponding environments + +``` +./build.sh +``` + +Then, launch a `tmux` session with panes executing each of the individual services by running + +``` +./run.sh +``` + +## Configuration +Each project reads its arguments from a shared configuration file [`pyproject.toml`](./pyproject.toml), which is parsed during command line execution by the locally-maintained `typeo` library. +The configuration is broken up into different tables for each project, denoted `[tool.typeo.scripts.]`, as well as a `[tool.typeo.base]` table for defining shared parameters. +For explanations of each argument, consult the corresponding project's documentation. + +## Project overviews diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py index be5e25c..d147063 100644 --- a/projects/microservice/monitor/monitor/main.py +++ b/projects/microservice/monitor/monitor/main.py @@ -48,11 +48,11 @@ def main( start_time = time.time() while not list(deployment.frame_directory.iterdir()): time.sleep(1) - div, mod = divmod(time.time() - start_time, 10) - if not mod: + elapsed = int(time.time() - start_time) + if not elapsed % 10: logger.info( "Waiting for first frame to be written, " - "{}s elapsed".format(div) + "{}s elapsed".format(elapsed) ) # now start iterating through written frames and @@ -64,7 +64,10 @@ def main( files = [] buffer, t0 = TimeSeriesDict(), None - latest_version = export_client.get_latest_version() + # initialize latest version to be the production + # version so that any newer versions will trigger + # validation + latest_version = export_client.get_production_version() validating = False in_spec_for = 0 diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index ba7deb3..b9fb89d 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -76,8 +76,8 @@ export_endpoint = "${base.export_endpoint}" # data parameters channels = "${base.livingston_60Hz_channels}" -train_duration = 2048 -retrain_cadence = 1024 +train_duration = 1024 +retrain_cadence = 512 sample_rate = "${base.sample_rate}" valid_frac = 0.25 @@ -151,7 +151,7 @@ look_ahead = 0.5 # inference args inference_sampling_rate = "${base.inference_sampling_rate}" batch_size = "${base.inference_batch_size}" -inference_rate = 8 +inference_rate = 64 max_latency = "${base.inference_aggregation_time}" # triton args diff --git a/projects/microservice/run.sh b/projects/microservice/run.sh new file mode 100644 index 0000000..539dd2e --- /dev/null +++ b/projects/microservice/run.sh @@ -0,0 +1,10 @@ +#!/bin/bash -x + +PROJECT=$1 + +prun() { + echo "PROJECT=$PROJECT pinto -p $1 run -e ../.env" +} +tmux new-session -s deepclean-production -d "CUDA_VISIBLE_DEVICES=0 $(prun exporter) flask --app=exporter run" +tmux split-window -v "CUDA_VISIBLE_DEVICES=0 $(prun trainer) train --typeo pyproject.toml script=train architecture=autoencoder" +tmux split-window -h "CUDA_VISIBLE_DEVICES=1 singularity exec --nv /cvmfs/singularity.opensciencegrid.org/ml4gw/tritonserver:22.12 " diff --git a/projects/microservice/trainer/trainer/checks.py b/projects/microservice/trainer/trainer/checks.py new file mode 100644 index 0000000..c1504ac --- /dev/null +++ b/projects/microservice/trainer/trainer/checks.py @@ -0,0 +1,61 @@ +from pathlib import Path +from typing import List + +import h5py +import numpy as np +from gwpy.timeseries import TimeSeries + +from deepclean.logging import logger + + +def check_coherences( + X: np.ndarray, + y: np.ndarray, + channels: List[str], + fftlength: float, + sample_rate: float, +): + """ + Measure the coherence of each witness channel + with the strain channel. TODO: add some checks + around having some minimal coherence in the + target frequency range. + """ + logger.info("Evaluating coherece of strain channel with witnesses") + psd_kwargs = dict(fftlength=fftlength, window="hann", method="median") + y = TimeSeries(y, sample_rate=sample_rate) + pyy = y.psd(**psd_kwargs) + + coherences = {} + for x, channel in zip(X, channels[1:]): + x = TimeSeries(x, sample_rate=sample_rate) + pxx = x.psd(**psd_kwargs) + pxy = y.csd(x, **psd_kwargs) + coh = pxy**2 / (pxx * pyy) + coherences[channel] = coh.value**0.5 + return coherences + + +def data_checks( + X: np.ndarray, + y: np.ndarray, + channels: List[str], + sample_rate: float, + fftlength: float, + fname: Path, +) -> bool: + logger.info( + "Performing checks on training data and " + "saving outputs to {}".format(fname) + ) + with h5py.File(fname, "w") as f: + f.attrs["sample_rate"] = sample_rate + f.attrs["df"] = 1 / fftlength + + group = f.create_group("coherence") + coherences = check_coherences(X, y, channels, sample_rate, fftlength) + group.update(coherences) + + # TODO: add actual checks and return boolean + # indicating whether or not data has passed them + return True diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index d00ef6d..fe9abe4 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -9,7 +9,7 @@ import numpy as np from microservice.deployment import DataStream -from microservice.frames import load_frame +from microservice.frames import load_frame, read_channel from scipy.signal.windows import hann from deepclean.logging import logger @@ -36,9 +36,21 @@ def collect_frames( edge_size = int(cadence * sample_rate) X = np.zeros((len(channels) - 1, 0)) y = np.zeros((0,)) + + # initialize a window taper we'll use if + # we drop any frames to keep some sense + # of continuity. Include a flag to indicate + # whether the taper needs to be applied + # or not in the case of a dropped frame zeroed = False taper = hann(int(2 * sample_rate))[: int(sample_rate)] + # set up some parameters to inspect the + # interferometer state vector + ifo = channels[0].split(":")[0] + state = f"{ifo}:GDS-CALIB_STATE_VECTOR" + collecting = True + # clear the first couple filenames to avoid # a race condition where the first file # accidentally gets cycled out before we start @@ -47,9 +59,49 @@ def collect_frames( while not event.is_set(): fname, strain_fname = next(crawler) + + # first check the logical and of the first + # two bits of the state vector to ensure + # that this strain data is analysis ready, + # otherwise we'll be training on bad data + # see: https://wiki.ligo.org/DetChar/DataQuality/O4Flags#Detector_state_segments # noqa: E501 + state_vector = read_channel(strain_fname, state, 16) + ready = all([all(map(int, f"{i:b}"[:2])) for i in state_vector.value]) + + if not ready and collecting: + logger.warning( + "Strain file {} has indicated that {} is no longer " + "analysis ready. Resetting train dataset until " + "{} is back online".format(strain_fname, ifo, ifo) + ) + start = None + collecting = False + zeroed = False + X = np.zeros((len(channels) - 1, 0)) + y = np.zeros((0,)) + continue + elif not ready and not collecting: + logger.warning( + "Strain file {} has indicated that {} continues " + "to not be analysis ready, skipping".format(strain_fname, ifo) + ) + continue + elif not collecting: + logger.info( + "Strain file {} indicates that {} is back " + "to be analysis ready, resuming training " + "data collection".format(strain_fname, ifo) + ) + collecting = True + + # mark the start time of the current stretch + # of data for returning to the main training + # process in case we interrupt at some point if start is None: start = int(fname.stem.split("-")[-2]) + # sometimes the witness channels get dropped. If + # this happens, create a dummy empty witness frame if not fname.exists(): logger.warning( "Witness frame {} was dropped, attempting " @@ -57,34 +109,35 @@ def collect_frames( fname, strain_fname ) ) - witnesses = np.zeros( - (len(channels) - 1, int(sample_rate)), - ) - if not zeroed: + + shape = (len(channels) - 1, int(sample_rate)) + witnesses = np.zeros(shape) + + # if we're not already zeroed out, taper out + # the old witness data to ensure we don't + # introduce any artifacts + if not zeroed and X.size > 0: X[:, -int(sample_rate) :] *= taper[::-1] zeroed = True else: logger.debug(f"Loading frame files {fname}, {strain_fname}") witnesses = load_frame(fname, channels[1:], sample_rate) + + # if we were in a stretch with zeroed out + # data, taper this witness back in if zeroed: witnesses[:, : int(sample_rate)] *= taper zeroed = False + # now add this data to our running array, and + # do the same for the strain data (which is + # pretty much never dropped) X = np.concatenate([X, witnesses], axis=1) strain = load_frame(strain_fname, channels[0], sample_rate) y = np.concatenate([y, strain]) - # TODO: insert some checks here about data quality, - # checking for coherence, etc., and possibly reset - # X, y, and start accordingly once the conditions have - # been met again - if False: - logger.info( - "Data quality conditions have been violated, " - "pausing data collection until conditions " - "improve." - ) - + # pass these arrays out to the training process + # if we've collected enough data if len(y) >= size: logger.info( "Accumulated {}s of data starting at " @@ -92,6 +145,7 @@ def collect_frames( ) yield X, y, start + # slough off any old data we don't need anymore X = X[:, edge_size:] y = y[edge_size:] start += cadence diff --git a/projects/microservice/trainer/trainer/main.py b/projects/microservice/trainer/trainer/main.py index 9304245..8e08385 100644 --- a/projects/microservice/trainer/trainer/main.py +++ b/projects/microservice/trainer/trainer/main.py @@ -3,8 +3,8 @@ import numpy as np from microservice.deployment import Deployment, ExportClient +from trainer.checks import data_checks from trainer.dataloader import DataCollector -from trainer.monitor import validate_csd from deepclean.architectures import architectures from deepclean.logging import logger @@ -85,14 +85,21 @@ def main( fine_tune_decay: Optional[float] = None, verbose: bool = False, **kwargs, -): +) -> None: + # build a deployment object that contains all + # the structure of where our logs/intermediate + # data products go deployment = Deployment(run_directory) log_file = deployment.log_directory / "train.root.log" root_logger = logger.set_logger( "DeepClean trainer", log_file, verbose=verbose ) + + # connect to the export service export_client = ExportClient(export_endpoint) + # start collecting frames from the data stream + # to aggregate into a dataset channels = get_channels(channels) frame_collector = DataCollector( data_directory, @@ -106,15 +113,32 @@ def main( verbose=verbose, ) + # enter it as a context since it does the + # collection in a background process while + # we train in the main process last_start = None with frame_collector as data_it: for X, y, start in data_it: span = _get_str(start, start + train_duration) - csd_fname = deployment.csd_directory / f"{span}.h5" - validate_csd( - X, y, channels, sample_rate, fftlength=8, fname=csd_fname - ) + # perform some checks on the training dataset + # to see if it's even worth attemptint to train + # a model on it + fname = deployment.data_checks_directory / f"{span}.h5" + passed = data_checks( + X, y, channels, sample_rate, fftlength=8, fname=fname + ) + if not passed: + logger.warning( + "Training dataset {} did not pass quality checks, " + "skipping training on this dataset" + ) + continue + + # check to see if this dataset falls directly + # after the previous one, or if we need to + # take some special steps to reset after a + # dropped dataset if last_start is not None: expected_start = last_start + retrain_cadence if start > expected_start: @@ -122,6 +146,7 @@ def main( # training differently on a new lock segment pass + # launch a training job on the current dataset root_logger.info(f"Launching training on segment {span}") weights_path = train_on_segment( X, @@ -139,6 +164,9 @@ def main( "Training on segment {} complete, saved " "optimized weights to {}".format(span, weights_path) ) + + # send the optimized weights to the export service + # to add the trained model to our model repository export_client.export(weights_path) # for trainings after the first, use the previous diff --git a/projects/microservice/trainer/trainer/monitor.py b/projects/microservice/trainer/trainer/monitor.py deleted file mode 100644 index f27fb8e..0000000 --- a/projects/microservice/trainer/trainer/monitor.py +++ /dev/null @@ -1,33 +0,0 @@ -from pathlib import Path -from typing import List - -import h5py -import numpy as np -from gwpy.timeseries import TimeSeries - -from deepclean.logging import logger - - -def validate_csd( - X: np.ndarray, - y: np.ndarray, - channels: List[str], - sample_rate: float, - fftlength: float, - fname: Path, -): - logger.info( - "Evaluating CSD of strain channel with witnesses " - "and saving outputs to {}".format(fname) - ) - - y = TimeSeries(y, sample_rate=sample_rate) - with h5py.File(fname, "w") as f: - f.attrs["sample_rate"] = sample_rate - f.attrs["df"] = 1 / fftlength - csds = f.create_group("csd") - - for x, channel in zip(X, channels[1:]): - x = TimeSeries(x, sample_rate=sample_rate) - csd = y.csd(x, fftlength=fftlength, window="hann") - csds[channel] = csd.value diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index 3807f96..9cbdc00 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -14,9 +14,10 @@ class Deployment: root: Path def __post_init__(self): - for d in ["log", "train", "csd", "frame", "repository", "storage"]: - dirname = getattr(self, f"{d}_directory") - dirname.mkdir(exist_ok=True, parents=True) + for name, prop in self.__class__.__dict__.items(): + if isinstance(prop, property) and name.endswith("directory"): + dirname = getattr(self, name) + dirname.mkdir(exist_ok=True, parents=True) @property def log_directory(self): @@ -27,8 +28,8 @@ def train_directory(self): return self.root / "train" @property - def csd_directory(self): - return self.train_directory / "csds" + def data_checks_directory(self): + return self.train_directory / "checks" @property def repository_directory(self): From ae3a11cd59b43f0ebb4ce6b4947f8257f7e508e1 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 23 May 2023 01:20:08 -0500 Subject: [PATCH 23/31] adding frame buffer during dataloading to avoid resampling artifacts --- projects/microservice/build.sh | 6 + .../cleaner/cleaner/dataloader.py | 108 ++++++---------- .../microservice/exporter/exporter/main.py | 2 +- projects/microservice/monitor/pyproject.toml | 2 +- projects/microservice/run.sh | 19 ++- .../trainer/trainer/dataloader.py | 60 ++------- .../microservice/utils/microservice/frames.py | 119 +++++++++++++++--- 7 files changed, 174 insertions(+), 142 deletions(-) create mode 100755 projects/microservice/build.sh mode change 100644 => 100755 projects/microservice/run.sh diff --git a/projects/microservice/build.sh b/projects/microservice/build.sh new file mode 100755 index 0000000..ce1d353 --- /dev/null +++ b/projects/microservice/build.sh @@ -0,0 +1,6 @@ +#! /bin/bash -x + +pinto -p trainer build +pinto -p exporter build +pinto -p cleaner build +pinto -p monitor build diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 070738c..0be2120 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -4,54 +4,8 @@ from typing import Iterable, List import numpy as np -from gwpy.timeseries import TimeSeries from microservice.deployment import DataStream -from microservice.frames import load_frame -from scipy.signal.windows import hann - -from deepclean.logging import logger - - -def extend(data, frame, stride, zeroed): - if frame is None: - shape = (len(data), stride) - frame = np.zeros(shape, dtype=np.float32) - if not zeroed: - taper = hann(2 * stride)[-stride:] - data[:, -stride:] *= taper - zeroed = True - elif zeroed: - taper = hann(2 * stride)[:stride] - frame[:, :stride] *= taper - zeroed = False - - data = np.concatenate([data, frame], axis=-1) - return data, zeroed - - -def witness_iterator(it: Iterable, stride: int) -> np.ndarray: - try: - data = next(it) - except StopIteration: - raise ValueError("Frame crawler never returned any data") - - idx, zeroed = 0, False - while True: - start = idx * stride - stop = (idx + 1) * stride - if (idx + 2) * stride > data.shape[-1]: - try: - frame = next(it) - except StopIteration: - yield data[:, start:stop], True - break - else: - data = data[:, start:] - data, zeroed = extend(data, frame, stride, zeroed) - idx, start, stop = 0, 0, stride - - yield data[:, start:stop], False - idx += 1 +from microservice.frames import frame_it def strain_iterator(q: Queue): @@ -66,27 +20,37 @@ def strain_iterator(q: Queue): yield response -def frame_iter(crawler, channels, sample_rate, q): - for fname, strain_fname in crawler: - if not fname.exists(): - logger.warning( - "Witness frame {} was dropped, attempting " - "to load corresponding strain frame {}".format( - fname, strain_fname - ) - ) - witnesses = None - else: - logger.debug(f"Loading frame files {fname}, {strain_fname}") - witnesses = load_frame(fname, channels[1:], sample_rate) +def frame_iter( + crawler: Iterable, + channels: List[str], + sample_rate: float, + inference_sampling_rate: float, + batch_size: int, + q: Queue, +): + stride = int(batch_size * sample_rate / inference_sampling_rate) + it = frame_it(crawler, channels, sample_rate) - strain = TimeSeries.read(strain_fname, channel=channels[0]) - if strain.sample_rate.value != sample_rate: - strain = strain.resample(sample_rate) + strain_fname, strain, witnesses = next(it) + q.put((strain, strain_fname)) - q.put((strain, strain_fname)) - yield witnesses - q.put(None) + idx = 0 + while True: + start = idx * stride + stop = (idx + 1) * stride + if (idx + 2) * stride > witnesses.shape[-1]: + try: + strain_fname, strain, update = next(it) + except StopIteration: + yield witnesses[:, start:stop], True + q.put(None) + break + else: + q.put((strain, strain_fname)) + witnesses = witnesses[:, start:] + witnesses = np.concatenate([witnesses, update], axis=1) + idx, start, stop = 0, 0, stride + yield witnesses[:, start:stop], False def get_data_generators( @@ -103,9 +67,13 @@ def get_data_generators( stream = DataStream(data_dir, data_field) crawler = stream.crawl(t0, timeout) strain_q = Queue() - it = frame_iter(crawler, channels, sample_rate, strain_q) - - stride = int(batch_size * sample_rate / inference_sampling_rate) - witness_it = witness_iterator(it, stride) + witness_it = frame_iter( + crawler, + channels, + sample_rate, + inference_sampling_rate, + batch_size, + strain_q, + ) strain_it = strain_iterator(strain_q) return witness_it, strain_it diff --git a/projects/microservice/exporter/exporter/main.py b/projects/microservice/exporter/exporter/main.py index a89e663..62d2ec8 100644 --- a/projects/microservice/exporter/exporter/main.py +++ b/projects/microservice/exporter/exporter/main.py @@ -7,7 +7,7 @@ @app.route("/alive") def alive(): - return "", 200 + return "0", 200 @app.route("/export/") diff --git a/projects/microservice/monitor/pyproject.toml b/projects/microservice/monitor/pyproject.toml index 622dd29..74a443f 100644 --- a/projects/microservice/monitor/pyproject.toml +++ b/projects/microservice/monitor/pyproject.toml @@ -5,7 +5,7 @@ description = "DeepClean production data product monitoring app" authors = ["Alec Gunny "] [tool.poetry.scripts] -train = "monitor.main:main" +monitor = "monitor.main:main" [tool.poetry.dependencies] python = ">=3.8,<3.11" diff --git a/projects/microservice/run.sh b/projects/microservice/run.sh old mode 100644 new mode 100755 index 539dd2e..006158b --- a/projects/microservice/run.sh +++ b/projects/microservice/run.sh @@ -1,4 +1,4 @@ -#!/bin/bash -x +#! /bin/bash -e PROJECT=$1 @@ -6,5 +6,20 @@ prun() { echo "PROJECT=$PROJECT pinto -p $1 run -e ../.env" } tmux new-session -s deepclean-production -d "CUDA_VISIBLE_DEVICES=0 $(prun exporter) flask --app=exporter run" + +while [[ -z $(curl -s localhost:5000/alive) ]]; do + echo "Waiting for export service to come online" + sleep 2 +done + tmux split-window -v "CUDA_VISIBLE_DEVICES=0 $(prun trainer) train --typeo pyproject.toml script=train architecture=autoencoder" -tmux split-window -h "CUDA_VISIBLE_DEVICES=1 singularity exec --nv /cvmfs/singularity.opensciencegrid.org/ml4gw/tritonserver:22.12 " +tmux split-window -h " + CUDA_VISIBLE_DEVICES=1 singularity exec \ + --nv /cvmfs/singularity.opensciencegrid.org/ml4gw/hermes/tritonserver:22.12 \ + /opt/tritonserver/bin/tritonserver \ + --model-repository ~/deepclean/results/microservice/$PROJECT/model_repo \ + --model-control-mode poll \ + --repository-poll-secs 10 \ +" +tmux split-window -v "$(prun cleaner) clean --typeo pyproject.toml script=clean" +tmux split-window -v "$(prun monitor) monitor --typeo pyproject.toml script=monitor" diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index fe9abe4..b36180c 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -9,8 +9,7 @@ import numpy as np from microservice.deployment import DataStream -from microservice.frames import load_frame, read_channel -from scipy.signal.windows import hann +from microservice.frames import frame_it, read_channel from deepclean.logging import logger from deepclean.utils.channels import ChannelList, get_channels @@ -26,10 +25,10 @@ def collect_frames( sample_rate: float, timeout: Optional[float] = None, ) -> np.ndarray: + channels = get_channels(channels) stream = DataStream(data_directory, data_field) crawler = stream.crawl(t0=0, timeout=timeout) - - channels = get_channels(channels) + loader = frame_it(crawler, channels, sample_rate) start = None size = int(duration * sample_rate) @@ -37,36 +36,22 @@ def collect_frames( X = np.zeros((len(channels) - 1, 0)) y = np.zeros((0,)) - # initialize a window taper we'll use if - # we drop any frames to keep some sense - # of continuity. Include a flag to indicate - # whether the taper needs to be applied - # or not in the case of a dropped frame - zeroed = False - taper = hann(int(2 * sample_rate))[: int(sample_rate)] - # set up some parameters to inspect the # interferometer state vector ifo = channels[0].split(":")[0] state = f"{ifo}:GDS-CALIB_STATE_VECTOR" collecting = True - # clear the first couple filenames to avoid - # a race condition where the first file - # accidentally gets cycled out before we start - for _ in range(2): - next(crawler) - while not event.is_set(): - fname, strain_fname = next(crawler) + strain_fname, strain, witnesses = next(loader) # first check the logical and of the first # two bits of the state vector to ensure # that this strain data is analysis ready, # otherwise we'll be training on bad data # see: https://wiki.ligo.org/DetChar/DataQuality/O4Flags#Detector_state_segments # noqa: E501 - state_vector = read_channel(strain_fname, state, 16) - ready = all([all(map(int, f"{i:b}"[:2])) for i in state_vector.value]) + state_vector = read_channel(strain_fname, state).value + ready = ((state_vector & 3) == 3).all() if not ready and collecting: logger.warning( @@ -76,7 +61,6 @@ def collect_frames( ) start = None collecting = False - zeroed = False X = np.zeros((len(channels) - 1, 0)) y = np.zeros((0,)) continue @@ -98,42 +82,12 @@ def collect_frames( # of data for returning to the main training # process in case we interrupt at some point if start is None: - start = int(fname.stem.split("-")[-2]) - - # sometimes the witness channels get dropped. If - # this happens, create a dummy empty witness frame - if not fname.exists(): - logger.warning( - "Witness frame {} was dropped, attempting " - "to load corresponding strain frame {}".format( - fname, strain_fname - ) - ) - - shape = (len(channels) - 1, int(sample_rate)) - witnesses = np.zeros(shape) - - # if we're not already zeroed out, taper out - # the old witness data to ensure we don't - # introduce any artifacts - if not zeroed and X.size > 0: - X[:, -int(sample_rate) :] *= taper[::-1] - zeroed = True - else: - logger.debug(f"Loading frame files {fname}, {strain_fname}") - witnesses = load_frame(fname, channels[1:], sample_rate) - - # if we were in a stretch with zeroed out - # data, taper this witness back in - if zeroed: - witnesses[:, : int(sample_rate)] *= taper - zeroed = False + start = int(strain_fname.stem.split("-")[-2]) # now add this data to our running array, and # do the same for the strain data (which is # pretty much never dropped) X = np.concatenate([X, witnesses], axis=1) - strain = load_frame(strain_fname, channels[0], sample_rate) y = np.concatenate([y, strain]) # pass these arrays out to the training process diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 5a733b1..eda8fcf 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -8,6 +8,8 @@ import numpy as np from gwpy.timeseries import TimeSeries +from scipy.signal import resample +from scipy.signal.windows import hann from deepclean.logging import logger @@ -174,13 +176,7 @@ def __next__(self): return fname -def resample(x: TimeSeries, sample_rate: float): - if x.sample_rate.value != sample_rate: - return x.resample(sample_rate) - return x - - -def read_channel(fname, channel, sample_rate): +def read_channel(fname, channel): for i in range(3): try: with lock: @@ -210,8 +206,7 @@ def read_channel(fname, channel, sample_rate): else: raise - x = resample(x, sample_rate) - if len(x) != sample_rate: + if len(x) != x.sample_rate.value: logger.warning( "Channel {} in file {} got corrupted with " "length {}, attempting reread {}".format( @@ -229,23 +224,117 @@ def read_channel(fname, channel, sample_rate): ) -def load_frame( - fname: str, channels: Union[str, Iterable[str]], sample_rate: float -) -> np.ndarray: +def load_frame(fname: str, channels: Union[str, Iterable[str]]) -> np.ndarray: """Load the indicated channels from the indicated frame file""" if isinstance(channels, str): # if we don't have multiple channels, then just grab the data - data = read_channel(fname, channels, sample_rate).value + data = read_channel(fname, channels).value else: data = [] for channel in channels: - x = read_channel(fname, channel, sample_rate) + x = read_channel(fname, channel) data.append(x.value) data = np.stack(data) # return as the expected type - return data.astype("float32") + return data + + +@dataclass +class Buffer: + def __init__(self, target_sample_rate: float): + self.target_sample_rate = target_sample_rate + self.sample_rate = None + self.x = None + self.taper = None + self.zeroed = False + + def update(self, x): + if self.sample_rate is None: + if x is None: + return + self.sample_rate = x.sample_rate.value + self.x = x.value + + fs = int(self.sample_rate) + self.taper = hann(2 * fs)[:fs] + return + + if x is None: + if not self.zeroed: + self.x *= self.taper[::-1] + self.x = np.append(self.x, np.zeros(self.sample_rate)) + self.zeroed = True + else: + x = x.value + if self.zeroed: + x *= self.taper + self.x = np.append(self.x, x) + self.zeroed = False + + dur = len(self.x) / self.sample_rate + if dur >= 3: + length = int(dur * self.target_sample_rate) + x = resample(self.x, length, window="hann") + + size = int(self.target_sample_rate) + x = x[-2 * size : -size] + self.x = self.x[int(self.sample_rate) :] + return x + return None + + +def frame_it(fname_it, channels: Iterable[str], sample_rate: float): + """ + Load frames from the filenames returned by an + iterator, using a 3 second buffer to reduce + the edge effects incurred by resampling. This + introduces a 1 second latency into the data + being used for inference, since we need to wait + for the next frame to become available for + resampling purposes. + """ + strain, *witnesses = channels + buffers = {i: Buffer(sample_rate) for i in channels} + fname_buffer = None + + i = 0 + for witness_fname, strain_fname in fname_it: + if i < 2: + i += 1 + continue + + if not witness_fname.exists(): + logger.warning( + "Witness frame {} was dropped, attempting " + "to load corresponding strain frame {}".format( + witness_fname, strain_fname + ) + ) + else: + logger.debug( + f"Loading frame files {witness_fname}, {strain_fname}" + ) + + x = read_channel(strain_fname, strain) + y = buffers[strain].update(x) + + X = [] + for channel in witnesses: + if not witness_fname.exists(): + x = buffers[channel].update(None) + else: + x = read_channel(witness_fname, channel) + x = buffers[channel].update(x) + + if x is not None: + X.append(x) + + if X: + X = np.stack(X) + yield fname_buffer, y, X + fname_buffer = strain_fname @dataclass From 0b4f87ecfec94c670ff37d19e65fc1e2ea0bf8c7 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Tue, 23 May 2023 09:40:41 -0500 Subject: [PATCH 24/31] dealing with missing strain and increasing documentation --- .../cleaner/cleaner/dataloader.py | 59 ++++++++- projects/microservice/pyproject.toml | 4 +- .../microservice/trainer/trainer/checks.py | 6 +- .../trainer/trainer/dataloader.py | 8 +- .../microservice/utils/microservice/frames.py | 120 +++++++++++++++--- 5 files changed, 169 insertions(+), 28 deletions(-) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 0be2120..4921c06 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -1,7 +1,7 @@ import time from pathlib import Path from queue import Empty, Queue -from typing import Iterable, List +from typing import Iterable, List, Tuple import numpy as np from microservice.deployment import DataStream @@ -20,14 +20,50 @@ def strain_iterator(q: Queue): yield response -def frame_iter( +def batch_iter( crawler: Iterable, channels: List[str], sample_rate: float, inference_sampling_rate: float, batch_size: int, q: Queue, -): +) -> Tuple[np.ndarray, bool]: + """ + Iterate through the frame files returned by + a crawler and load the relevant witness and + strain data, placing the strain data into a + `Queue` and breaking the witness timeseries + up into `batch_size` updates of size + `sample_rate // inference_sampling_rate` each. + + Arguments: + crawler: + Iterator which returns strain and + witness frame file names + channels: + Channels to load from the frame files, + with the strain channel name first + sample_rate: + Sample rate at which to resample all + the loaded channels + inference_sampling_rate: + Rate at which to sample input windows to + pass to DeepClean from the timeseries + batch_size: + Number of windows on which DeepClean will + perform inference at once + q: + `Queue` object into which strain data and + its associated filename will be placed + for use by a cleaning callback thread. + Returns: + Array containing one batch worth of updates + for streaming to the inference server + Flag indicating whether this represents this + batch represents the end of a sequence of + updates, after which the associated + snapshot state should be reset. + """ stride = int(batch_size * sample_rate / inference_sampling_rate) it = frame_it(crawler, channels, sample_rate) @@ -38,6 +74,12 @@ def frame_iter( while True: start = idx * stride stop = (idx + 1) * stride + + # check if the _next_ batch of updates + # will require grabbing more data, that + # way we can check if our data iterator + # has been exhausted and can return True + # for the sequence_end flag if (idx + 2) * stride > witnesses.shape[-1]: try: strain_fname, strain, update = next(it) @@ -46,9 +88,18 @@ def frame_iter( q.put(None) break else: + # put the strain data and its associated + # filename in the queue that can be passed + # to our callback thread q.put((strain, strain_fname)) + + # slough off any old witness data that + # we no longer need and append the new data witnesses = witnesses[:, start:] witnesses = np.concatenate([witnesses, update], axis=1) + + # reset all our indices to reflect the + # new witness data array idx, start, stop = 0, 0, stride yield witnesses[:, start:stop], False @@ -67,7 +118,7 @@ def get_data_generators( stream = DataStream(data_dir, data_field) crawler = stream.crawl(t0, timeout) strain_q = Queue() - witness_it = frame_iter( + witness_it = batch_iter( crawler, channels, sample_rate, diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index b9fb89d..86cfd44 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -76,8 +76,8 @@ export_endpoint = "${base.export_endpoint}" # data parameters channels = "${base.livingston_60Hz_channels}" -train_duration = 1024 -retrain_cadence = 512 +train_duration = 4096 +retrain_cadence = 2048 sample_rate = "${base.sample_rate}" valid_frac = 0.25 diff --git a/projects/microservice/trainer/trainer/checks.py b/projects/microservice/trainer/trainer/checks.py index c1504ac..5ff843a 100644 --- a/projects/microservice/trainer/trainer/checks.py +++ b/projects/microservice/trainer/trainer/checks.py @@ -22,14 +22,14 @@ def check_coherences( target frequency range. """ logger.info("Evaluating coherece of strain channel with witnesses") - psd_kwargs = dict(fftlength=fftlength, window="hann", method="median") + psd_kwargs = dict(fftlength=fftlength, window="hann") y = TimeSeries(y, sample_rate=sample_rate) - pyy = y.psd(**psd_kwargs) + pyy = y.psd(method="median", **psd_kwargs) coherences = {} for x, channel in zip(X, channels[1:]): x = TimeSeries(x, sample_rate=sample_rate) - pxx = x.psd(**psd_kwargs) + pxx = x.psd(method="median", **psd_kwargs) pxy = y.csd(x, **psd_kwargs) coh = pxy**2 / (pxx * pyy) coherences[channel] = coh.value**0.5 diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index b36180c..2fb4ff0 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -50,8 +50,12 @@ def collect_frames( # that this strain data is analysis ready, # otherwise we'll be training on bad data # see: https://wiki.ligo.org/DetChar/DataQuality/O4Flags#Detector_state_segments # noqa: E501 - state_vector = read_channel(strain_fname, state).value - ready = ((state_vector & 3) == 3).all() + # Allow ourselves to accept a dropped frame, + # but inherit the readiness status of the + # previous non-dropped frame + if strain_fname.exists(): + state_vector = read_channel(strain_fname, state).value + ready = ((state_vector & 3) == 3).all() if not ready and collecting: logger.warning( diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index eda8fcf..0fd925c 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -3,7 +3,6 @@ import time from dataclasses import dataclass from pathlib import Path -from threading import Lock from typing import Iterable, Literal, Optional, Tuple, Union import numpy as np @@ -25,8 +24,6 @@ pattern = "{prefix}-{start}-{duration}.{suffix}".format(**groups) fname_re = re.compile(pattern) -lock = Lock() - def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: """Use the name of a frame file to infer its initial timestamp and length @@ -123,6 +120,32 @@ class DroppedFrames(Exception): @dataclass class FrameCrawler: + """ + Iterable which will crawl through a directory + containing a stream of frame data and will + return the frame filenames in chronological order + based on the timestamps in their filenames. + + Args: + data_dir: Path to the frame directory + t0: First timestamp that should be used + from frame directory. If left as `None` + or set to `0`, will begin with the first frame + in `data_dir`. If set to `-1`, will begin + with the last frame in `data_dir`. + timeout: Time after which a frame that is expected + to exist but is not available should be considered + dropped. If left as `None`, will wait indefinitely + for expected frames to exist. If set to `0`, will + raise a `StopIteration` as soon as an expected file + does not exist (useful for cases where you don't + expect a directory to be updated with new files). + max_dropped_frames: The number of consecutive dropped + frames allowed before an error is raised indicating + that the `data_dir` is no longer producing the + expected data stream + """ + data_dir: Path t0: Optional[float] = None timeout: Optional[float] = None @@ -144,13 +167,18 @@ def __iter__(self): return self def __next__(self): + # get the full path to the next frame fname = self.file_format.get_name(self.t0, self.length) fname = self.data_dir / fname + # begin a timer to check if frame has been + # dropped or not start_time = time.time() i, interval = 1, 3 while not fname.exists(): time.sleep(1e-3) + + # log that we're still waiting at some fixed interval if (time.time() - start_time) > (i * interval): logging.debug( "Waiting for frame files for timestamp {}, " @@ -159,28 +187,46 @@ def __next__(self): i += 1 if self.timeout is None: + # never break if timeout is None continue elif self.timeout == 0: + # if we have no waiting tolerance, break + # by raising a `StopIteration` raise StopIteration elif (time.time() - start_time) > self.timeout: + # otherwise if we've hit our timeout, increment + # our number of consecutive dropped frames self._dropped_frames += 1 + + # raise an error if we've hit our dropped frame limit if self._dropped_frames > self.max_dropped_frames: raise DroppedFrames( f"No frame file {fname} after {self.timeout}s" ) break else: + # the frame existed before timeout, so reset our counter self._dropped_frames = 0 + # increment our timestamp to the next file, and + # *always* return a filename, even if it doesn't + # exist, for downstream use cases to decide how + # to handle missing files self.t0 += self.length return fname -def read_channel(fname, channel): +def read_channel(fname: Union[str, Path], channel: str): + """ + Read a specific channel from a frame file. Encodes + a lot of gross logic about how to handle errors that + occur when trying to read frames that are still being + written. + """ + for i in range(3): try: - with lock: - x = TimeSeries.read(fname, channel=channel) + x = TimeSeries.read(fname, channel=channel) except ValueError as e: if str(e) == ( "Cannot generate TimeSeries with 2-dimensional data" @@ -243,45 +289,82 @@ def load_frame(fname: str, channels: Union[str, Iterable[str]]) -> np.ndarray: @dataclass class Buffer: + """ + Maintains a 3 second buffer of a particular + channel's timeseries that is intended to be + resampled to a given `target_sample_rate`, and + which uses the buffer to resample the middle + second at each iteration to avoid edge effects + from resampling. + """ + def __init__(self, target_sample_rate: float): self.target_sample_rate = target_sample_rate + + # these will all be updated using the first + # timeseries used to update the buffer self.sample_rate = None self.x = None self.taper = None self.zeroed = False - def update(self, x): + def update(self, fname, channel): + if not fname.exists(): + x = None + else: + x = read_channel(fname, channel) + + # if this is the first time this buffer + # is being updated, infer the original + # sample rate of this channel from the + # TimeSeries and then exit if self.sample_rate is None: if x is None: return self.sample_rate = x.sample_rate.value self.x = x.value + # create a half Hann window for tapering + # data in and out when frames are missing fs = int(self.sample_rate) self.taper = hann(2 * fs)[:fs] return if x is None: + # passing None indicates that this frame was + # dropped, so taper out the existing data + # and just append zeros to our buffer if not self.zeroed: - self.x *= self.taper[::-1] + self.x[-len(self.taper) :] *= self.taper[::-1] self.x = np.append(self.x, np.zeros(self.sample_rate)) self.zeroed = True else: + # otherwise append this timeseries data to + # our buffer, potentially tapering it back + # in if the previous frame was dropped x = x.value if self.zeroed: x *= self.taper self.x = np.append(self.x, x) self.zeroed = False + # check if we have enough data to resample + # the center of our buffer and return it dur = len(self.x) / self.sample_rate if dur >= 3: length = int(dur * self.target_sample_rate) x = resample(self.x, length, window="hann") + # slice out the middle second of our buffer + # to return, then cut off the earliest second + # that we no longer need size = int(self.target_sample_rate) x = x[-2 * size : -size] self.x = self.x[int(self.sample_rate) :] return x + + # otherwise indicate that we don't have + # enough data to return return None @@ -317,23 +400,26 @@ def frame_it(fname_it, channels: Iterable[str], sample_rate: float): f"Loading frame files {witness_fname}, {strain_fname}" ) - x = read_channel(strain_fname, strain) - y = buffers[strain].update(x) - + y = buffers[strain].update(strain_fname, strain) X = [] for channel in witnesses: - if not witness_fname.exists(): - x = buffers[channel].update(None) - else: - x = read_channel(witness_fname, channel) - x = buffers[channel].update(x) + x = buffers[channel].update(witness_fname, channel) + # if the buffer update returned a frame that's + # been resampled and is ready for use, append it if x is not None: X.append(x) - if X: + # if all our buffers returned usable data, + # concatenate them into a single array and + # return it along with the strain filename + # and associated data + if len(X) == len(witnesses): X = np.stack(X) yield fname_buffer, y, X + + # update our strain filename buffer to + # represent the current filename fname_buffer = strain_fname From d88e4e7acb353fc1326838b2061c8b51be43ae9e Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 5 Jun 2023 17:24:20 -0500 Subject: [PATCH 25/31] simplifying things for production deployment --- .../cleaner/cleaner/dataloader.py | 4 +- projects/microservice/cleaner/cleaner/main.py | 13 +- .../exporter/exporter/exporter.py | 5 + projects/microservice/exporter/poetry.lock | 162 +++++++++++------- projects/microservice/exporter/pyproject.toml | 1 + projects/microservice/monitor/monitor/main.py | 4 +- projects/microservice/pyproject.toml | 109 ++++++------ .../trainer/trainer/dataloader.py | 8 +- projects/microservice/trainer/trainer/main.py | 12 +- .../utils/microservice/deployment.py | 23 ++- .../microservice/utils/microservice/frames.py | 6 + 11 files changed, 200 insertions(+), 147 deletions(-) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 4921c06..e93a6ea 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -106,7 +106,7 @@ def batch_iter( def get_data_generators( data_dir: Path, - data_field: str, + ifo: str, channels: List[str], sample_rate: float, inference_sampling_rate: float, @@ -115,7 +115,7 @@ def get_data_generators( timeout: float = 10, ): t0 = 0 if start_first else -1 - stream = DataStream(data_dir, data_field) + stream = DataStream(data_dir, ifo) crawler = stream.crawl(t0, timeout) strain_q = Queue() witness_it = batch_iter( diff --git a/projects/microservice/cleaner/cleaner/main.py b/projects/microservice/cleaner/cleaner/main.py index dd94f1b..e17d7cb 100644 --- a/projects/microservice/cleaner/cleaner/main.py +++ b/projects/microservice/cleaner/cleaner/main.py @@ -1,15 +1,15 @@ import time from pathlib import Path -from typing import Iterable, Optional, Union +from typing import Dict, Iterable, Optional, Union from cleaner.dataloader import get_data_generators from cleaner.utils import wait_for_server from cleaner.writer import ASDRMonitor, Writer from microservice.deployment import Deployment +from microservice.frames import get_channels from deepclean.infer.callback import Callback, State from deepclean.logging import logger -from deepclean.utils.channels import ChannelList, get_channels from hermes.aeriel.client import InferenceClient from typeo import scriptify @@ -19,7 +19,7 @@ def main( # IO args run_directory: Path, data_directory: Path, - data_field: str, + ifo: str, export_endpoint: str, # Data args sample_rate: float, @@ -27,7 +27,7 @@ def main( inference_sampling_rate: float, batch_size: int, inference_rate: float, - channels: ChannelList, + channels: Dict[str, str], freq_low: Union[float, Iterable[float]], freq_high: Union[float, Iterable[float]], # Triton args @@ -131,7 +131,8 @@ def main( deployment = Deployment(run_directory) log_file = deployment.log_directory / "infer.log" logger.set_logger("DeepClean infer", log_file, verbose) - channels = get_channels(channels) + + channels = get_channels(channels[ifo]) # don't start loading data until the server # is online and a trained version of DeepClean @@ -139,7 +140,7 @@ def main( wait_for_server(triton_endpoint) witness_it, strain_it = get_data_generators( data_directory, - data_field, + ifo, channels, sample_rate=sample_rate, inference_sampling_rate=inference_sampling_rate, diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index 2e16ba1..d226822 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -6,6 +6,7 @@ import torch import tritonclient.grpc.model_config_pb2 as model_config +from dotenv import load_dotenv from microservice.deployment import Deployment import hermes.quiver as qv @@ -45,6 +46,10 @@ def from_config( if script is None: script = "exporter" + dotenv_file = config.parent / ".env" + if dotenv_file.exists(): + load_dotenv(dotenv_file) + args.append(str(config)) if script is not None: args.append(f"script={script}") diff --git a/projects/microservice/exporter/poetry.lock b/projects/microservice/exporter/poetry.lock index 1511c61..b6263ee 100644 --- a/projects/microservice/exporter/poetry.lock +++ b/projects/microservice/exporter/poetry.lock @@ -16,7 +16,7 @@ multidict = ">=4.5,<7.0" yarl = ">=1.0,<2.0" [package.extras] -speedups = ["aiodns", "brotli", "cchardet"] +speedups = ["Brotli", "aiodns", "cchardet"] [[package]] name = "aiosignal" @@ -44,11 +44,11 @@ pyerfa = ">=2.0" PyYAML = ">=3.13" [package.extras] -all = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "certifi", "dask", "h5py", "pyarrow (>=5.0.0)", "beautifulsoup4", "html5lib", "bleach", "pandas", "sortedcontainers", "pytz", "jplephem", "mpmath", "asdf (>=2.10.0)", "bottleneck", "ipython (>=4.2)", "pytest (>=7.0)", "typing-extensions (>=3.10.0.1)", "fsspec[http] (>=2022.8.2)", "s3fs (>=2022.8.2)"] -docs = ["sphinx", "sphinx-astropy (>=1.6)", "pytest (>=7.0)", "scipy (>=1.3)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "sphinx-changelog (>=1.2.0)", "Jinja2 (>=3.0)"] -recommended = ["scipy (>=1.5)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)"] -test = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist"] -test_all = ["pytest (>=7.0)", "pytest-doctestplus (>=0.12)", "pytest-astropy-header (>=0.2.1)", "pytest-astropy (>=0.10)", "pytest-xdist", "objgraph", "ipython (>=4.2)", "coverage", "skyfield (>=1.20)", "sgp4 (>=2.3)"] +all = ["asdf (>=2.10.0)", "beautifulsoup4", "bleach", "bottleneck", "certifi", "dask[array]", "fsspec[http] (>=2022.8.2)", "h5py", "html5lib", "ipython (>=4.2)", "jplephem", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "mpmath", "pandas", "pyarrow (>=5.0.0)", "pytest (>=7.0)", "pytz", "s3fs (>=2022.8.2)", "scipy (>=1.5)", "sortedcontainers", "typing-extensions (>=3.10.0.1)"] +docs = ["Jinja2 (>=3.0)", "matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "pytest (>=7.0)", "scipy (>=1.3)", "sphinx", "sphinx-astropy (>=1.6)", "sphinx-changelog (>=1.2.0)"] +recommended = ["matplotlib (>=3.1,!=3.4.0,!=3.5.2)", "scipy (>=1.5)"] +test = ["pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist"] +test-all = ["coverage[toml]", "ipython (>=4.2)", "objgraph", "pytest (>=7.0)", "pytest-astropy (>=0.10)", "pytest-astropy-header (>=0.2.1)", "pytest-doctestplus (>=0.12)", "pytest-xdist", "sgp4 (>=2.3)", "skyfield (>=1.20)"] [[package]] name = "async-timeout" @@ -67,12 +67,11 @@ optional = false python-versions = ">=3.6" [package.extras] -cov = ["attrs", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] -dev = ["attrs"] -docs = ["furo", "sphinx", "myst-parser", "zope-interface", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier"] -tests = ["attrs", "zope-interface"] -tests-no-zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] -tests_no_zope = ["hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist", "cloudpickle", "mypy (>=0.971,<0.990)", "pytest-mypy-plugins"] +cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] +dev = ["attrs[docs,tests]"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] +tests = ["attrs[tests-no-zope]", "zope.interface"] +tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] [[package]] name = "bilby" @@ -97,9 +96,9 @@ scipy = ">=1.5" tqdm = "*" [package.extras] -all = ["cpnest (>=0.9.4)", "dynesty", "emcee", "nestle", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "ptemcee", "pymc (>=4.0.0)", "pymultinest", "kombine", "ultranest (>=3.0.0)", "dnest4", "nessai (>=0.7.0)", "schwimmbad", "zeus-mcmc (>=2.3.0)", "astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw", "scikit-learn", "nflows", "celerite", "george", "plotly"] -gw = ["astropy (>=5)", "lalsuite", "gwpy", "tables", "pyfftw"] -mcmc = ["scikit-learn", "nflows"] +all = ["astropy (>=5)", "celerite", "cpnest (>=0.9.4)", "dnest4", "dynesty", "emcee", "george", "gwpy", "kombine", "lalsuite", "nessai (>=0.7.0)", "nestle", "nflows", "plotly", "ptemcee", "ptmcmcsampler (!=0.0.0,!=2.1.0)", "pyfftw", "pymc (>=4.0.0)", "pymultinest", "schwimmbad", "scikit-learn", "tables", "ultranest (>=3.0.0)", "zeus-mcmc (>=2.3.0)"] +gw = ["astropy (>=5)", "gwpy", "lalsuite", "pyfftw", "tables"] +mcmc = ["nflows", "scikit-learn"] [[package]] name = "bilby-cython" @@ -190,8 +189,8 @@ numpy = ">=1.16" [package.extras] bokeh = ["bokeh", "chromedriver", "selenium"] docs = ["furo", "sphinx-copybutton"] -mypy = ["contourpy", "docutils-stubs", "mypy (==0.991)", "types-pillow"] -test = ["matplotlib", "pillow", "pytest"] +mypy = ["contourpy[bokeh]", "docutils-stubs", "mypy (==0.991)", "types-Pillow"] +test = ["Pillow", "matplotlib", "pytest"] test-no-images = ["pytest"] [[package]] @@ -207,9 +206,9 @@ matplotlib = ">=2.1" [package.extras] arviz = ["arviz (>=0.9)"] -dev = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml", "arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme", "pre-commit", "flake8", "pep517", "twine"] -docs = ["arviz (>=0.9)", "sphinx (>=1.7.5)", "pandoc", "myst-nb", "sphinx-book-theme"] -test = ["pytest (>=3.6)", "pytest-cov (>=2.6.1)", "black", "isort", "toml"] +dev = ["arviz (>=0.9)", "black", "flake8", "isort", "myst-nb", "pandoc", "pep517", "pre-commit", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "sphinx (>=1.7.5)", "sphinx-book-theme", "toml", "twine"] +docs = ["arviz (>=0.9)", "myst-nb", "pandoc", "sphinx (>=1.7.5)", "sphinx-book-theme"] +test = ["black", "isort", "pytest (>=3.6)", "pytest-cov (>=2.6.1)", "toml"] [[package]] name = "cryptography" @@ -224,11 +223,11 @@ cffi = ">=1.12" [package.extras] docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"] -docstest = ["pyenchant (>=1.6.11)", "twine (>=1.12.0)", "sphinxcontrib-spelling (>=4.0.1)"] -pep8test = ["black", "ruff", "mypy", "check-manifest"] +docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"] +pep8test = ["black", "check-manifest", "mypy", "ruff"] sdist = ["setuptools-rust (>=0.11.4)"] ssh = ["bcrypt (>=3.1.5)"] -test = ["pytest (>=6.2.0)", "pytest-shard (>=0.1.2)", "pytest-benchmark", "pytest-cov", "pytest-subtests", "pytest-xdist", "pretend", "iso8601"] +test = ["iso8601", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-shard (>=0.1.2)", "pytest-subtests", "pytest-xdist"] test-randomorder = ["pytest-randomly"] tox = ["tox"] @@ -351,7 +350,7 @@ numpy = "*" [package.extras] extras = ["h5py", "scipy"] -tests = ["pytest", "pytest-cov", "coverage"] +tests = ["coverage[toml]", "pytest", "pytest-cov"] [[package]] name = "exceptiongroup" @@ -392,9 +391,9 @@ optional = false python-versions = ">=3.8" [package.extras] -all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=15.0.0)", "xattr"] +all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.0.0)", "xattr", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["scipy", "munkres"] +interpolatable = ["munkres", "scipy"] lxml = ["lxml (>=4.0,<5)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] @@ -403,7 +402,7 @@ symfont = ["sympy"] type1 = ["xattr"] ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=15.0.0)"] -woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "frozenlist" @@ -430,10 +429,10 @@ setuptools = "*" [package.extras] dnspython = ["dnspython (>=1.16.0,<2.0)", "idna"] -docs = ["repoze-sphinx-autointerface", "sphinxcontrib-programoutput", "zope-schema"] +docs = ["repoze.sphinx.autointerface", "sphinxcontrib-programoutput", "zope.schema"] monitor = ["psutil (>=5.7.0)"] -recommended = ["cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "backports-socketpair", "psutil (>=5.7.0)"] -test = ["requests", "objgraph", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "selectors2", "futures", "mock", "backports-socketpair", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "psutil (>=5.7.0)"] +recommended = ["backports.socketpair", "cffi (>=1.12.2)", "dnspython (>=1.16.0,<2.0)", "idna", "psutil (>=5.7.0)", "selectors2"] +test = ["backports.socketpair", "cffi (>=1.12.2)", "contextvars (==2.4)", "coverage (>=5.0)", "coveralls (>=1.7.0)", "dnspython (>=1.16.0,<2.0)", "futures", "idna", "mock", "objgraph", "psutil (>=5.7.0)", "requests", "selectors2"] [[package]] name = "geventhttpclient" @@ -458,7 +457,7 @@ optional = false python-versions = 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"sha256:f5971a9226b701070a4bf2c38c89e5a3f0d64de8debda981d1db98583009122a"}, +] python-rapidjson = [ {file = "python-rapidjson-1.10.tar.gz", hash = "sha256:acfecbf5edb91ec72a20a125de7f56b8c2f6161eff4c65382c8ee6a2484d3540"}, {file = "python_rapidjson-1.10-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1db7b0af882999f5685eb7046a0f3b3aca5d55a3e84b3089747d29a4ec6fdade"}, diff --git a/projects/microservice/exporter/pyproject.toml b/projects/microservice/exporter/pyproject.toml index b820d15..6ce675d 100644 --- a/projects/microservice/exporter/pyproject.toml +++ b/projects/microservice/exporter/pyproject.toml @@ -20,6 +20,7 @@ microservice = {path = "../utils", develop = true} "deepclean.export" = {path = "../../../libs/export", develop = true} "deepclean.logging" = {path = "../../../libs/logging", develop = true} "deepclean.utils" = {path = "../../../libs/utils", develop = true} +python-dotenv = "^1.0.0" [tool.poetry.dependencies."hermes.quiver"] path = "../../../hermes/hermes/hermes.quiver" diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py index d147063..2487d84 100644 --- a/projects/microservice/monitor/monitor/main.py +++ b/projects/microservice/monitor/monitor/main.py @@ -16,7 +16,7 @@ def main( # paths run_directory: Path, data_directory: Path, - data_field: str, + ifo: str, export_endpoint: str, # data/analysis args channels: ChannelList, @@ -60,7 +60,7 @@ def main( # 2. Remove files to keep frame directory at fixed max length # 3. Monitor frames cleaned by the canary model to ensure that # it's in spec before moving it into production - stream = DataStream(data_directory, data_field) + stream = DataStream(data_directory, ifo) files = [] buffer, t0 = TimeSeriesDict(), None diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 86cfd44..647eee8 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -2,58 +2,12 @@ # global paths run_directory = "${PROJECT_DIR}" data_directory = "${DATA_DIR}" -data_field = "${DATA_FIELD}" export_endpoint = "localhost:5000" triton_endpoint = "localhost:8001" # global data args -hanford_60Hz_channels = [ - "H1:GDS-CALIB_STRAIN", - "H1:PEM-CS_MAINSMON_EBAY_1_DQ", - "H1:ASC-INP1_P_OUT_DQ", - "H1:ASC-INP1_Y_OUT_DQ", - "H1:ASC-MICH_P_OUT_DQ", - "H1:ASC-MICH_Y_OUT_DQ", - "H1:ASC-PRC1_P_OUT_DQ", - "H1:ASC-PRC1_Y_OUT_DQ", - "H1:ASC-PRC2_P_OUT_DQ", - "H1:ASC-PRC2_Y_OUT_DQ", - "H1:ASC-SRC1_P_OUT_DQ", - "H1:ASC-SRC1_Y_OUT_DQ", - "H1:ASC-SRC2_P_OUT_DQ", - "H1:ASC-SRC2_Y_OUT_DQ", - "H1:ASC-DHARD_P_OUT_DQ", - "H1:ASC-DHARD_Y_OUT_DQ", - "H1:ASC-CHARD_P_OUT_DQ", - "H1:ASC-CHARD_Y_OUT_DQ", - "H1:ASC-DSOFT_P_OUT_DQ", - "H1:ASC-DSOFT_Y_OUT_DQ", - "H1:ASC-CSOFT_P_OUT_DQ", - "H1:ASC-CSOFT_Y_OUT_DQ" -] -livingston_60Hz_channels = [ - "L1:GDS-CALIB_STRAIN", - "L1:PEM-CS_MAINSMON_EBAY_1_DQ", - "L1:ASC-CHARD_P_OUT_DQ", - "L1:ASC-CHARD_Y_OUT_DQ", - "L1:ASC-CSOFT_P_OUT_DQ", - "L1:ASC-DHARD_P_OUT_DQ", - "L1:ASC-DHARD_Y_OUT_DQ", - "L1:ASC-DSOFT_P_OUT_DQ", - "L1:ASC-INP1_P_OUT_DQ", - "L1:ASC-MICH_P_OUT_DQ", - "L1:ASC-MICH_Y_OUT_DQ", - "L1:ASC-PRC1_P_OUT_DQ", - "L1:ASC-PRC1_Y_OUT_DQ", - "L1:ASC-PRC2_P_OUT_DQ", - "L1:ASC-PRC2_Y_OUT_DQ", - "L1:ASC-SRC1_P_OUT_DQ", - "L1:ASC-SRC1_Y_OUT_DQ", - "L1:ASC-SRC2_P_OUT_DQ", - "L1:ASC-SRC2_Y_OUT_DQ", -] - +ifo = "${IFO}" sample_rate = 4096 freq_low = 58 freq_high = 62 @@ -66,16 +20,63 @@ inference_batch_size = 128 # global misc args verbose = false +[tool.typeo.base.channels] +H1 = """\ + H1:GDS-CALIB_STRAIN,\ + H1:PEM-CS_MAINSMON_EBAY_1_DQ,\ + H1:ASC-INP1_P_OUT_DQ,\ + H1:ASC-INP1_Y_OUT_DQ,\ + H1:ASC-MICH_P_OUT_DQ,\ + H1:ASC-MICH_Y_OUT_DQ,\ + H1:ASC-PRC1_P_OUT_DQ,\ + H1:ASC-PRC1_Y_OUT_DQ,\ + H1:ASC-PRC2_P_OUT_DQ,\ + H1:ASC-PRC2_Y_OUT_DQ,\ + H1:ASC-SRC1_P_OUT_DQ,\ + H1:ASC-SRC1_Y_OUT_DQ,\ + H1:ASC-SRC2_P_OUT_DQ,\ + H1:ASC-SRC2_Y_OUT_DQ,\ + H1:ASC-DHARD_P_OUT_DQ,\ + H1:ASC-DHARD_Y_OUT_DQ,\ + H1:ASC-CHARD_P_OUT_DQ,\ + H1:ASC-CHARD_Y_OUT_DQ,\ + H1:ASC-DSOFT_P_OUT_DQ,\ + H1:ASC-DSOFT_Y_OUT_DQ,\ + H1:ASC-CSOFT_P_OUT_DQ,\ + H1:ASC-CSOFT_Y_OUT_DQ\ +""" +L1 = """\ + L1:GDS-CALIB_STRAIN,\ + L1:PEM-CS_MAINSMON_EBAY_1_DQ,\ + L1:ASC-CHARD_P_OUT_DQ,\ + L1:ASC-CHARD_Y_OUT_DQ,\ + L1:ASC-CSOFT_P_OUT_DQ,\ + L1:ASC-DHARD_P_OUT_DQ,\ + L1:ASC-DHARD_Y_OUT_DQ,\ + L1:ASC-DSOFT_P_OUT_DQ,\ + L1:ASC-INP1_P_OUT_DQ,\ + L1:ASC-MICH_P_OUT_DQ,\ + L1:ASC-MICH_Y_OUT_DQ,\ + L1:ASC-PRC1_P_OUT_DQ,\ + L1:ASC-PRC1_Y_OUT_DQ,\ + L1:ASC-PRC2_P_OUT_DQ,\ + L1:ASC-PRC2_Y_OUT_DQ,\ + L1:ASC-SRC1_P_OUT_DQ,\ + L1:ASC-SRC1_Y_OUT_DQ,\ + L1:ASC-SRC2_P_OUT_DQ,\ + L1:ASC-SRC2_Y_OUT_DQ,\ +""" + [tool.typeo.scripts.train] # paths run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" -data_field = "${base.data_field}" +ifo = "${base.ifo}" export_endpoint = "${base.export_endpoint}" # data parameters -channels = "${base.livingston_60Hz_channels}" +channels = "${base.channels}" train_duration = 4096 retrain_cadence = 2048 sample_rate = "${base.sample_rate}" @@ -113,7 +114,7 @@ architecture.autoencoder = {} run_directory = "${base.run_directory}" # data args -channels = "${base.livingston_60Hz_channels}" +channels = "${base.channels}" kernel_length = 1 sample_rate = "${base.sample_rate}" @@ -136,12 +137,12 @@ verbose = "${base.verbose}" # paths run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" -data_field = "${base.data_field}" +ifo = "${base.ifo}" export_endpoint = "${base.export_endpoint}" # data args sample_rate = "${base.sample_rate}" -channels = "${base.livingston_60Hz_channels}" +channels = "${base.channels}" freq_low = "${base.freq_low}" freq_high = "${base.freq_high}" frame_length = 1 @@ -168,11 +169,11 @@ verbose = true # "${base.verbose}" # paths run_directory = "${base.run_directory}" data_directory = "${base.data_directory}" -data_field = "${base.data_field}" +ifo = "${base.ifo}" export_endpoint = "${base.export_endpoint}" # analysis args -channels = "${base.livingston_60Hz_channels}" +channels = "${base.channels}" sample_rate = "${base.sample_rate}" asd_segment_length = 10 fftlength = 8 diff --git a/projects/microservice/trainer/trainer/dataloader.py b/projects/microservice/trainer/trainer/dataloader.py index 2fb4ff0..f8c5b45 100644 --- a/projects/microservice/trainer/trainer/dataloader.py +++ b/projects/microservice/trainer/trainer/dataloader.py @@ -18,7 +18,7 @@ def collect_frames( event: Event, data_directory: Path, - data_field: str, + ifo: str, channels: ChannelList, duration: float, cadence: float, @@ -26,7 +26,7 @@ def collect_frames( timeout: Optional[float] = None, ) -> np.ndarray: channels = get_channels(channels) - stream = DataStream(data_directory, data_field) + stream = DataStream(data_directory, ifo) crawler = stream.crawl(t0=0, timeout=timeout) loader = frame_it(crawler, channels, sample_rate) start = None @@ -112,7 +112,7 @@ def collect_frames( @dataclass class DataCollector: data_directory: Path - data_field: str + ifo: str log_directory: Path channels: ChannelList duration: float @@ -190,7 +190,7 @@ def __call__(self): it = collect_frames( self.event, self.data_directory, - self.data_field, + self.ifo, self.channels, self.duration, self.cadence, diff --git a/projects/microservice/trainer/trainer/main.py b/projects/microservice/trainer/trainer/main.py index 8e08385..d6e005f 100644 --- a/projects/microservice/trainer/trainer/main.py +++ b/projects/microservice/trainer/trainer/main.py @@ -1,15 +1,15 @@ from pathlib import Path -from typing import Callable, Optional +from typing import Callable, Dict, Optional import numpy as np from microservice.deployment import Deployment, ExportClient +from microservice.frames import get_channels from trainer.checks import data_checks from trainer.dataloader import DataCollector from deepclean.architectures import architectures from deepclean.logging import logger from deepclean.trainer.trainer import train -from deepclean.utils.channels import ChannelList, get_channels from typeo import scriptify from typeo.utils import make_dummy @@ -74,9 +74,9 @@ def train_on_segment( def main( run_directory: Path, data_directory: Path, - data_field: str, + ifo: str, export_endpoint: str, - channels: ChannelList, + channels: Dict[str, str], architecture: Callable, train_duration: float, retrain_cadence: float, @@ -100,10 +100,10 @@ def main( # start collecting frames from the data stream # to aggregate into a dataset - channels = get_channels(channels) + channels = get_channels(channels[ifo]) frame_collector = DataCollector( data_directory, - data_field, + ifo, deployment.log_directory, channels, train_duration, diff --git a/projects/microservice/utils/microservice/deployment.py b/projects/microservice/utils/microservice/deployment.py index 9cbdc00..5fb80d9 100644 --- a/projects/microservice/utils/microservice/deployment.py +++ b/projects/microservice/utils/microservice/deployment.py @@ -51,15 +51,16 @@ def storage_directory(self): @dataclass(frozen=True) class DataStream: root: Path - field: str + ifo: str + strain_offset: float = 7 @property def detchar(self): - return self.root / "lldetchar" / self.field + return self.root / "lldetchar" / self.ifo @property def hoft(self): - return self.root / "kafka" / self.field.replace("replay", "") + return self.root / "kafka" / self.ifo def crawl(self, t0, timeout): crawler = FrameCrawler(self.detchar, t0, timeout, max_dropped_frames=5) @@ -67,12 +68,16 @@ def crawl(self, t0, timeout): strain_fname = fname.name.replace("lldetchar", "llhoft") strain_fname = self.hoft / strain_fname - start_time = time.time() - while not strain_fname.exists(): - if (time.time() - start_time) > 10: - raise FileNotFoundError( - f"Strain file {strain_fname} does not exist" - ) + tick = time.time() + timeout = 3 + self.strain_offset + while time.time() < tick + timeout: + if strain_fname.exists(): + break + else: + raise FileNotFoundError( + "Strain file {} still doesn't exist after " + "waiting for {} seconds".format(strain_fname, timeout) + ) yield fname, strain_fname diff --git a/projects/microservice/utils/microservice/frames.py b/projects/microservice/utils/microservice/frames.py index 0fd925c..8fab102 100644 --- a/projects/microservice/utils/microservice/frames.py +++ b/projects/microservice/utils/microservice/frames.py @@ -11,6 +11,7 @@ from scipy.signal.windows import hann from deepclean.logging import logger +from deepclean.utils.channels import get_channels as sort_channels PATH_LIKE = Union[str, Path] @@ -25,6 +26,11 @@ fname_re = re.compile(pattern) +def get_channels(channels: str): + channels = [i for i in channels.split(",") if i] + return sort_channels(channels) + + def parse_frame_name(fname: PATH_LIKE) -> Tuple[str, int, int]: """Use the name of a frame file to infer its initial timestamp and length From 8dc8e1156223bc283df5b8b2d88fa01fa9bdb94c Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 5 Jun 2023 17:45:24 -0500 Subject: [PATCH 26/31] adding container build workflow --- .github/workflows/ci-tests.yaml | 44 ++++++++++++++++++++++++++++++++- 1 file changed, 43 insertions(+), 1 deletion(-) diff --git a/.github/workflows/ci-tests.yaml b/.github/workflows/ci-tests.yaml index c966d89..14659f5 100644 --- a/.github/workflows/ci-tests.yaml +++ b/.github/workflows/ci-tests.yaml @@ -5,6 +5,11 @@ on: push: branches: - main + - dev + +env: + REGISTRY: ghcr.io + IMAGE_NAME: ${{ github.repository }} jobs: # tests the libraries that our projects depend on @@ -21,7 +26,7 @@ jobs: - ${{ github.workspace }}:/github/workspace strategy: matrix: - lib: [architectures, signal, export, gwftools, trainer, infer] + lib: [architectures, export, infer, trainer, utils] steps: - uses: actions/checkout@v2 with: @@ -58,3 +63,40 @@ jobs: env: test_dir: /github/workspace/projects/${{ matrix.project }} run: pinto -p $test_dir run pytest $test_dir/tests -x + + production-container-builds: + needs: project-ci-tests + if: | + ${{ github.event_name == 'push' }} && + ${{ github.repository_owner == 'ML4GW' }} && + ${{ github.ref == 'refs/heads/main' }} + runs-on: ubuntu-latest + strategy: + matrix: + project: [cleaner, exporter, trainer, monitor] + steps: + - + name: Check out the repo + uses: actions/checkout@v2 + - + name: Log in to registry + uses: docker/login-action@master + with: + registry: ${{ env.REGISTRY }} + username: ${{ github.actor }} + password: ${{ secrets.GITHUB_TOKEN }} + - + name: Extract metadata (tags, labels) for Docker + id: meta + uses: docker/metadata-action@master + with: + images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}/production/${{ matrix.project }} + tags: type=schedule,pattern={{date 'YY.MM'}} + - + name: Rebuild and push + uses: docker/build-push-action@v2 + with: + context: projects/microservice + push: true + tags: ${{ steps.meta.outputs.tags }} + labels: ${{ steps.meta.outputs.labels }} From 54b3c8c3f41c9659969a86cf6562fc7d04424b22 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 5 Jun 2023 21:13:03 -0500 Subject: [PATCH 27/31] getting pipeline running with new channel format and expanding README --- projects/microservice/README.md | 117 +++++++++++++++++- .../exporter/exporter/exporter.py | 9 +- projects/microservice/pyproject.toml | 2 + projects/microservice/run.sh | 3 + 4 files changed, 122 insertions(+), 9 deletions(-) diff --git a/projects/microservice/README.md b/projects/microservice/README.md index 6e0c887..842a361 100644 --- a/projects/microservice/README.md +++ b/projects/microservice/README.md @@ -8,28 +8,135 @@ The services contained here are: - `cleaner`: Responsible for sending inference requests to DeepClean to compute noise predictions and subtract them from low-latency strain. - `monitor`: Monitors the cleaned data produced by the latest version of DeepClean in order to validate it and assess whether it's ready to move into production. -Additionally, this project contains a `utils` library for defining utility functions shared by the various projects, and leverages a Triton inference server instance to handle serving DeepClean for inference and moving new models into production. +Additionally, this project contains a `utils` library for defining utility functions shared by the various projects, and leverages a [Triton inference server](https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/index.html) instance to handle serving DeepClean for inference and moving new models into production. Each project is discussed in more detail [below](#project-overviews). ## Build and run instructions +> Note: future deployment strategy will leverage Singularity containers pushed to GitHub registry automatically via CI, and made available on LDG via the OSG container repository. +This strategy will be implemented once the current PR is pushed to main and the container builds can be tested. + On a node with at least 2 enterprise-grade GPUs, begin by installing the `pinto` environment management utility by following the instructions [here](https://github.com/ML4GW/pinto). If you chose to go with the virtual environment installation of `pinto`, be sure to activate that environment (`conda activate pinto`) before proceeding with the instructions below. First, begin by using `pinto` to build all of the projects' corresponding environments -``` +```bash ./build.sh ``` -Then, launch a `tmux` session with panes executing each of the individual services by running +Next, define the required environment variables in a local `.env` file pointing to the relevant I/O directories. +The minimum required environment variables are +```bash +# Note that you shouldn't define the $PROJECT +# variable here, it will be defined when you +# launch the service and filled in here to +# given individual runs their own directories +PROJECT_DIR=${HOME}/deepclean/${PROJECT} # path to save logs, cleaned frames, versioned models, etc. +DATA_DIR=/dev/shm/ # path to input data +IFO=L1 # interferometer being run on ``` -./run.sh + +Then, launch a `tmux` session with panes executing each of the individual services by running + +```bash +./run.sh first-pipeline-run ``` +where `first-pipeline-run` is used to set `PROJECT` environment variable used to name individual pipeline runs. +Alternatively, you can run each one of the commands implemented in [`run.sh`](./run.sh) inside a pre-configured `tmux` session. + ## Configuration Each project reads its arguments from a shared configuration file [`pyproject.toml`](./pyproject.toml), which is parsed during command line execution by the locally-maintained `typeo` library. The configuration is broken up into different tables for each project, denoted `[tool.typeo.scripts.]`, as well as a `[tool.typeo.base]` table for defining shared parameters. -For explanations of each argument, consult the corresponding project's documentation. +For explanations of each argument, consult the corresponding project's documentation. Instructions for running each project individually can be found by inspecting `run.sh`(./run.sh). ## Project overviews +### `trainer` +Iteratively trains a new version of DeepClean on overlapping stretches of data. +For every training run after the first, the weights from the previously trained version of the model are used to initialize the weights for the next run, which are then optimized with a reduced learning rate. + +While the model trains, its next training set is collected from frames in real time. +As noted above, all training data must have the `DMT-ANALYSIS_READY` flag set. +If at any point during data collection, a frame has any samples for which this flag is not set, the frame and existing training data are dropped. +Data collection resumes again once this flag is set again for a sufficiently long segment of frames. + +If any dropped frames are encountered, the previous frame of data is tapered using a Hann window and a frame of all zeros is appended to the data stream. +Once a non-dropped frame re-enters the stream, its reentry is tapered using a Hann window. +If too many consecutive frames are dropped, the current training set is dropped and data collection resumes once frames become available again. + +For details on the training process, consult the [associated publication](https://dcc.ligo.org/LIGO-P2300153). The relevant arguments can be found by running (in the associated environment) + +```bash +train -h +``` + +The parameter values used in production are available in the [`pyproject.toml`](./pyproject.toml). + +### `exporter` +This service runs a Flask app that manages the repository of models used to executed DeepClean in production, including the multiple versions of the DeepClean model itself. +DeepClean versions are associated with the timestamps used to train them via metadata in the DeepClean model config. + +At any given moment, two versions of DeepClean are producing predictions. +The **production** model has had its predictions validated by the `monitor` service and has the quality of its ASD ratio actively monitored to ensure the final data product has no artifacts. +The **canary** model usually represents the most recently trained version of DeepClean which is waiting to have its performance validated by the `monitor` service. The `exporter` service +- creates the server-side pipeline to produce both sets of predictions in parallel +- exposes an endpoint for the `trainer` service to notify it about newly trained versions of DeepClean for export +- exposes an endpoint for the `monitor` service to notify that a recently trained "canary" version of DeepClean has passed validation and should be moved into production. + +This service can only be run via the `pyproject.toml` config, which should be consulted for information about the relevant arguments. + +### `cleaner` +Service responsible for producing a 300s long rotating carousel of frames containing cleaned strain data. +Reads strain and auxiliary channels from low-latency frames and sends them to a running Triton instance serving up both the production and canary versions of DeepClean and uses their predictions to produce cleaned frames. +Note that this service is completely agnostic to which _specific_ versions of the model are being served up by Triton. It just makes requests to a pipeline containing "production" and "canary" models, and its up to the `exporter` service which actual versions of the model weights these correspond to. + +#### Data Products +Each frame produced by DeepClean will have 3 channels associated with it, each sampled at **4096 Hz**. The channels are: +- `:GDSD-CALIB_STRAIN_DEEPCLEAN_PRODUCTION_OUT_DQ` - The official data product of DeepClean. The data contained in this channel will have been cleaned by a validated and stable version of DeepClean, and will have the content of its data monitored _prior_ to writing. +If the data is found to cause increases in the average ASD ratio over the relevant frequency band over an 8 second period including the current frame, the timeseries of noise predictions that is typically subtracted will be tapered out using a Hann window and only reintroduced once that ASD ratio falls under 1 again. +Until that point, this channel will just contain the raw strain data. +- `:GDS-CALIB_STRAIN_DEEPCLEAN_PRODUCTION` - Similar to the `OUT_DQ` channel, but with subtraction _always_ turned on. +Intended mostly for analysis purposes. +- `:GDS-CALIB_STRAIN_DEEPCLEAN_CANARY` - Frames cleaned using predictions from the canary model, used by the `monitor` service to validate the performance of the model. +Since the model used to clean the data in this channel will have been trained on more recent data, it may have superior performance to the data in the `PRODUCTION` channel, but we make no guarantees about the quality of the data in this channel. + +#### Missing data +Unlike the `trainer`, this will produce predictions _regardless_ of the current data quality status, leaving that to downstream pipelines to decide what data to use. +However, it handles dropped frames the same way the `trainer` project does: tapering out the timeseries before the dropped frame using a Hann window, appending a frame of all 0s to each channel, then tapering back in the next non-dropped frame. + +#### Sources of latency +Signal processing techniques and data safety measures necessitate some procedures which prevent DeepClean from being able to produce predictions on the most recent low-latency frame. +Moreover, because frames are only made available in discreet 1 second increments, each piece of new data required means waiting a full second for the entire next frame to become available. +This introduces some latency into the DeepClean pipeline that has nothing to do with its compute requirements, which are comparably light. +Those latency sources are: +- Preprocessing + - Data resampling: downsampling the data from 16384 to 4096 Hz introduces artifacts at the edges of the downsampled timeseries. + To combat this, we maintain a 3-second buffer used to downsample, and take the middle second as the next frame to send through DeepClean. + This introduces 1 second of latency waiting for future data to fill out the buffer. + - Tapering dropped frames: We need to check if the next frame has been dropped before appending to our buffer, because if it has then we need to taper the end of our current timeseries to avoid introducing noise artifacts when we append all 0s. + This introduces another second of latency waiting for the next frame to become available. +- Postprocessing + - Averaging predictions: DeepClean produces 512 1-second long noise prediction timeseries each second. That means that each of these predictions carries a lot of overlap, and predictions made farther from the edge of the 1-second long window tend to be better as a consequence of the way DeepClean is trained. + This means we can improve our performance by averaging over the overlapping portions of these predictions. + This means predictions at the end of a frame can't be made until they've been averaged with predictions on that same time segment from a timeseries which contains data _after_ the frame as well. + This introduces another 1 second of latency waiting for the next frame to be preprocessed and sent through DeepClean. + - Filter padding: Once a frame of (averaged) noise predictions has been produced by DeepClean, we need to bandpass filter it to the target frequency range before subtracting it from the strain channel. + Like the downsampling, this introduces artifacts at the edges of the timeseries being filtered. + So we wait for another 0.5s worth of averaged predictions to become available to pad our timeseries and ensure that we have well behaved data. + Because the averaging mentioned above only takes place over 0.5s, this actually ends up not introducing any additional latency, but is worth mentioning since it would under different circumstances (e.g. if low-latency frames were written at a faster cadence). +- Data safety + - To ensure that DeepClean doesn't introduce additional noise artifacts into the data, we maintain an 8s buffer of cleaned data, of which the frame to be written is the second-to-last. + Before writing the `OUT_DQ` channel, we measure the ASD ratio over this buffer on the target frequency range. + If the average of that ratio is greater than 1, we elect not to write our noise-subtracted data and instead taper the noise prediction from the second-to-last frame. + Until the ASD ratio returns below 1, we continue writing raw strain data to the `OUT_DQ` channel (the regular `PRODUCTION` channel, on the other hand, will always contain the noise-subtracted data used to compute this ASD ratio). + This introduces another second of latency waiting for the next frame to be cleaned. + +In total, our data processing strategy introduces 4s of latency, while our compute itself takes on the order of 200ms. +This is obviously less than ideal, but worthwhile to ensure that the data produced by DeepClean does not degrade the quality data used by downstream applications. + +### `monitor` +The monitoring service serves two primary functions +- Monitors the quality of data produced by the canary model to validate whether it was trained sufficiently well to be put in production. +If the ASD ratio of the canary cleaned data with the raw strain data is less than 1 over a 100 second period, it is deemed ready to go into production and a request is made to the `exporter` service to increment the DeepClean version corresponding to the production model. +- Ensures that the carousel of frames produced by the `cleaner` service never exceeds 300, and periodically saves larger stretches (O(1000s)) of cleaned and raw data to cold storage for offline analysis and testing. diff --git a/projects/microservice/exporter/exporter/exporter.py b/projects/microservice/exporter/exporter/exporter.py index d226822..c96fc91 100644 --- a/projects/microservice/exporter/exporter/exporter.py +++ b/projects/microservice/exporter/exporter/exporter.py @@ -2,19 +2,19 @@ import os from dataclasses import dataclass from pathlib import Path -from typing import Optional +from typing import Dict, Optional import torch import tritonclient.grpc.model_config_pb2 as model_config from dotenv import load_dotenv from microservice.deployment import Deployment +from microservice.frames import get_channels import hermes.quiver as qv from deepclean.architectures import DeepCleanAE as architecture from deepclean.export import repo as repo_utils from deepclean.export.model import DeepClean from deepclean.logging import logger -from deepclean.utils.channels import ChannelList, get_channels from ml4gw.transforms import ChannelWiseScaler from typeo.actions import TypeoTomlAction from typeo.parser import make_parser @@ -23,7 +23,8 @@ @dataclass class Exporter: run_directory: Path - channels: ChannelList + channels: Dict[str, str] + ifo: str kernel_length: float sample_rate: float inference_sampling_rate: float @@ -76,7 +77,7 @@ def __post_init__(self): self.deployment.log_directory / "exporter.log", verbose=self.verbose, ) - self.channels = get_channels(self.channels) + self.channels = get_channels(self.channels[self.ifo]) self.num_witnesses = len(self.channels) - 1 # start by initializing a full model instance diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 647eee8..1e7fc9b 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -114,6 +114,7 @@ architecture.autoencoder = {} run_directory = "${base.run_directory}" # data args +ifo = "${base.ifo}" channels = "${base.channels}" kernel_length = 1 sample_rate = "${base.sample_rate}" @@ -165,6 +166,7 @@ start_first = false timeout = 2 verbose = true # "${base.verbose}" + [tool.typeo.scripts.monitor] # paths run_directory = "${base.run_directory}" diff --git a/projects/microservice/run.sh b/projects/microservice/run.sh index 006158b..855de30 100755 --- a/projects/microservice/run.sh +++ b/projects/microservice/run.sh @@ -7,6 +7,9 @@ prun() { } tmux new-session -s deepclean-production -d "CUDA_VISIBLE_DEVICES=0 $(prun exporter) flask --app=exporter run" +# all the other projects require communicating with the +# export service, so wait until it's online before +# launching the remainder of the services while [[ -z $(curl -s localhost:5000/alive) ]]; do echo "Waiting for export service to come online" sleep 2 From 8ea0a868f25d6b8494c84e303e0bfef9d21fab2f Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 5 Jun 2023 23:06:49 -0500 Subject: [PATCH 28/31] debugging production clean pipeline --- .../cleaner/cleaner/dataloader.py | 6 ++++++ projects/microservice/cleaner/cleaner/main.py | 9 +++++++-- .../microservice/cleaner/cleaner/writer.py | 14 ++++++++++---- projects/microservice/monitor/monitor/main.py | 19 ++++++++++++------- projects/microservice/pyproject.toml | 2 +- 5 files changed, 36 insertions(+), 14 deletions(-) diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index e93a6ea..69965b3 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -4,6 +4,7 @@ from typing import Iterable, List, Tuple import numpy as np +from gwpy.timeseries import TimeSeries from microservice.deployment import DataStream from microservice.frames import frame_it @@ -68,6 +69,7 @@ def batch_iter( it = frame_it(crawler, channels, sample_rate) strain_fname, strain, witnesses = next(it) + strain = TimeSeries(strain, channel=channels[0], sample_rate=sample_rate) q.put((strain, strain_fname)) idx = 0 @@ -91,6 +93,9 @@ def batch_iter( # put the strain data and its associated # filename in the queue that can be passed # to our callback thread + strain = TimeSeries( + strain, channel=channels[0], sample_rate=sample_rate + ) q.put((strain, strain_fname)) # slough off any old witness data that @@ -102,6 +107,7 @@ def batch_iter( # new witness data array idx, start, stop = 0, 0, stride yield witnesses[:, start:stop], False + idx += 1 def get_data_generators( diff --git a/projects/microservice/cleaner/cleaner/main.py b/projects/microservice/cleaner/cleaner/main.py index e17d7cb..5f2936f 100644 --- a/projects/microservice/cleaner/cleaner/main.py +++ b/projects/microservice/cleaner/cleaner/main.py @@ -182,7 +182,12 @@ def main( monitor = ASDRMonitor( buffer_length=8, freq_low=freq_low, freq_high=freq_high, fftlength=2 ) - writer = Writer(write_dir, strain_it, monitor, export_endpoint) + writer = Writer( + write_dir, + strain_generator=strain_it, + monitor=monitor, + export_endpoint=export_endpoint, + ) callback = Callback(writer, **states) # finally create an inference client which will stream @@ -217,7 +222,7 @@ def main( response = client.get() if response is not None: fname, latency = response - logger.info(f"Wrote file {fname} with latency {latency}s") + logger.info(f"Wrote file {fname} with latency {latency:0.2f}s") if __name__ == "__main__": diff --git a/projects/microservice/cleaner/cleaner/writer.py b/projects/microservice/cleaner/cleaner/writer.py index 8773c69..80c0379 100644 --- a/projects/microservice/cleaner/cleaner/writer.py +++ b/projects/microservice/cleaner/cleaner/writer.py @@ -70,7 +70,12 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: # if we don't have enough data to measure the ASDR # yet, then just return the buffered raw data - if self.raw.duration.value < self.buffer_length: + duration = self.raw.duration.value + if duration < self.buffer_length: + self.logger.info( + "Buffer only {:0.0f}s long, not enough to " + "measure ASDR so only producing raw data".format(duration) + ) return raw, raw # check the average ASDR over the relevant @@ -80,7 +85,7 @@ def __call__(self, raw: TimeSeries, noise: TimeSeries) -> TimeSeries: clean_asd = self.get_asd(self.clean) asdr = (clean_asd / raw_asd).value.mean() - self.logger.debug( + self.logger.info( "Mean ASDR value over GPS times {}-{} " "and frequency range {}-{}Hz is currently {}".format( t0, tf, self.freq_low, self.freq_high, asdr @@ -164,7 +169,7 @@ def __call__(self, **predictions): # TODO: incorporate this as metadata somehow version = self.export_client.get_production_version() self.logger.info( - "Cleaning strain from file {} using predictions" + "Cleaning strain from file {} using predictions " "made by DeepClean version {}".format(fname, version) ) @@ -259,12 +264,13 @@ def __call__(self, **predictions): # record the timestamp of this first file # to compute our write latency later self.timestamp_buffer = file_timestamp + self.logger.info("Skipping clean of first file") return None write_name = f"{ifo}-{field}-{t0}-{dur}.gwf" write_path = self.write_dir / write_name tsd.write(write_path) - self.logger.debug(f"Finished cleaning of file {write_path}") + self.logger.info(f"Finished cleaning of file {write_path}") write_time = time.time() latency = write_time - self.timestamp_buffer diff --git a/projects/microservice/monitor/monitor/main.py b/projects/microservice/monitor/monitor/main.py index 2487d84..08f114f 100644 --- a/projects/microservice/monitor/monitor/main.py +++ b/projects/microservice/monitor/monitor/main.py @@ -1,12 +1,17 @@ import time from pathlib import Path +from typing import Dict from gwpy.timeseries import TimeSeriesDict from microservice.deployment import DataStream, Deployment, ExportClient -from microservice.frames import DataProducts, FrameCrawler, read_channel +from microservice.frames import ( + DataProducts, + FrameCrawler, + get_channels, + read_channel, +) from deepclean.logging import logger -from deepclean.utils.channels import ChannelList, get_channels from deepclean.utils.filtering import Frequency, normalize_frequencies from typeo import scriptify @@ -19,7 +24,7 @@ def main( ifo: str, export_endpoint: str, # data/analysis args - channels: ChannelList, + channels: Dict[str, str], sample_rate: float, fftlength: float, asd_segment_length: float, @@ -37,7 +42,7 @@ def main( logger.set_logger("DeepClean infer", log_file, verbose) export_client = ExportClient(export_endpoint) - channels = get_channels(channels) + channels = get_channels(channels[ifo]) strain_channel = channels[0] data_products = DataProducts(strain_channel) @@ -82,8 +87,8 @@ def main( strain_fname = stream.hoft / fname.name frame = TimeSeriesDict() for channel in data_products.channels: - frame[channel] = read_channel(fname, channel, sample_rate) - strain = read_channel(strain_fname, strain_channel, sample_rate) + frame[channel] = read_channel(fname, channel) + strain = read_channel(strain_fname, strain_channel) frame[strain_channel] = strain buffer.append(frame) @@ -169,7 +174,7 @@ def main( else: logger.debug( "Canary DeepClean version {} has fallen " - "out of spec after {}s with asdr {}, " + "out of spec after {}s with asdr {:0.4f}, " "resetting validation counter".format( latest_version, in_spec_for, mean_asdr ) diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 1e7fc9b..769e968 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -164,7 +164,7 @@ sequence_id = 1001 # misc args start_first = false timeout = 2 -verbose = true # "${base.verbose}" +verbose = "${base.verbose}" [tool.typeo.scripts.monitor] From 36811ce298142dcc4bc76e899282a94c1cd3165d Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Fri, 9 Jun 2023 13:47:30 -0500 Subject: [PATCH 29/31] adding callback tests --- libs/infer/deepclean/infer/callback.py | 29 ++++++- libs/infer/poetry.lock | 103 +++++++++++------------- libs/infer/pyproject.toml | 4 +- libs/infer/tests/test_callback.py | 105 +++++++++++++++++++++++++ 4 files changed, 177 insertions(+), 64 deletions(-) create mode 100644 libs/infer/tests/test_callback.py diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index 53a9430..76fa201 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -150,20 +150,41 @@ def update(self, x, request_id): # number of steps in each frame to see if the current # cleanable index is past the end of the next frame div, rem = divmod(step_idx, self.steps_per_frame) - if ( - div == (self._frame_idx + 1) and rem >= self.steps_ahead - ) or div > self._frame_idx + 1: + if div <= self._frame_idx: + # haven't gotten past the current frame, + # so definitely won't be ready + return None + elif div > (self._frame_idx + 1) or rem >= self.steps_ahead: + # either we're far enough into the next frame to + # clean the current frame, or we're already into + # the frame after that, so either way we're ready + # to clean + + # start by figuring out how far into + # our current memory the frame to be + # cleaned ought to start frame_idx = self._frame_idx * self.frame_size idx = min(self.memory, frame_idx) + + # then include the current frame and whatever + # future data we'll need to clean it idx += self.frame_size + self.samples_ahead noise = self._state[:idx] + # if we have our full memory's worth of + # data store in our internal state *excluding* + # the current frame then slough off one frame's + # worth of data extra = len(noise) - self.samples_ahead - self.memory if extra > 0: self._state = self._state[extra:] + + # increment our frame and return our postprocessed + # noise estimate self._frame_idx += 1 return self.cleaner(noise) - return None + else: + return None class Callback: diff --git a/libs/infer/poetry.lock b/libs/infer/poetry.lock index 85e580b..1d4f7a7 100644 --- a/libs/infer/poetry.lock +++ b/libs/infer/poetry.lock @@ -1,26 +1,3 @@ -[[package]] -name = "atomicwrites" -version = "1.4.1" -description = "Atomic file writes." -category = "dev" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" - -[[package]] -name = "attrs" -version = "22.2.0" -description = "Classes Without Boilerplate" -category = "dev" -optional = false -python-versions = ">=3.6" - -[package.extras] -cov = ["attrs[tests]", "coverage-enable-subprocess", "coverage[toml] (>=5.3)"] -dev = ["attrs[docs,tests]"] -docs = ["furo", "myst-parser", "sphinx", "sphinx-notfound-page", "sphinxcontrib-towncrier", "towncrier", "zope.interface"] -tests = ["attrs[tests-no-zope]", "zope.interface"] -tests-no-zope = ["cloudpickle", "cloudpickle", "hypothesis", "hypothesis", "mypy (>=0.971,<0.990)", "mypy (>=0.971,<0.990)", "pympler", "pympler", "pytest (>=4.3.0)", "pytest (>=4.3.0)", "pytest-mypy-plugins", "pytest-mypy-plugins", "pytest-xdist[psutil]", "pytest-xdist[psutil]"] - [[package]] name = "colorama" version = "0.4.6" @@ -29,6 +6,19 @@ category = "dev" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" +[[package]] +name = "deepclean-logging" +version = "0.0.1" +description = "Utilities for standardizing logging practices" +category = "main" +optional = false +python-versions = "^3.8,<3.11" +develop = true + +[package.source] +type = "directory" +url = "../logging" + [[package]] name = "deepclean-utils" version = "0.0.1" @@ -45,6 +35,17 @@ scipy = "^1.7" type = "directory" url = "../utils" +[[package]] +name = "exceptiongroup" +version = "1.1.1" +description = "Backport of PEP 654 (exception groups)" +category = "dev" +optional = false +python-versions = ">=3.7" + +[package.extras] +test = ["pytest (>=6)"] + [[package]] name = "iniconfig" version = "2.0.0" @@ -81,34 +82,24 @@ python-versions = ">=3.6" dev = ["pre-commit", "tox"] testing = ["pytest", "pytest-benchmark"] -[[package]] -name = "py" -version = "1.11.0" -description = "library with cross-python path, ini-parsing, io, code, log facilities" -category = "dev" -optional = false -python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" - [[package]] name = "pytest" -version = "6.2.5" +version = "7.3.1" description = "pytest: simple powerful testing with Python" category = "dev" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" [package.dependencies] -atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""} -attrs = ">=19.2.0" colorama = {version = "*", markers = "sys_platform == \"win32\""} +exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""} iniconfig = "*" packaging = "*" pluggy = ">=0.12,<2.0" -py = ">=1.8.2" -toml = "*" +tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""} [package.extras] -testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"] +testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"] [[package]] name = "scipy" @@ -127,31 +118,29 @@ doc = ["matplotlib (>2)", "numpydoc", "pydata-sphinx-theme (==0.9.0)", "sphinx ( test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] -name = "toml" -version = "0.10.2" -description = "Python Library for Tom's Obvious, Minimal Language" +name = "tomli" +version = "2.0.1" +description = "A lil' TOML parser" category = "dev" optional = false -python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" +python-versions = ">=3.7" [metadata] lock-version = "1.1" python-versions = "^3.8,<3.11" -content-hash = "cae8f5f2430a42503e5873087396fe5afb45369c670a4bcbf1a0f539c88ae58c" +content-hash = "0f1953f82ea60b6c12166f1ec028513c5d5466fe99282f229301431f68fd0bbe" [metadata.files] -atomicwrites = [ - {file = "atomicwrites-1.4.1.tar.gz", hash = "sha256:81b2c9071a49367a7f770170e5eec8cb66567cfbbc8c73d20ce5ca4a8d71cf11"}, -] -attrs = [ - {file = "attrs-22.2.0-py3-none-any.whl", hash = "sha256:29e95c7f6778868dbd49170f98f8818f78f3dc5e0e37c0b1f474e3561b240836"}, - {file = "attrs-22.2.0.tar.gz", hash = "sha256:c9227bfc2f01993c03f68db37d1d15c9690188323c067c641f1a35ca58185f99"}, -] colorama = [ {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"}, {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"}, ] +deepclean-logging = [] deepclean-utils = [] +exceptiongroup = [ + {file = "exceptiongroup-1.1.1-py3-none-any.whl", hash = "sha256:232c37c63e4f682982c8b6459f33a8981039e5fb8756b2074364e5055c498c9e"}, + {file = "exceptiongroup-1.1.1.tar.gz", hash = "sha256:d484c3090ba2889ae2928419117447a14daf3c1231d5e30d0aae34f354f01785"}, +] iniconfig = [ {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, @@ -194,13 +183,9 @@ pluggy = [ {file = "pluggy-1.0.0-py2.py3-none-any.whl", hash = "sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3"}, {file = "pluggy-1.0.0.tar.gz", hash = "sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159"}, ] -py = [ - {file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"}, - {file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"}, -] pytest = [ - {file = "pytest-6.2.5-py3-none-any.whl", hash = "sha256:7310f8d27bc79ced999e760ca304d69f6ba6c6649c0b60fb0e04a4a77cacc134"}, - {file = "pytest-6.2.5.tar.gz", hash = "sha256:131b36680866a76e6781d13f101efb86cf674ebb9762eb70d3082b6f29889e89"}, + {file = "pytest-7.3.1-py3-none-any.whl", hash = "sha256:3799fa815351fea3a5e96ac7e503a96fa51cc9942c3753cda7651b93c1cfa362"}, + {file = "pytest-7.3.1.tar.gz", hash = "sha256:434afafd78b1d78ed0addf160ad2b77a30d35d4bdf8af234fe621919d9ed15e3"}, ] scipy = [ {file = "scipy-1.10.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e7354fd7527a4b0377ce55f286805b34e8c54b91be865bac273f527e1b839019"}, @@ -225,7 +210,7 @@ scipy = [ {file = "scipy-1.10.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ff7f37b1bf4417baca958d254e8e2875d0cc23aaadbe65b3d5b3077b0eb23ea"}, {file = "scipy-1.10.1.tar.gz", hash = "sha256:2cf9dfb80a7b4589ba4c40ce7588986d6d5cebc5457cad2c2880f6bc2d42f3a5"}, ] -toml = [ - {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"}, - {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"}, +tomli = [ + {file = "tomli-2.0.1-py3-none-any.whl", hash = "sha256:939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc"}, + {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"}, ] diff --git a/libs/infer/pyproject.toml b/libs/infer/pyproject.toml index 1101010..1680d46 100644 --- a/libs/infer/pyproject.toml +++ b/libs/infer/pyproject.toml @@ -11,10 +11,12 @@ packages = [ [tool.poetry.dependencies] python = "^3.8,<3.11" scipy = "^1.7" + "deepclean.utils" = {path = "../utils", develop = true} +"deepclean.logging" = {path = "../logging", develop = true} [tool.poetry.dev-dependencies] -pytest = "^6.2" +pytest = "^7.0" [build-system] requires = ["poetry-core"] diff --git a/libs/infer/tests/test_callback.py b/libs/infer/tests/test_callback.py new file mode 100644 index 0000000..2b93d8b --- /dev/null +++ b/libs/infer/tests/test_callback.py @@ -0,0 +1,105 @@ +from unittest.mock import patch + +import numpy as np +import pytest + +from deepclean.infer import callback +from deepclean.logging import logger + + +def add_one(_, x): + return x + 1 + + +@patch("deepclean.infer.callback.BandpassFilter.__call__", new=add_one) +class TestCleaner: + def test_call(self): + x = np.arange(128 * 8).astype("float32") + cleaner = callback.Cleaner( + kernel_length=1, + sample_rate=128, + filter_pad=0.5, + freq_low=10, + freq_high=50, + ) + y = cleaner(x) + assert y.shape == (128,) + + expected = np.arange(int(128 * 6.5), int(128 * 7.5)) + 1 + assert (y == expected).all() + + +@patch("deepclean.infer.callback.BandpassFilter.__call__", new=add_one) +class TestState: + @pytest.fixture + def state(self): + logger.set_logger("test") + return callback.State( + name="test", + frame_length=1, + memory=8, + filter_pad=0.5, + sample_rate=128, + inference_sampling_rate=16, + batch_size=8, + aggregation_steps=4, + freq_low=10, + freq_high=20, + ) + + def test_init(self, state): + assert state.frame_size == 128 + assert state.stride == 8 + assert state.steps_per_frame == 16 + assert state.memory == 1024 + assert state.samples_ahead == 64 + assert state.steps_ahead == 8 + assert state.agg_batches == 0 + assert state.agg_leftover == 4 + + def test_validate(self, state): + x = np.arange(64).astype("float32") + with pytest.raises(ValueError) as exc: + state.validate(x[:-1], 0) + assert str(exc.value).startswith("Noise prediction is of wrong length") + + response = state.validate(x, 0) + assert response is not None + assert response.shape == (32,) + assert state._latest_seen == 0 + + expected = np.arange(32, 64) + assert (response == expected).all() + + response = state.validate(x, 1) + assert response.shape == (64,) + assert state._latest_seen == 1 + assert (response == x).all() + + def test_update(self, state): + x = np.arange(64).astype("float32") + response = state.update(x, 0) + assert response is None + assert state._state.shape == (128,) + assert (state._state[:32] == x[-32:]).all() + assert (state._state[32:] == 0).all() + + response = state.update(x + 64, 1) + assert response is None + assert state._state.shape == (128,) + expected = np.arange(32, 128) + assert (state._state[: 32 + 64] == expected).all() + assert (state._state[32 + 64 :] == 0).all() + + response = state.update(x + 128, 2) + assert response is None + assert state._state.shape == (256,) + expected = np.arange(32, 64 * 3) + assert (state._state[: 32 + 64 * 2] == expected).all() + assert (state._state[32 + 64 * 2 :] == 0).all() + + response = state.update(x + 64 * 3, 3) + assert response is not None + assert response.shape == (128,) + assert (response[64:] == np.arange(64) + 32 + 64 + 1).all() + assert state._frame_idx == 1 From f85fcbe30ce797ac657b4cd0ffecc8ba60e867e5 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Mon, 12 Jun 2023 11:48:50 -0500 Subject: [PATCH 30/31] getting online cleaning working --- libs/infer/deepclean/infer/callback.py | 32 ++++++++++++++------------ 1 file changed, 17 insertions(+), 15 deletions(-) diff --git a/libs/infer/deepclean/infer/callback.py b/libs/infer/deepclean/infer/callback.py index 76fa201..b48c7a1 100644 --- a/libs/infer/deepclean/infer/callback.py +++ b/libs/infer/deepclean/infer/callback.py @@ -124,24 +124,26 @@ def update(self, x, request_id): # use the package request id to figure out where # in the blank noise array we need to insert # this prediction. Subtract the steps that we threw away + request_id = request_id - self.agg_batches total_steps = request_id * self.batch_size - step_idx = total_steps - self.aggregation_steps - start_idx = 0 if step_idx < 0 else int(step_idx * self.stride) + step_idx = total_steps - self.agg_leftover + # start_idx = 0 if step_idx < 0 else int(step_idx * self.stride) - # If we've sloughed off any past data so far, - # make sure to account for that - frame_idx = self._frame_idx * self.frame_size - start_idx -= max(frame_idx - self.memory, 0) + # # If we've sloughed off any past data so far, + # # make sure to account for that + # frame_idx = self._frame_idx * self.frame_size + # start_idx -= max(frame_idx - self.memory, 0) - # now make sure that we have data to fill out, - # otherwise extend the array - if (start_idx + len(x)) > len(self._state): - self._state = np.append(self._state, self._zeros) + # # now make sure that we have data to fill out, + # # otherwise extend the array + # if (start_idx + len(x)) > len(self._state): + # self._state = np.append(self._state, self._zeros) - # now insert the response into the existing array - # TODO: should we check that this is all 0s to - # double check ourselves here? - self._state[start_idx : start_idx + len(x)] = x + # # now insert the response into the existing array + # # TODO: should we check that this is all 0s to + # # double check ourselves here? + # self._state[start_idx : start_idx + len(x)] = x + self._state = np.append(self._state, x) # return the number of steps that have been completed step_idx = step_idx + self.batch_size @@ -182,7 +184,7 @@ def update(self, x, request_id): # increment our frame and return our postprocessed # noise estimate self._frame_idx += 1 - return self.cleaner(noise) + return self.cleaner(noise.copy()) else: return None From 180b979a1d8505d69bab9f254da01c81bc743777 Mon Sep 17 00:00:00 2001 From: Alec Gunny Date: Fri, 16 Jun 2023 15:18:39 -0500 Subject: [PATCH 31/31] trying to get production cleaning up to scratch by debugging with notebook --- .../cleaner/cleaner/dataloader.py | 6 +- projects/microservice/cleaner/cleaner/main.py | 4 +- projects/microservice/pyproject.toml | 2 +- .../microservice/sandbox/environment.yaml | 9 + projects/microservice/sandbox/poetry.lock | 4591 +++++++++++++++++ projects/microservice/sandbox/poetry.toml | 2 + projects/microservice/sandbox/pyproject.toml | 16 + projects/microservice/sandbox/sandbox.ipynb | 945 ++++ projects/microservice/sandbox/sandbox.py | 0 9 files changed, 5569 insertions(+), 6 deletions(-) create mode 100644 projects/microservice/sandbox/environment.yaml create mode 100644 projects/microservice/sandbox/poetry.lock create mode 100644 projects/microservice/sandbox/poetry.toml create mode 100644 projects/microservice/sandbox/pyproject.toml create mode 100644 projects/microservice/sandbox/sandbox.ipynb create mode 100644 projects/microservice/sandbox/sandbox.py diff --git a/projects/microservice/cleaner/cleaner/dataloader.py b/projects/microservice/cleaner/cleaner/dataloader.py index 69965b3..fc03834 100644 --- a/projects/microservice/cleaner/cleaner/dataloader.py +++ b/projects/microservice/cleaner/cleaner/dataloader.py @@ -1,7 +1,7 @@ import time from pathlib import Path from queue import Empty, Queue -from typing import Iterable, List, Tuple +from typing import Iterable, List, Optional, Tuple import numpy as np from gwpy.timeseries import TimeSeries @@ -117,10 +117,10 @@ def get_data_generators( sample_rate: float, inference_sampling_rate: float, batch_size: int, - start_first: bool = True, + t0: Optional[float] = None, timeout: float = 10, ): - t0 = 0 if start_first else -1 + # t0 = 0 if start_first else -1 stream = DataStream(data_dir, ifo) crawler = stream.crawl(t0, timeout) strain_q = Queue() diff --git a/projects/microservice/cleaner/cleaner/main.py b/projects/microservice/cleaner/cleaner/main.py index 5f2936f..8a8d10d 100644 --- a/projects/microservice/cleaner/cleaner/main.py +++ b/projects/microservice/cleaner/cleaner/main.py @@ -145,7 +145,7 @@ def main( sample_rate=sample_rate, inference_sampling_rate=inference_sampling_rate, batch_size=batch_size, - start_first=start_first, + t0=0 if start_first else -1, timeout=timeout, ) @@ -158,7 +158,7 @@ def main( if max_latency is None: aggregation_steps = 0 else: - aggregation_steps = int(max_latency * inference_sampling_rate) + aggregation_steps = int(max_latency * inference_sampling_rate) - 1 states = {} for model in ["production", "canary"]: diff --git a/projects/microservice/pyproject.toml b/projects/microservice/pyproject.toml index 769e968..0a87f52 100644 --- a/projects/microservice/pyproject.toml +++ b/projects/microservice/pyproject.toml @@ -148,7 +148,7 @@ freq_low = "${base.freq_low}" freq_high = "${base.freq_high}" frame_length = 1 memory = 10 -look_ahead = 0.5 +look_ahead = 1 # inference args inference_sampling_rate = "${base.inference_sampling_rate}" diff --git a/projects/microservice/sandbox/environment.yaml b/projects/microservice/sandbox/environment.yaml new file mode 100644 index 0000000..9f45b41 --- /dev/null +++ b/projects/microservice/sandbox/environment.yaml @@ -0,0 +1,9 @@ +name: deepclean-production-sandbox +channels: + - conda-forge +dependencies: + - python=3.10 + - gwpy<3.0 + - astropy + - python-ldas-tools-framecpp=2.6.14 + - python-nds2-client diff --git a/projects/microservice/sandbox/poetry.lock b/projects/microservice/sandbox/poetry.lock new file mode 100644 index 0000000..a3aad1b --- /dev/null +++ b/projects/microservice/sandbox/poetry.lock @@ -0,0 +1,4591 @@ +[[package]] +name = "aiohttp" +version = "3.8.4" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<4.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["Brotli", "aiodns", "cchardet"] + +[[package]] +name = "aiosignal" +version = "1.3.1" +description = "aiosignal: a list of registered asynchronous callbacks" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +frozenlist = ">=1.1.0" + +[[package]] +name = "anyio" +version = "3.7.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +category = "main" +optional = false +python-versions = ">=3.7" + +[package.dependencies] +exceptiongroup = {version = "*", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" + +[package.extras] +doc = ["Sphinx (>=6.1.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme", "sphinxcontrib-jquery"] +test = ["anyio[trio]", "coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "mock (>=4)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"] +trio = ["trio (<0.22)"] + +[[package]] +name = "appnope" +version = "0.1.3" +description = "Disable App Nap on macOS >= 10.9" +category = "main" +optional = false +python-versions = "*" + +[[package]] +name = "argon2-cffi" +version = "21.3.0" +description = "The secure Argon2 password hashing algorithm." +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +argon2-cffi-bindings = "*" + +[package.extras] +dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"] +docs = ["furo", "sphinx", "sphinx-notfound-page"] +tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] + +[[package]] +name = "argon2-cffi-bindings" +version = "21.2.0" +description = "Low-level CFFI bindings for Argon2" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +cffi = ">=1.0.1" + +[package.extras] +dev = ["cogapp", "pre-commit", "pytest", "wheel"] +tests = 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a/projects/microservice/sandbox/poetry.toml b/projects/microservice/sandbox/poetry.toml new file mode 100644 index 0000000..084377a --- /dev/null +++ b/projects/microservice/sandbox/poetry.toml @@ -0,0 +1,2 @@ +[virtualenvs] +create = false diff --git a/projects/microservice/sandbox/pyproject.toml b/projects/microservice/sandbox/pyproject.toml new file mode 100644 index 0000000..2afce87 --- /dev/null +++ b/projects/microservice/sandbox/pyproject.toml @@ -0,0 +1,16 @@ +[tool.poetry] +name = "sandbox" +version = "0.0.1" +description = "Production DeepClean cleaning service" +authors = ["Alec Gunny "] + +[tool.poetry.dependencies] +python = ">3.8,<3.11" +jupyter = "^1.0" +trainer = {path = "../trainer", develop = true} +cleaner = {path = "../cleaner", develop = true} +monitor = {path = "../monitor", develop = true} + +[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" diff --git a/projects/microservice/sandbox/sandbox.ipynb b/projects/microservice/sandbox/sandbox.ipynb new file mode 100644 index 0000000..f9191ec --- /dev/null +++ b/projects/microservice/sandbox/sandbox.ipynb @@ -0,0 +1,945 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "7c0300e1", + "metadata": {}, + "outputs": [], + "source": [ + "import toml\n", + "from importlib import reload\n", + "from pathlib import Path\n", + "\n", + "import h5py\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "from gwpy.timeseries import TimeSeries, TimeSeriesDict\n", + "from hermes.quiver.streaming.streaming_input import Snapshotter\n", + "from hermes.quiver.streaming.streaming_output import OnlineAverager\n", + "\n", + "from deepclean.architectures import DeepCleanAE\n", + "from deepclean.export.model import DeepClean\n", + "from deepclean.infer import callback\n", + "from deepclean.logging import logger\n", + "\n", + "from microservice.deployment import Deployment\n", + "from microservice.frames import get_channels\n", + "from ml4gw.transforms import ChannelWiseScaler\n", + "from trainer.main import train_on_segment\n", + "\n", + "\n", + "home = Path.home()\n", + "run_dir = home / \"deepclean\" / \"results\" / \"microservice\" / \"sandbox-live\"\n", + "ifo = \"L1\"\n", + "device = \"cuda:1\"\n", + "\n", + "deployment = Deployment(run_dir)\n", + "log_file = deployment.log_directory / \"run.log\"\n", + "logger = logger.set_logger(\"Deepclean sandbox\", log_file)\n", + "\n", + "ifo = \"L1\"\n", + "with open(\"../pyproject.toml\", \"r\") as f:\n", + " config = toml.load(f)[\"tool\"][\"typeo\"]\n", + "\n", + "channels = config[\"base\"][\"channels\"][ifo]\n", + "channels = get_channels(channels)\n", + "sample_rate = config[\"base\"][\"sample_rate\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3954ff16", + "metadata": {}, + "outputs": [], + "source": [ + "def download_data(start, duration, channels, cache_file):\n", + " train_data = TimeSeriesDict.fetch(\n", + " start=start,\n", + " end=start + duration,\n", + " channels=channels\n", + " )\n", + " train_data = train_data.resample(sample_rate)\n", + " X = np.stack([train_data.pop(i).value for i in channels[1:]])\n", + " y = train_data.pop(channels[0]).value\n", + "\n", + " with h5py.File(cache_file, \"a\") as f:\n", + " group = f.create_group(ifo)\n", + " group[\"X\"] = X\n", + " group[\"y\"] = y\n", + " return X, y\n", + "\n", + "\n", + "def get_data(cache_file, start, duration, channels):\n", + " if cache_file.exists():\n", + " with h5py.File(cache_file, \"r\") as f:\n", + " if ifo in f:\n", + " group = f[ifo]\n", + " X = group[\"X\"][:]\n", + " y = group[\"y\"][:]\n", + " else:\n", + " X, y = download_data(\n", + " start,\n", + " duration,\n", + " channels,\n", + " cache_file\n", + " )\n", + " else:\n", + " X, y = download_data(\n", + " start,\n", + " duration,\n", + " channels,\n", + " cache_file\n", + " )\n", + " return X, y\n", + "\n", + "\n", + "def get_live_data(duration, channels, start=None):\n", + " fnames = sorted((run_dir / \"live-data\" / \"lldetchar\" / ifo).iterdir())\n", + " t0 = int(fnames[0].stem.split(\"-\")[-2])\n", + " if start is not None:\n", + " t0 += start\n", + "\n", + " logger.info(\"Loading live data from span {}-{}\".format(\n", + " t0, t0 + duration\n", + " ))\n", + " ts = TimeSeriesDict.read(\n", + " fnames,\n", + " start=t0,\n", + " end=t0 + duration,\n", + " channels=channels[1:]\n", + " )\n", + " ts = ts.resample(sample_rate)\n", + " X = np.stack([ts.pop(i).value for i in channels[1:]])\n", + " logger.info(\"Witness channels loaded\")\n", + "\n", + " fnames = sorted((run_dir / \"live-data\" / \"kafka\" / ifo).iterdir())\n", + " ts = TimeSeries.read(\n", + " fnames,\n", + " start=t0,\n", + " end=t0 + duration,\n", + " channel=channels[0]\n", + " )\n", + " y = ts.resample(sample_rate).value\n", + " logger.info(\"Strain data loaded\")\n", + " return t0, X, y\n", + "\n", + "\n", + "def train(duration, start=None): # cache_file):\n", + " # X, y = get_data(\n", + " # cache_file,\n", + " # start,\n", + " # duration,\n", + " # channels\n", + " # )\n", + " t0, X, y = get_live_data(duration, channels, start=start)\n", + " weights_path = train_on_segment(\n", + " X,\n", + " y,\n", + " deployment,\n", + " start=t0,\n", + " architecture=DeepCleanAE,\n", + " sample_rate=sample_rate,\n", + " valid_frac=0.25,\n", + " kernel_length=8,\n", + " kernel_stride=1/16,\n", + " freq_low=58,\n", + " freq_high=62,\n", + " batch_size=256,\n", + " max_epochs=50,\n", + " device=device\n", + " )\n", + " return weights_path\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "57c385ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-12 20:50:04,309 - Deepclean sandbox - INFO - Loading live data from span 1370643654-1370647750\n", + "2023-06-12 20:51:35,518 - Deepclean sandbox - INFO - Witness channels loaded\n", + "2023-06-12 20:52:04,316 - Deepclean sandbox - INFO - Strain data loaded\n", + "2023-06-12 20:52:04,345 - Deepclean sandbox - INFO - Saving training outputs to directory /home/alec.gunny/deepclean/results/microservice/sandbox-live/train/1370643654-1370647750\n", + "2023-06-12 20:52:53,435 - DeepClean train 1370643654-1370647750 - INFO - Building and initializing model\n", + "2023-06-12 20:52:53,444 - DeepClean train 1370643654-1370647750 - INFO - DeepCleanAE(\n", + " (input_conv): ConvBlock(\n", + " (conv): Conv1d(18, 18, kernel_size=(7,), stride=(1,), padding=(3,))\n", + " (bn): BatchNorm1d(18, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (downsampler): Sequential(\n", + " (CONV_1): ConvBlock(\n", + " (conv): Conv1d(18, 8, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_2): ConvBlock(\n", + " (conv): Conv1d(8, 16, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_3): ConvBlock(\n", + " (conv): Conv1d(16, 32, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_4): ConvBlock(\n", + " (conv): Conv1d(32, 64, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " )\n", + " (upsampler): Sequential(\n", + " (CONVTRANS_1): ConvBlock(\n", + " (conv): ConvTranspose1d(64, 32, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_2): ConvBlock(\n", + " (conv): ConvTranspose1d(32, 16, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_3): ConvBlock(\n", + " (conv): ConvTranspose1d(16, 8, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_4): ConvBlock(\n", + " (conv): ConvTranspose1d(8, 18, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(18, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " )\n", + " (output_conv): Conv1d(18, 1, kernel_size=(7,), stride=(1,), padding=(3,))\n", + ")\n", + "2023-06-12 20:53:00,535 - DeepClean train 1370643654-1370647750 - INFO - Initializing loss and optimizer\n", + "2023-06-12 20:53:00,540 - DeepClean train 1370643654-1370647750 - INFO - Beginning training loop\n", + "2023-06-12 20:53:00,540 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 1/50 ===\n", + "2023-06-12 20:54:48,371 - DeepClean train 1370643654-1370647750 - INFO - Duration 107.83s, Throughput 454.7 samples/s\n", + "2023-06-12 20:54:51,996 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 2.8386e+01, Valid Loss: 1.2866e+00\n", + "2023-06-12 20:54:52,012 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 2/50 ===\n", + "2023-06-12 20:55:36,323 - DeepClean train 1370643654-1370647750 - INFO - Duration 44.31s, Throughput 1106.4 samples/s\n", + "2023-06-12 20:55:39,941 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 1.1102e+00, Valid Loss: 1.0581e+00\n", + "2023-06-12 20:55:39,956 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 3/50 ===\n", + "2023-06-12 20:56:15,780 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.82s, Throughput 1368.5 samples/s\n", + "2023-06-12 20:56:19,398 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 9.5492e-01, Valid Loss: 9.6403e-01\n", + "2023-06-12 20:56:19,413 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 4/50 ===\n", + "2023-06-12 20:56:55,239 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 20:56:58,857 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 8.6030e-01, Valid Loss: 9.0034e-01\n", + "2023-06-12 20:56:58,872 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 5/50 ===\n", + "2023-06-12 20:57:34,701 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 20:57:38,319 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 8.0453e-01, Valid Loss: 8.6289e-01\n", + "2023-06-12 20:57:38,345 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 6/50 ===\n", + "2023-06-12 20:58:14,175 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 20:58:17,792 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 7.6864e-01, Valid Loss: 8.2477e-01\n", + "2023-06-12 20:58:17,807 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 7/50 ===\n", + "2023-06-12 20:58:53,636 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 20:58:57,253 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 7.4508e-01, Valid Loss: 7.9832e-01\n", + "2023-06-12 20:58:57,269 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 8/50 ===\n", + "2023-06-12 20:59:33,099 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 20:59:36,717 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 7.2921e-01, Valid Loss: 7.7578e-01\n", + "2023-06-12 20:59:36,732 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 9/50 ===\n", + "2023-06-12 21:00:12,562 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:00:16,180 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 7.1685e-01, Valid Loss: 7.5371e-01\n", + "2023-06-12 21:00:16,195 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 10/50 ===\n", + "2023-06-12 21:00:52,024 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:00:55,641 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 7.0729e-01, Valid Loss: 7.4098e-01\n", + "2023-06-12 21:00:55,666 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 11/50 ===\n", + "2023-06-12 21:01:31,496 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:01:35,114 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.9995e-01, Valid Loss: 7.2631e-01\n", + "2023-06-12 21:01:35,130 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 12/50 ===\n", + "2023-06-12 21:02:10,958 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:02:14,575 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.9367e-01, Valid Loss: 7.1870e-01\n", + "2023-06-12 21:02:14,589 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 13/50 ===\n", + "2023-06-12 21:02:50,417 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:02:54,034 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.8904e-01, Valid Loss: 7.1431e-01\n", + "2023-06-12 21:02:54,049 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 14/50 ===\n", + "2023-06-12 21:03:29,878 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:03:33,496 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.8574e-01, Valid Loss: 7.0319e-01\n", + "2023-06-12 21:03:33,510 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 15/50 ===\n", + "2023-06-12 21:04:09,340 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:04:12,957 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.8155e-01, Valid Loss: 6.9919e-01\n", + "2023-06-12 21:04:12,983 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 16/50 ===\n", + "2023-06-12 21:04:48,812 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:04:52,430 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.7865e-01, Valid Loss: 6.9825e-01\n", + "2023-06-12 21:04:52,444 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 17/50 ===\n", + "2023-06-12 21:05:28,275 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:05:31,892 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.7571e-01, Valid Loss: 6.9922e-01\n", + "2023-06-12 21:05:31,905 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 18/50 ===\n", + "2023-06-12 21:06:07,733 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:06:11,351 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.7318e-01, Valid Loss: 6.9721e-01\n", + "2023-06-12 21:06:11,365 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 19/50 ===\n", + "2023-06-12 21:06:47,191 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:06:50,809 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.7199e-01, Valid Loss: 6.9516e-01\n", + "2023-06-12 21:06:50,824 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 20/50 ===\n", + "2023-06-12 21:07:26,654 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:07:30,274 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6987e-01, Valid Loss: 6.9907e-01\n", + "2023-06-12 21:07:30,293 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 21/50 ===\n", + "2023-06-12 21:08:06,131 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.84s, Throughput 1368.0 samples/s\n", + "2023-06-12 21:08:09,749 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6781e-01, Valid Loss: 6.9494e-01\n", + "2023-06-12 21:08:09,764 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 22/50 ===\n", + "2023-06-12 21:08:45,593 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:08:49,212 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6755e-01, Valid Loss: 6.9583e-01\n", + "2023-06-12 21:08:49,218 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 23/50 ===\n", + "2023-06-12 21:09:25,054 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.84s, Throughput 1368.1 samples/s\n", + "2023-06-12 21:09:28,675 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6432e-01, Valid Loss: 7.0830e-01\n", + "2023-06-12 21:09:28,681 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 24/50 ===\n", + "2023-06-12 21:10:04,519 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.84s, Throughput 1368.0 samples/s\n", + "2023-06-12 21:10:08,138 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6344e-01, Valid Loss: 6.9194e-01\n", + "2023-06-12 21:10:08,160 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 25/50 ===\n", + "2023-06-12 21:10:43,990 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:10:47,607 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6231e-01, Valid Loss: 7.0905e-01\n", + "2023-06-12 21:10:47,627 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 26/50 ===\n", + "2023-06-12 21:11:23,459 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:11:27,080 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6124e-01, Valid Loss: 7.1155e-01\n", + "2023-06-12 21:11:27,085 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 27/50 ===\n", + "2023-06-12 21:12:02,922 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.84s, Throughput 1368.0 samples/s\n", + "2023-06-12 21:12:06,542 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.6008e-01, Valid Loss: 7.0218e-01\n", + "2023-06-12 21:12:06,548 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 28/50 ===\n", + "2023-06-12 21:12:42,381 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:12:46,000 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5966e-01, Valid Loss: 6.9669e-01\n", + "2023-06-12 21:12:46,007 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 29/50 ===\n", + "2023-06-12 21:13:21,840 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:13:25,458 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5903e-01, Valid Loss: 7.0134e-01\n", + "2023-06-12 21:13:25,464 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 30/50 ===\n", + "2023-06-12 21:14:01,290 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:14:04,910 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5839e-01, Valid Loss: 7.1507e-01\n", + "2023-06-12 21:14:04,927 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 31/50 ===\n", + "2023-06-12 21:14:40,760 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:14:44,378 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5657e-01, Valid Loss: 7.0434e-01\n", + "2023-06-12 21:14:44,384 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 32/50 ===\n", + "2023-06-12 21:15:20,219 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.1 samples/s\n", + "2023-06-12 21:15:23,837 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5712e-01, Valid Loss: 7.2002e-01\n", + "2023-06-12 21:15:23,843 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 33/50 ===\n", + "2023-06-12 21:15:59,677 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:16:03,297 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5633e-01, Valid Loss: 7.0952e-01\n", + "2023-06-12 21:16:03,304 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 34/50 ===\n", + "2023-06-12 21:16:39,132 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.4 samples/s\n", + "2023-06-12 21:16:42,749 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5455e-01, Valid Loss: 7.1624e-01\n", + "2023-06-12 21:16:42,755 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 35/50 ===\n", + "2023-06-12 21:17:18,585 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:17:22,204 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5330e-01, Valid Loss: 7.2483e-01\n", + "2023-06-12 21:17:22,222 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 36/50 ===\n", + "2023-06-12 21:17:58,055 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:18:01,673 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5521e-01, Valid Loss: 7.1553e-01\n", + "2023-06-12 21:18:01,679 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 37/50 ===\n", + "2023-06-12 21:18:37,509 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.3 samples/s\n", + "2023-06-12 21:18:41,128 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5334e-01, Valid Loss: 7.2034e-01\n", + "2023-06-12 21:18:41,135 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 38/50 ===\n", + "2023-06-12 21:19:16,968 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:19:20,587 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5202e-01, Valid Loss: 7.1586e-01\n", + "2023-06-12 21:19:20,592 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 39/50 ===\n", + "2023-06-12 21:19:56,425 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:20:00,045 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5450e-01, Valid Loss: 7.3204e-01\n", + "2023-06-12 21:20:00,052 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 40/50 ===\n", + "2023-06-12 21:20:35,886 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:20:39,505 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5273e-01, Valid Loss: 7.1608e-01\n", + "2023-06-12 21:20:39,523 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 41/50 ===\n", + "2023-06-12 21:21:15,360 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.84s, Throughput 1368.0 samples/s\n", + "2023-06-12 21:21:18,978 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5163e-01, Valid Loss: 7.4276e-01\n", + "2023-06-12 21:21:18,985 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 42/50 ===\n", + "2023-06-12 21:21:54,821 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.1 samples/s\n", + "2023-06-12 21:21:58,441 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.4882e-01, Valid Loss: 7.0839e-01\n", + "2023-06-12 21:21:58,447 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 43/50 ===\n", + "2023-06-12 21:22:34,279 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.2 samples/s\n", + "2023-06-12 21:22:37,898 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5041e-01, Valid Loss: 7.3223e-01\n", + "2023-06-12 21:22:37,904 - DeepClean train 1370643654-1370647750 - INFO - === Epoch 44/50 ===\n", + "2023-06-12 21:23:13,739 - DeepClean train 1370643654-1370647750 - INFO - Duration 35.83s, Throughput 1368.1 samples/s\n", + "2023-06-12 21:23:17,359 - DeepClean train 1370643654-1370647750 - INFO - Train Loss: 6.5079e-01, Valid Loss: 7.5146e-01\n", + "2023-06-12 21:23:17,376 - DeepClean train 1370643654-1370647750 - INFO - Performing post-hoc analysis on trained model\n" + ] + }, + { + "data": { + "text/plain": [ + "DeepClean(\n", + " (preprocessor): Preprocessor(\n", + " (scaler): ChannelWiseScaler()\n", + " )\n", + " (deepclean): DeepCleanAE(\n", + " (input_conv): ConvBlock(\n", + " (conv): Conv1d(18, 18, kernel_size=(7,), stride=(1,), padding=(3,))\n", + " (bn): BatchNorm1d(18, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (downsampler): Sequential(\n", + " (CONV_1): ConvBlock(\n", + " (conv): Conv1d(18, 8, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_2): ConvBlock(\n", + " (conv): Conv1d(8, 16, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_3): ConvBlock(\n", + " (conv): Conv1d(16, 32, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONV_4): ConvBlock(\n", + " (conv): Conv1d(32, 64, kernel_size=(7,), stride=(2,), padding=(3,))\n", + " (bn): BatchNorm1d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " )\n", + " (upsampler): Sequential(\n", + " (CONVTRANS_1): ConvBlock(\n", + " (conv): ConvTranspose1d(64, 32, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_2): ConvBlock(\n", + " (conv): ConvTranspose1d(32, 16, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_3): ConvBlock(\n", + " (conv): ConvTranspose1d(16, 8, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " (CONVTRANS_4): ConvBlock(\n", + " (conv): ConvTranspose1d(8, 18, kernel_size=(7,), stride=(2,), padding=(3,), output_padding=(1,))\n", + " (bn): BatchNorm1d(18, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activation): ELU(alpha=1.0)\n", + " )\n", + " )\n", + " (output_conv): Conv1d(18, 1, kernel_size=(7,), stride=(1,), padding=(3,))\n", + " )\n", + " (postprocessor): Postprocessor(\n", + " (scaler): ChannelWiseScaler()\n", + " )\n", + ")" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cache_file = deployment.storage_directory / \"train.h5\"\n", + "train_start = 1243278913\n", + "train_duration = 4096\n", + "\n", + "train_stop = train_start + train_duration\n", + "train_dir = deployment.train_directory / f\"{train_start}-{train_stop}\"\n", + "weights_path = train_dir / \"weights.pt\"\n", + "if not weights_path.exists():\n", + " weights_path = train(train_duration, None) #, cache_file)\n", + "else:\n", + " logger.info(f\"Loading weights from {weights_path}\")\n", + "\n", + "preprocessor = ChannelWiseScaler(len(channels) - 1)\n", + "postprocessor = ChannelWiseScaler()\n", + "nn = DeepCleanAE(len(channels) - 1)\n", + "deepclean = DeepClean(preprocessor, nn, postprocessor)\n", + "deepclean.load_state_dict(torch.load(weights_path))\n", + "deepclean = deepclean.to(device)\n", + "deepclean.eval()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "02c2d899", + "metadata": {}, + "source": [ + "# Export\n", + "Set up the input and output caching models to use locally" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "b81dc63e", + "metadata": {}, + "outputs": [], + "source": [ + "infer_batch_size = 256\n", + "inference_sampling_rate = 512\n", + "stride = int(sample_rate / inference_sampling_rate)\n", + "aggregation_size = int(0.5 * sample_rate)\n", + "num_updates = int(aggregation_size // stride)\n", + "\n", + "snapshotter = Snapshotter(\n", + " snapshot_size=sample_rate,\n", + " stride_size=stride,\n", + " batch_size=infer_batch_size,\n", + " channels_per_snapshot=[len(channels) - 1]\n", + ").to(device)\n", + "\n", + "aggregator = OnlineAverager(\n", + " update_size=stride,\n", + " batch_size=infer_batch_size,\n", + " num_updates=num_updates\n", + ").to(device)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "0bb769f5", + "metadata": {}, + "source": [ + "# Inference\n", + "## Build dataset\n", + "Start by creating a small offline dataset of the test frames" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "378f7b7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-06-12 21:29:10,554 - Deepclean sandbox - INFO - Loading live data from span 1370647750-1370647878\n", + "2023-06-12 21:29:14,368 - Deepclean sandbox - INFO - Witness channels loaded\n", + "2023-06-12 21:29:18,418 - Deepclean sandbox - INFO - Strain data loaded\n" + ] + } + ], + "source": [ + "test_start = train_start + train_duration\n", + "test_duration = 128\n", + "t0, X, y = get_live_data(test_duration, channels, start=train_duration)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "3cb5575d", + "metadata": {}, + "source": [ + "## Fully offline inference\n", + "First test: run through the offline data and postprocess it in an offline fashion" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "07fb69e4", + "metadata": {}, + "outputs": [], + "source": [ + "def do_inference(data_it):\n", + " input_snapshot = torch.zeros((1, len(channels) - 1, sample_rate))\n", + " input_snapshot = input_snapshot.to(device)\n", + " snapshot_size = stride * (num_updates + infer_batch_size - 1)\n", + " output_snapshot = torch.zeros((snapshot_size,)).to(device)\n", + "\n", + " for x in data_it:\n", + " with torch.no_grad():\n", + " x = torch.Tensor(x).to(device)\n", + " x, input_snapshot = snapshotter(x[None], input_snapshot)\n", + " y_hat = deepclean(x)\n", + " y_hat, output_snapshot = aggregator(y_hat, output_snapshot)\n", + " yield y_hat.cpu().numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "bca76146", + "metadata": {}, + "outputs": [], + "source": [ + "def offline_it():\n", + " step_size = int(stride * infer_batch_size)\n", + " num_steps = (X.shape[-1] - sample_rate) // step_size + 1\n", + " for i in range(num_steps):\n", + " start = i * step_size\n", + " stop = (i + 1) * step_size\n", + " yield X[:, start: stop]\n", + "\n", + "\n", + "def get_state():\n", + " cb = reload(callback)\n", + " return cb.State(\n", + " \"noise\",\n", + " frame_length=1,\n", + " memory=8,\n", + " filter_pad=1,\n", + " sample_rate=sample_rate,\n", + " inference_sampling_rate=inference_sampling_rate,\n", + " batch_size=infer_batch_size,\n", + " aggregation_steps=num_updates - 1,\n", + " freq_low=58,\n", + " freq_high=62,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "1bd78349", + "metadata": {}, + "outputs": [], + "source": [ + "state = get_state()\n", + "predictions = []\n", + "for y_hat in do_inference(offline_it()):\n", + " y_hat = y_hat[0]\n", + " if not predictions:\n", + " y_hat = y_hat[(num_updates - 1) * stride:]\n", + " predictions.append(y_hat)\n", + "predictions = np.concatenate(predictions)\n", + "predictions = state.cleaner.bandpass(predictions)\n", + "clean = y[:len(predictions)] - predictions" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "67dd3e0a", + "metadata": {}, + "source": [ + "Now let's build some plotting utilities to ensure that we've cleaned appropriately." + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "id": "becd8b78", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3nNksYl9RJbIOncXWQ2exr6jS5v7wUDXGxoVjXHwERsWEOTRMolAI6Bsegr7hIQ6Fhl8LK/BrYYX18ReHhmuay9LGRoS2+8zp97cdw878MgSqlFg2fYjXjaFaehA4xNB2lvkbOhdqIEhFkL8f5k7qj0mDI/Hiun04eLoKMV1DMKxPZwyP7oJhvTujm7b9l+0qFAJGxYRhVEwYymr0yNxbhDU5J5FfWot/7z6Ff+8+hdiIENw5tCduS+zu1pLlUiKZgBAUFIQJEyZgwoQJ+Oyzz1BfX3/Z8w0GA7Zu3YpXX33VJiXOmDEDK1aswJ49ezB06NCObnarqhsa8fVvxVibU2jzAdizcxDuHBqFaUk9OnS9uyA0ffO+LroLymr644vcU1iTcxLHy+rwyc8n8MnPJzCkpw7Th/fCpEEdP9mn2jLB8NBZbDtcajMJEwAG99BibHxX3BAfgQGRGpsxyLZqLTQcL6vFPgdCg0opIK5bKK6J1GJA96aqdVd30yCwjRPPfjl+Hm9uOQIAWDjlGvTr6n2zsbmKwXVVHVADwdslROmweU4yGhpNbg+9XULU1i9FuSfKsSanEP/Z1zSBe+HGPLy++RBuvKYb7hoahcFROgSolCz41EwyAaF79+549tlnAQD79+/H7t27L3t+fn4+DAaDXU+D5fbhw4dbDQh6vR56vd7mttFodKX5VvuLKvHPXcex4bfTqG9s6ir3VyowYUAE7hrWEyOiu7TLh58zuoSo8dCYvpiVHI2d+WVYnXMC3x4owZ6TFdhzsgILNhzAlMTuiNQFOvycjr4Cg9GMnwvKkFNw3mZ3uRC1H5JjwjA2vitS4sLRNdQ9xYEUCgHR4SGIbiU07G8ODPuLKlHVYMT+oirsL6oCfilserwA9OsaYg0N10Rq0D9Sc8Vxz/JaA55YsxdmEbgtsTumJvXo8NfaFpZvvXqj2SN/7OWgQgY1ENrKk78vFw+1pk/uj69+LcbanJM4UFyFDb8VY8NvF4Y//BQC1H4KBKiUUPspoL7kf63Hbf5biQBV0/+qVU33+SkV8FMI8FMIUCkV8FMKzbeb/lulVECpEKBSNh1TXnSeSqGAUilApRDgp2x6Pndvty2ZgOCs8vKmMfxOnWzX4ep0OgBARUVFq49dvHgxMjIybI6lpqYiNzfX5WGJ4+dqoamuwd3RQLBaie66QFylDYS/XyNQlo9fyvJden5XqQDc2w9I661DcUUDisrrUN+oB87/gXPnO+aaVyuBq/sBQf5KhIeoERbqD12gPxQKI2AqRkFeMQo65tJO6QagWxgwPkyAOFCLhkYTqhqMqG5oRHWDEdUNxuYlWZVAXSVOHwVOHwW2oOm1hQb4QROgQmiAH0IDVNbhCVEU8dupSkzspkdQTyWGR9UjOzvbky+1VaIoYla8CaLYNMeHAcF54fVV+GucCdGKEmRn13q6OT4rVgDmDfdHVb0WRRX1OFPVAGMLW2C3ytT8c1Fnp6H5pyPqSWoDVe22QZvJZLrySZBxQBCbqweZzWaHjl/sxRdfxNNPP229rdfr8fbbbyMpKQlqtWubnkRX65Gz+RDShkXhWglUOTSbRWw/WooteSV265EtxCv8mxLR+gkCBFx9VSjGxXdFtMQLnIiiiLPVeuwvqsT+oirrtrtFFZbhMGPzT5PuukAMiNQgyF+J9b8a4a9UIfOxkRgQ6d0rSh7dsgXnaw2YdvNAxHUL9XRzJOdfa/bi68PFmNuvD4YPj/Z0c6iZ2SyiwWiCvtFs7SHTG83QG00Xbl/uPqMZ+kbzRc9hgtEkwmgWYTSbm/+76X8bzSKMJjNMZhGNJnPTORffbznWfJ5ZBIb21uLx4cPb5bXq9Xps2bLliufJNiBYeg4u7Smw9CxYehJaolarbYKAXq+Hn1/7vFXhoWosuWNwuzyXOygUAlLiunq8xK8UCIKACE0AIjQBuOHqCOvx87UGHCi2DQ0F52pRVFF/UXgA/t/Eq70+HABNy/PO1xpQwf0Y2qTCB+cgSIFCISDI3w/eWNzSbBZhutI3sQ4g24DQt29fqFQq5OXl4eabb7Yez8vLAwDEx8d7qmnkYzoH+yM5JhzJMeHWY9UNjcgrrsL+4qbQ0KtzMO4Z0cuDrXQcqym6ptK6isELP4nIKykUAhQOz+xqP7INCGq1GikpKVi9ejWeeuopKBRN472rV69GREQEkpKSPNxC8mWhASoMj+6C4dFdPN0Up3Elg2t8cRUDSZOsAsK+fftQVFSEMWPGIDAwEPPmzUNKSgrS0tKQlpaG7OxsrFixAsuXL/eaGghEUmOpAFjJWghtYhma8YU6CCRt3leE3gVLlizBzTffjJKSEgDAqFGjkJmZicOHD2P69On44osvsGzZMjz44IMebimRdFm6xllN0Xlms2jteWEPAnk7SfYgrFy5stXjl96XmpqK1NTUjm8UkY/QcIihzWoMRuveAwwI5O1k1YNARB1Px3LLbWYZlrEU2CHyZgwIROQUy9g5exCcx+EFkhIGBCJyClcxtB0DAkkJAwIROcW65TOHGJxW6UM7OZL0MSAQkVO0gc2rGFhJ0WnsQSApYUAgIqdYPtyq9UaYzO4v/yplF3ZyZBVF8n4MCETkFEtAEMWmktHkOPYgkJQwIBCRU/z9FAj2b1qix3kIzqlsLi7FgEBSwIBARE7jSoa24SRFkhIGBCJymtZabpkBwRkcYiApYUAgIqddqKbIlQzOuDBJkQGBvB8DAhE5zfIBV8UeBKdYexA4xEASwIBARE5jsaS24RADSQkDAhE5zfINmHMQHGcyi6huMAJgQCBpYEAgIqdxFYPzLh6OYUAgKWBAICKn6azllhkQHGUJU8H+SqiU/NNL3o+/pUTktAtbPnMVg6MqrDUQWGaZpIEBgYicxiEG51neKw2HF0giGBCIyGnaQK5icJalZoQ20M/DLSFyDAMCETlNx1UMTrNMUtRxJ0eSCAYEInKapQfBYDSjodHk4dZIA2sgkNQwIBCR00LUflAqBAAcZnCUtcwyqyiSRDAgEJHTBEG4sB8DVzI4hD0IJDUMCETUJtaVDOxBcAgDAkkNAwIRtQnLLTvnQh0EBgSSBgYEImoTHXsQnFLFHgSSGAYEImoTFktyjnWSIgMCSQQDAhG1iaVkMCcpOqaSdRBIYhgQiKhNWE3RcXqjCfXN9SLYg0BSwYBARG3CIQbHWd4jQQBCA1hqmaSBAYGI2uTCjo4MCFdimaCoCVBB0VxgisjbMSAQUZtY92PgEMMVsQYCSREDAhG1CYcYHGcJUayBQFLCgEBEbaJtno1v2caYWsceBJIiBgQiahPLt+GqBiNMZtHDrfFulh4EDQMCSQgDAhG1ycXfhqsbOMxwORdqIDAgkHQwIBBRm6iUCgT7KwFwouKVcIiBpIgBgYja7EI1RQaEy2FAICliQCCiNtNwJYNDKrmTI0kQAwIRtZnOWm6ZKxkuhz0IJEUMCETUZqym6BhLgNJyoyaSEAYEImoza7EkTlK8rMp6IwD2IJC0MCAQUZtpLeWW2YPQKlEUUdm8JbaWcxBIQhgQiKjNdNZqigwIralvNKHR1FRIinUQSEoYEIiozbgfw5VZ3hs/hYCg5roRRFLAgEBEbXZhkiJXMbTG0ruiDVRBELjVM0kHAwIRtdmFZY7sQWiNdYkj5x+QxDAgEFGbsVDSlbEGAkkVAwIRtZnuolUMosgdHVtiWQLKCYokNZIKCFlZWUhMTERgYCD69OmDJUuWXPExK1aswMCBAxEcHIyYmBgsWLAAer3eDa0lkj/LXgwGoxkNjWYPt8Y7sQeBpEoyASE7OxuTJk1CbGws1qxZg2nTpuG5557D0qVLW33MO++8gwcffBATJkzAp59+invvvRevv/46Hn/8cTe2nEi+gv2VUCqaJt5xmKFlFZYaCAwIJDF+nm6Ao9LT09G/f3+sXbsWgiBgypQpKC8vx6JFizB79mwolfbLh5YvX460tDRrT8Ott94KAFiwYAHeeecdqNVqt74GIrkRBAG6QBXKag2oqDegmzbA003yOhcmKbLMMkmLJHoQDAYDtm7diunTp9ssE5oxYwZKS0uxZ8+eFh93/vx5dO7c2eZYWFgYGhsbUVtb26FtJvIV1mqKXMnQIpZZJqlyqgfh5MmT7Xbhnj17Onxufn4+DAYDBgwYYHPccvvw4cMYOnSo3eNmzpyJ999/HzfccANGjRqFgwcP4vXXX8fUqVPtggMRtQ2LJV3ehY2aGBBIWpwKCL17926XQh+CIMBoNDp8fnl5OQCgU6dONsd1Oh0AoKKiosXHLV68GL/88gtuu+0267G4uDh89NFHl72eXq+3mcio1+udai+RL9Fxw6bLqqrnKgaSJqfnIAwdOhQ333xzmy+4adMm7N6926nHWJZPmc1mh45b/OUvf8GePXvw2muvITExEUeOHMGiRYtw0003Ydu2bVCpWv4Hu3jxYmRkZNgcS01NRW5ubotzHYh82ShtJfrGmYDSo8jOLvZ0c7zOmE6VGBpsgrHkCLKrj3u6OUQwmUwOned0QBg2bBjS09OdbpBFaWmp0wHB0nNwaU+BpWfB0pNwsd27d2PVqlVYs2YN0tLSAAATJkxAUlISRo4cifXr12PatGktXu/FF1/E008/bb2t1+vx9ttvIykpiRMbiS6x+esDWHn4OB6LjMQdw+M93Ryv89B/v0VFHXDnLQmIiQj1dHOIoNfrsWXLliue51RAmDNnDkaPHt3mRgHAuHHj4O/v3Gzevn37QqVSIS8vz6b3Ii8vDwAQH2//Ryk/Px8AMGLECJvjw4YNg0KhsN7fErVabRME9Ho9/Pwks+CDyK20LLfcKrNZtA4xcA4CSY1TqxhWrlyJ7Oxsly6YnZ2NlStXOvUYtVqNlJQUrF692mY4YfXq1YiIiEBSUpLdY+Li4gAAP/zwg83xHTt2wGw2IzY21vnGE5Eda0DgJEU71XojzM0FJjUMCCQxTn0trqysRF1dnUsXrKurQ2VlpdOPmzdvHlJSUpCWloa0tDRkZ2djxYoVWL58uXVewL59+1BUVIQxY8YgISEBf/7zn/Hoo48iPz8fCQkJOHLkCP72t79h6NChmDx5skuvg4iaWMotVzEg2LG8JwEqBQJUnL9E0uJ0v/k///lPbNy4sc0XLCsra9PjRo0ahczMTMydOxfTp09H9+7dsWzZMjz44IPWc5YsWYJVq1ahoKAAvXv3xtq1a7Fo0SJ8+umneP3119GzZ0/MmjULc+fO5ZABUTvRsQ5Cq1hmmaTMqU/J0aNHu7zMsVevXm1+bGpqKlJTU1u9f+XKlTbDFwEBAVi4cCEWLlzY5msS0eVdGGIweLgl3scSmhgQSIqcCgjbtm3roGYQkVRpA5smHbMOgr1Kaw0Ellkm6ZFEqWUi8l7WOQgNRpjM3PL5YpaAwAmKJEUMCETkkou7zzlR0RZ3ciQpY0AgIpeolAoE+zfN0Od+DLasQwxBDAgkPQwIROQyXfNWxqyFYKuSkxRJwhgQiMhlGms1Ra5kuBh7EEjKOiQgmEwmHDhwAMXF3LiFyBfouOVzi1gHgaTM5YCwbds2/OUvf7HubXDmzBkMHDgQgwYNQlRUFGbNmmXddZGI5MnyDZkBwZalDgJXMZAUuRQQNm3ahPHjx2PlypWoqqoCALzwwgs4cuQIZs2aheTkZHz00Ud477332qWxROSduGFTyy7UQWBAIOlxKSAsXrwYOp0Ov/76KxITE6HX67Fu3TrMnDkTy5cvR1ZWFqKjo7Fq1ar2ai8ReSEtexBaxJ0cScpcCgh5eXm45ZZbMHDgQABATk4OampqMGnSJACAUqlEcnIyDh065HpLichrWSoFsgfhAqPJjGq9EQADAkmTSwFBFEXrTopA09bKgiBg9OjR1mNGoxEGA2c2E8mZ1jpJkf/WLaoajNb/ZkAgKXIpIMTGxmLLli2ora2F0WjE2rVrMWjQIISHhwMAGhoa8MMPPyAqKqpdGktE3omTFO1ZlnyGqP3gp+SKcpIel35rn3jiCRQXFyMuLg7XXHMNDh48iAceeAAAsHHjRiQkJODUqVOYMmVKe7SViLyUjpMU7XCJI0mdSwFh+vTpyMjIQGNjI0pLS/HQQw/h0UcfBQD89NNPOHLkCCZMmID09PR2aSwReSdroST2IFgxIJDUObXdc0tefvllvPzyy3bHH3nkETz88MPo1auXq5cgIi938RCDKIoQBMHDLfI8BgSSug6rpFhdXQ2Viv8wiHyBZS8Gg9GMhkazh1vjHVhmmaSOlRSJyGXB/kooFU29BhVcyQCAGzWR9LGSIhG5TBAE7sdwiQoOMZDEsZIiEbULSzVFrmRoYp2DwCEGkihWUiSidqFlD4KNCg4xkMSxkiIRtQvrEAN7EABc2IfBUoaaSGpYSZGI2oVlJQMnKTbhMkeSOlZSJKJ2wSEGW5agxIBAUsVKikTULrQst2yDdRBI6lhJkYjaheWDkOWWgYZGk7VglIY9CCRRLgeEixkMBpSXlyMiIgI9e/Zsz6cmIi9n6UGoYkCwvgeCAISq2/XPLJHbuFxJsaamBhkZGRgwYACCg4PRvXt3AMDf/vY3zJ07F9XV1S43koi8n451EKwunqCoUHBfCpImlwLCuXPnMHToUCxYsAABAQGIjo62llWurq7Gq6++ilGjRqGioqI92kpEXkwbyFUMFqyiSHLgUkBIT0/H4cOH8eabbyI3NxcTJkyw3rdgwQK8//772LdvH9544w2XG0pE3k3LOghWlvdAx4BAEuZSQNiwYQOGDBmCOXPmtHj/Qw89hJEjRyIzM9OVyxCRBFiGGKoajDCZfXuDNssQAycokpS5FBDOnj2LwYMHX/ac+Ph4nDhxwpXLEJEEXNyd7usTFTnEQHLgUkAIDw/HyZMnL3vOsWPHoNPpXLkMEUmASqlAsH9T6XVfL5bEGggkBy4FhIkTJ2Lr1q3YsWNHi/dv3LgR27dvt5mbQETydaHcsm8HhCr2IJAMuBQQMjIyEBkZiQkTJuDhhx/G3r17AQDLli3D3XffjVtvvRVhYWGYP39+e7SViLzchWqKvr2SwfL6GRBIylyq4BEREYGdO3fisccew4cffmhd4vjEE08AAEaPHo3ly5ejd+/eLjeUiLwf92NoUsmdHEkGXC7x1aNHD3z11VcoKSlBbm4uKioqEBISgoSEBFZTJPIxljF3Xw8IFVzFQDLQbjVAIyIicMstt7TX0xGRBLGaYhNOUiQ5cCogqFQqCILzZUMFQYBer3f6cUQkLRoOMQDgJEWSB6cCwvXXX9+mgEBEvsEy5u7LPQiiKFpfPwMCSZlTAWHbtm0d1AwikoMLcxB8dxVDncEEY3MlSQ4xkJS5vJsjEZEFVzFceO0qpYBAldLDrSFqO5cnKWZnZ2PdunUoLS1t9RxBEPCPf/zD1UsRkZfTBXKS4sXDCxySJSlzKSBs2LABt99+O4xG42XPY0Ag8g1ayyoG9iBw/gFJnsuVFEVRxCeffIK6ujqYzeYWf0wmU3u1l4i82MVDDJbCab6GAYHkwqUehEOHDmHixIm4++6726s9RCRhlr0YDEYzGhrNCPT3vTF4ywRNBgSSOpd6EDp16oTg4OD2agsRSVywvxJ+iqZx9wofXclwoUgSyyyTtLkUEGbOnInvv/8e58+fb6/2EJGECYLg8ysZWAOB5MKlgLBgwQKMGzcOY8eOxebNm1FRUdFOzSIiqdL6eLllzkEguXApIAwaNAh79+5FXl4eJk2ahC5dusDf39/uR61Wt0tjs7KykJiYiMDAQPTp0wdLliy54mOOHTuGiRMnQqfTISwsDNOnT8fp06fbpT1EZM/XlzoyIJBcuDRJsWvXrhAEAREREe3VnlZlZ2dj0qRJmDx5MtLT07Fz504899xz8PPzw5w5c1p8TFFREcaNG4fo6Gh88MEHqKmpwSuvvIKJEydi9+7dUChYJ4qovVk+GKt8dIiBAYHkwqWAcGnpZb1ej6qqKoSGhiIgIMCVp7aTnp6O/v37Y+3atRAEAVOmTEF5eTkWLVqE2bNnQ6m0ny2dkZEBQRDwzTffWNszaNAgPP744zhy5Aji4+PbtY1EdGFyHicpMiCQtLn8FbqhoQGvvPIK4uPjERQUhG7duiE4OBjx8fFYuHAhGhoaXG6kwWDA1q1bMX36dJvKZDNmzEBpaSn27NnT4uPWrVuHmTNnIiAgACaTCWazGUOHDsXPP//McEDUQbQcYgDAHgSSPpcCQnV1Na6//nqkp6ejuLgYycnJuOOOOzBmzBicOXMG8+fPR3JyMmpra11qZH5+PgwGAwYMGGBz3HL78OHDdo8pLCxEWVkZAgMDMXHiRAQEBCAgIACpqakoLCx0qT1E1DquYmBAIHlwKSAsWrQIe/fuxcMPP4yTJ09i27ZtWLNmDb7//nucOHECDz74IHJzc/Haa6+51Mjy8nIATXUXLqbT6QCgxdUTlomICxYsQFBQENatW4cPPvgAe/bswQ033HDZng3LUMnFP1cqJ01ETXQ+XG7ZbBZR1dAcEDjEQBLn0hyEL7/8EgkJCXj33Xft7tNqtXj//fexa9cufPbZZ1i4cGGbr2Mp2Wo2mx06DjQNSwDA9ddfj88//9x6PD4+HiNHjsS///1v3HPPPS1eb/HixcjIyLA5lpqaitzc3BbnOhDRBZ1q6/HXOBO6+J1Fdna2p5vjVo1GM/4S21Ra/tj+X1Gg4GZN5H0c3f7ApYBQWFiI++6777LnDB8+HKtWrXLlMtaeg0t7Ciw9C5aehItZKjxOnjzZ5viIESOg0Wjw+++/t3q9F198EU8//bT1tl6vx9tvv42kpKR2W7JJJFffHyrBiu93Y2D3EDxyx3BPN8etTpbVYcWGrQhUKTH3/us83RyiFun1emzZsuWK57k0xKDVanH8+PHLnnPy5MkWP8Cd0bdvX6hUKuTl5dkct9xuacJh3759IQgC9Hq93X1ms/myJaLVajU0Go3Nj5+fyztjE/kEbaDvrmLgBEWSE5cCwrhx4/Ddd98hMzOzxfvXr1+Pb7/9FjfccIMrl4FarUZKSgpWr15tM5ywevVqREREICkpye4xGo0Gw4cPx2effWbzmE2bNqGmpgZjxoxxqU1E1DKdD1dSrOBGTSQjLn0tXrhwITZv3oypU6di3LhxGDduHLp164aSkhJ8//33yMrKglarxfz5811u6Lx585CSkoK0tDSkpaUhOzsbK1aswPLly63zAvbt24eioiKMGTMGgYGBmD9/Pm6++WbcdNNNuP/++1FcXIyFCxfilltuwbhx41xuExHZs3w4VjcYYTKLUPrQOLy1B4ETFEkGXAoI/fr1w44dO/DII48gKysLWVlZNvePGjUK7733HmJiYlxqpOW5MjMzMXfuXEyfPh3du3fHsmXL8OCDD1rPWbJkCVatWoWCggL07t0bN954I7744gssWLAA999/P8LCwvDQQw9hwYIFLreHiFp28bfnqvpGdAr2nV0NOcRAcuLywPrAgQPx448/4sSJE/j9999RVVUFjUaDQYMGoVevXu3RRqvU1FSkpqa2ev/KlSuxcuVKm2O33XYbbrvttnZtBxG1TqVUIETthxq9ERU+FhBYA4HkpN1m3vXq1avdAwERSZM2UIUavdHniiVZ9p/QMSCQDHC3IiJqdxfKLfvWSgb2IJCcOBUQlEqlzc6JCoUCSqXyij9cIkjkWywrGXytB4EbNZGcOPXJPXPmTAwbNsx6+5577rHZPImICPDd/Rgsr1fDHgSSAacCwqUTAC+9bWEymXDo0CF06tQJkZGRbW0bEUmUr9ZCqOAqBpIRl+cgbNu2DX/5y1+Qn58PADhz5gwGDhyIQYMGISoqCrNmzbLumUBEvsFaTdHHAoJ1kmKQ76zcIPlyKSBs2rQJ48ePx8qVK1FVVQUAeOGFF3DkyBHMmjULycnJ+Oijj/Dee++1S2OJSBp8fYiBPQgkBy4FhMWLF0On0+HXX39FYmIi9Ho91q1bh5kzZ2L58uXIyspCdHS0y5s1EZG0XJik6DurGBpNZtTom7aFZ0AgOXApIOTl5eGWW27BwIEDAQA5OTmoqanBpEmTADStekhOTsahQ4dcbykRSYYu0PfmIFRd1FuiCeDKLZI+lwKCKIrWfRAA4IcffoAgCBg9erT1mNFohMHgO98iiMg3hxgsExRD1X7wU7LEDEmfS7/FsbGx2LJlC2pra2E0GrF27VoMGjQI4eHhAICGhgb88MMPiIqKapfGEpE0WDYrqvChgMCNmkhuXAoITzzxBIqLixEXF4drrrkGBw8exAMPPAAA2LhxIxISEnDq1ClMmTKlPdpKRBJhmcVfWdfoM6uYOEGR5MalgDB9+nRkZGSgsbERpaWleOihh/Doo48CAH766SccOXIEEyZMQHp6ers0loikwfIhaTCZ0dBo9nBr3KOSZZZJZlweKHv55ZdRUlKCsrIyvPfee9Y5CY888ggKCgqwefNmhISEuNxQIpKOYH8l/BRNVVYrfGQlA8ssk9x02FTbnj17dtRTE5GXEwQBuiAVztUYUFHXiKu0gZ5uUofjEAPJjcsBITs7G+vWrUNpaWmr5wiCgH/84x+uXoqIJEQT2BQQfGUlg2VJJ/dhILlwKSBs2LABt99+O4xG42XPY0Ag8j2+VgvBOsQQyDLLJA8uzUHIyMiAKIr45JNPUFdXB7PZ3OKPyWRqr/YSkURYVzL4zByEptfJIQaSC5d6EA4dOoSJEyfi7rvvbq/2EJFM+FqxJE5SJLlxqQehU6dOCA4Obq+2EJGMaH10iIE9CCQXLgWEmTNn4vvvv8f58+fbqz1EJBM6H6umWME6CCQzLgWEBQsWYNy4cRg7diw2b96MioqKdmoWEUmdrw4xMCCQXLg0B2HQoEEAgKNHj9rs4HgpQRCg1+tduRQRSYx1y2cfGGJoaDRBb2yqGMm9GEguXAoIXbt2hSAIiIiIaK/2EJFMWJb7+UIlRUvvgUIAQvy51TPJg0u/ydu2bWunZhCR3Gh8aIjh4uEFRXOJaSKpa9dNyw0GA0pKStrzKYlIoqyTFH1giIETFEmOXA4INTU1yMjIwIABAxAcHIzu3bsDAP72t79h7ty5qK6udrmRRCQ9lkqK1Q1GmMzy3vLZ2oMQxCqKJB8uBYRz585h6NChWLBgAQICAhAdHW3d+726uhqvvvoqRo0axdUNRD7o4j0JqmQ+zMAVDCRHLgWE9PR0HD58GG+++SZyc3MxYcIE630LFizA+++/j3379uGNN95wuaFEJC0qpQIh6qZpTnKvhVBRxzLLJD8uBYQNGzZgyJAhmDNnTov3P/TQQxg5ciQyMzNduQwRSdSFaoryXslQZd2oiQGB5MOlgHD27FkMHjz4sufEx8fjxIkTrlyGiCTKV4olcYiB5MilgBAeHo6TJ09e9pxjx45Bp9O5chkikihrsSSZB4QKBgSSIZcCwsSJE7F161bs2LGjxfs3btyI7du328xNICLf4StLHS+sYmBAIPlwKSBkZGQgMjISEyZMwMMPP4y9e/cCAJYtW4a7774bt956K8LCwjB//vz2aCsRSYyvDDGwDgLJkUuVFCMiIrBz50489thj+PDDD61LHJ944gkAwOjRo7F8+XL07t3b5YYSkfRoLeWWZd6DUMUhBpIhl4uG9+jRA1999RVKSkqQm5uLiooKhISEICEhAT179myPNhKRRF3Y8lneqxgsPSQ6DjGQjLTLriINDQ0wmUxISEiwOV5cXGz978jIyPa4FBFJiOUbtZwLJYmiyEmKJEsuBYTCwkLMnDkTP/74o3V4oSWCIMBoNLpyKSKSIF2g/Ccp1hpM1lLSlh0sieTApYDw+OOPY/v27YiMjMSoUaMQGBjYXu0iIhnQWocY5BsQLMML/koFAlTtuv8dkUe5FBB27NiBvn37Yu/evQgJCWmvNhGRTPjCKgZLlUhNoAqCwK2eST5cirsmkwnJyckMB0TUIl3z7oaVdY2XHYaUMk5QJLlyKSCMHz8eOTk5MJlM7dUeIpIRyxwEg8mM+kZ5/p2oZA0EkimXAsLSpUtRW1uLO++8E0ePHm2vNhGRTAT5K+GnaOp2l+swA/dhILlyKSBotVr0798fmZmZiI+Ph1KpbPHHz69dVlMSkcQIgiD7csuV3MmRZMrlVQzffPMNgoODMWjQIK5iICI72kAVztUYZBsQLCs0NAwIJDMuBYTMzExERkZi9+7diIiIaK82EZGMyH0lAycpkly5NMSgUCgwfvx4hgMiapV1JYNMyy1zDgLJlcvbPf/666+yXb5ERK6TezVFrmIguXIpILz99ttQKpW4++67UVBQ0F5tIiIZ0XCIgUiSXAoI119/PcrLy7F27Vr069cPKpUK/v7+dj9qtbpdGpuVlYXExEQEBgaiT58+WLJkiVOPf/XVV1nUicjNdDIvt2zZqZI9CCQ3Lk1S7Nq1KwRBcMu2ztnZ2Zg0aRImT56M9PR07Ny5E8899xz8/PwwZ86cKz5+7969mD9/Pvz9uZkKkTtZhhgqOcRAJCkuBYRt27a1UzOuLD09Hf3798fatWshCAKmTJmC8vJyLFq0CLNnz4ZSqWz1sQ0NDZgxYwYCAwNZ9ZHIzSwbNslxiMFkFlGtb9qpVsudHElmJLH1mMFgwNatWzF9+nSbzVBmzJiB0tJS7Nmz57KPf+GFFyCKIh555JGObioRXcKyBXKFDFcxVDc0wjJHmz0IJDeSCAj5+fkwGAwYMGCAzXHL7cOHD7f62KysLLz//vv49NNPERAQ0KHtJCJ7WhlXUrT0igT5K+HvJ4k/p0QOc+o3WqlU2oz3KxSKVssrt2ep5fLycgBAp06dbI7rdDoAQEVFRYuPq6iowH333Yd58+ZhyJAhDl9Pr9ejqqrK5sdoNLap7US+Ts6FklgDgeTMqU/umTNnYtiwYdbb99xzj1v2P7fUWTCbzQ4dt3jkkUcQFRWFF154wanrLV68GBkZGTbHUlNTkZube9m5DkRkz2A04a9xJgAm7Pr5Zyjc8DfDXcpq9PhrnAmhaj2ys7M93Rwihzg6F8+pgLBy5coWb1dVVaGmpgZBQUHWb/XtydJzcGlPgaVnoaVrZmZmYv369di1axfq6+sBNM1lAICamhr4+fm1OuTw4osv4umnn7be1uv1ePvtt5GUlNRuSzaJfIXRZMbMDZsBAI/eMQSdg+UzmW/Db8VYsWUvhvfphDnDh3u6OUQO0ev12LJlyxXPa9OgmdFoxEcffYTx48dDp9OhU6dOiIqKQpcuXaDT6XDjjTfi448/brdu+b59+0KlUiEvL8/muOV2fHy83WN+++03NDQ0IDExEaGhoQgNDcXixYtRW1uL0NBQpKWltXo9tVoNjUZj88MdKYnaxk+pQIi66d+P3IYZKjjEQDLm9KfesWPHcOuttyIvLw+iKKJTp05ITExEUFAQ6urqcPz4cWzZsgXfffcd3nzzTWRmZqJfv34uNVKtViMlJQWrV6/GU089BYWiKdesXr0aERERSEpKsnvMAw88gPHjx9sc++ijj7B27Vp8++236NKli0ttIiLHaQNVqNEbUVFnABDs6ea0myoGBJIxpwJCeXk5UlJSUFxcjHvuuQdPP/00Bg0aZHfe/v378eabb2LlypW44YYbsHfvXnTu3Nmlhs6bNw8pKSlIS0tDWloasrOzsWLFCixfvtw6L2Dfvn0oKirCmDFj0LNnT7sCTt999x0UCgVGjRrlUluIyDm6IBWKKuplV02RZZZJzpwaYli8eDGKi4vx2muvYeXKlS2GAwC45ppr8NFHH+GNN95AYWEh3njjDZcbOmrUKGRmZuLw4cOYPn06vvjiCyxbtgwPPvig9ZwlS5bg5ptvRklJicvXI6L2Y/mGXSWzgNDUI8IeBJInpwLC119/jejoaDz//PMOnf/ss88iOjoa69evb0vb7KSmplrnFuTn5+PRRx+1uX/lypUQRRG9e/du8fHz589HTU1Nu7SFiBynk2ktBOsyxyD5TLwksnAqIJw4cQLXX3+9Uxe4/vrrcfz4caceQ0TyYilDLNuAwB4EkiGnAoJer4dWq3XqAhqNBo2N8vqjQETOkWuxpApu1EQyxtqgRNThLmz5LK/9GCxzKnQMCCRDDAhE1OHkuuUz6yCQnDldByEnJwcLFixw6nwi8m1yHGJoNJlRZ2gqWcuAQHLUpoDg7Ie+O/ZrICLvZd3RUUYB4eKwo2FAIBlyKiB8/PHHHdUOIpIxnQxXMVheS2iAH5QKfgki+XEqINx7770d1Q4ikjFLD0JVfSNEUZRFryKrKJLccZIiEXU4yyRFg8mM+kbHtpr1dtyHgeSOAYGIOlyQvxIqZVOvgVyGGSxLNhkQSK4YEIiowwmCILuVDJYlm5b5FURyw4BARG5hCQhy6UGorDcC4AoGki8GBCJyC13zhkaVMqmmyCEGkjsGBCJyC9kNMXAVA8kcAwIRuYVObkMM3KiJZI4BgYjcQm7VFLnVM8kdAwIRuYVshxgYEEimGBCIyC3ktqOjpSeEqxhIrhgQiMgtLKsYKmSyioGTFEnuGBCIyC3kNMTQ0GiCwWgGwDkIJF8MCETkFtZJijIYYrC8BqVCQIjaqT3viCSDAYGI3EJOcxAuXsEgh50piVrCgEBEbmHpiq/WG2E0mT3cGtdU1LGKIskfAwIRucXFH6ZVDUYPtsR1lVzBQD6AAYGI3MJPqUBo83i95Ru4VLEGAvkCBgQichuNTFYysIoi+QIGBCJyG51Myi2zBgL5AgYEInIbyweq1FcysAeBfAEDAhG5jVyKJVVwJ0fyAQwIROQ22sDmcsvsQSDyegwIROQ2F+YgSHsVQwUDAvkABgQichu5DDFUMSCQD2BAICK3kUu55QurGPw93BKijsOAQERuI4dljqIocg4C+QQGBCJyGzkUSqrRG2EyiwBYB4HkjQGBiNxGJ4NVDJZw4++nQIBK6eHWEHUcBgQichtroaR6A0RR9HBr2oY1EMhXMCAQkdtYPlQbTSLqG00ebk3bVHGjJvIRDAhE5DZB/kqolAIA6Q4zsAYC+QoGBCJyG0EQJF9NkSsYyFcwIBCRW2kD/QBIdyWDNSBwBQPJHAMCEbmVpbhQpUTLLXOSIvkKBgQicivL5D6pDzFYlmwSyRUDAhG5ldT3Y7iwD4Ofh1tC1LEYEIjIrbQSL7ds2YmScxBI7hgQiMitpF5NkUMM5CsYEIjIrSxd81VS7UFoDjYaTlIkmWNAICK3sqxiqJDoKgbWQSBfwYBARG5lnYMgwSEGk1lEdYMRAHdyJPmTVEDIyspCYmIiAgMD0adPHyxZsuSKj/n73/+OuLg4qNVqREdHY86cOaisrHRDa4moJVJexXDxsAh7EEjuJBMQsrOzMWnSJMTGxmLNmjWYNm0annvuOSxdurTVxyxZsgRPPfUUhgwZgk8++QSPPvooPv30U6SkpKCxUXp/nIjkwFIHoVKCPQiWUBPsr4RKKZk/n0RtIpmFvOnp6ejfvz/Wrl0LQRAwZcoUlJeXY9GiRZg9ezaUSvt92ZcuXYqUlBSsWbPGeiwxMRHjx4/HV199halTp7rzJRARLsxBqNYbYTSZ4SehD1rOPyBfIol/mQaDAVu3bsX06dMhCIL1+IwZM1BaWoo9e/bYPaaiogKFhYW49dZbbY6PHTsWSqUSx44d6/B2E5E9TcCF7yVVzeP5UmGp3cAVDOQLJBEQ8vPzYTAYMGDAAJvjltuHDx+2e0xwcDB27dqFGTNm2Bzfu3cvTCYTevXq1XENJqJW+SkVCFU3hYSKOmmtZLDWQOAERfIBkhhiKC8vBwB06tTJ5rhOpwPQ1FtwKZVKheuuu87m2IEDB3DnnXciKioKU6ZMafV6er0eer3e5rbRKK1vOkTeTBukQrXeKLlqipXNgYZDDOQLJBEQRFEEAJjNZoeOX6qiogIZGRl499130b17d2zevBmBgYGtnr948WJkZGTYHEtNTUVubm6Lcx2IyDl39GxAVVcTTh/dh+zTak83x2Gqshr8Nc6E7iHnkZ2d7enmELWJyWRy6DxJBARLz8GlPQWWngVLT0JLNm/ejPvvvx/nz5/HY489hgULFiA0NPSy13vxxRfx9NNPW2/r9Xq8/fbbSEpKglotnT9mRN7q7X0/46djZfh7Ql8MT+zu6eY47Lv/5GHF4QI8ODoK9w+/2tPNIWoTvV6PLVu2XPE8ScxB6Nu3L1QqFfLy8myOW27Hx8e3+LjPP/8cqampiI2Nxf79+/HWW29dMRwAgFqthkajsfnx85NEliKShAv7MUhrDoKluBOHGMgXSCIgqNVqpKSkYPXq1TbDCatXr0ZERASSkpLsHmMymfDkk08iOTkZWVlZiI2NdWeTiegyNNZiSdKa28NljuRLJPO1eN68eUhJSUFaWhrS0tKQnZ2NFStWYPny5dZ5Afv27UNRURHGjBmD3377DcXFxbjvvvuQlZVl93z9+vVDv3793P0yiAgXVgFIbT8GBgTyJZIJCKNGjUJmZibmzp2L6dOno3v37li2bBkefPBB6zlLlizBqlWrUFBQgKKiIgDAq6++2uLzpaenY/78+e5oOhFdQqrVFBkQyJdIJiAATSsJUlNTW71/5cqVWLlyJQCgd+/e1lUORORdpLofA+sgkC+RxBwEIpKXC0MM0goInKRIvoQBgYjcTivBVQwGoxn1jU3rxxkQyBcwIBCR22kluIrBMrwgCEBoAAMCyR8DAhG5nWWIobLeIJm5QpXNKy5C1X5QKoQrnE0kfQwIROR2loDQaBJRZ3Cs7KunWVcwcIIi+QgGBCJyu0CVEipl07dwqaxksK5gaJ4/QSR3DAhE5HaCIFw0UVEaAYErGMjXMCAQkUdIrZoihxjI1zAgEJFHWL6JV0lkiIE9CORrGBCIyCMs5ZalMsTAMsvkaxgQiMgjtBKrplhlnaTIgEC+gQGBiDxCavsxVLAHgXwMAwIReYROYqsYOMRAvoYBgYg8QhvYtJlsJVcxEHklBgQi8ghdUFMPgmSGGLiKgXwMAwIReYR1kqIEhhhEUbwwSTGIlRTJNzAgEJFHaCW0zLG+0QSDyQyAPQjkOxgQiMgjdBIqlGQZBlEqBAT7Kz3cGiL3YEAgIo+wdNVX641obP527q0qL6qBIAjc6pl8AwMCEXmEJsDP+t/e3ovACYrkixgQiMgj/JQKhKotSx29OyBY2qdhQCAfwoBARB4jlXLL1iEG1kAgH8KAQEQeYy237OUrGSo5xEA+iAGBiDzG8o1cKkMM3KiJfAkDAhF5zIX9GLy73HJFczlo9iCQL2FAICKPsUz68/45CEYAnKRIvoUBgYg8RnJDDCyzTD7E78qnEBF1DMuY/pa8EqiUCsRFhCKuWyj6dQ1BgMo7KhbW6o0ordYD4BAD+RYGBCLymNhuoQCAU+X1+L/tf1iPKxUCencJQnw3DeK6NYWGuIhQ9OwcBIWi/SsZNprMKDxfh4JztSg4V4v80loUnKtBwblalFTpredxmSP5EgYEIvKYsXFdsfHxUfjtVAUOn6lu+impRkVdI/JLmz6o/7PvtPX8QJUSsREhzaFBg/jm8BAWor7itURRREmVHn80f/AXlDaFgT/O1eLk+TqYzGKrj+0S7I9re3fC4B669njZRJLAgEBEHnVNdy2u6a613hZFEWer9Th0phqHz1Th0JlqHCmpxtGSGtQ3mvDbqUr8dqrS5jm6BPtbexriu4WiZ+dgnKmqR0FpUwCw9AzUGUyttiNQpUSfsGD0CQ9GdFgw+oQFIzo8BH26BFsLOhH5EgYEIvIqgiAgQhOACE0AxsSGW4+bzCKOl9Xi8Jlqa3g4UlKD42W1KKs1YGd+GXbml132uZUKAT07BzUFAWsICEZ0WAgiNGpuxER0EQYEIpIEpUJA3/AQ9A0PwS0Dr7IerzeYcPSsJTQ09TYUnq9DN20A+oSFXNQbEIyozkFQKbl4i8gRDAhEJGmB/koM6qHDIM4PIGpXjNJERERkhwGBiIiI7DAgEBERkR0GBCIiIrLDgEBERER2GBCIiIjIDgMCERER2WFAICIiIjsMCERERGSHAYGIiIjsMCAQERGRHQYEIiIissOAQERERHa4m6MDRFEEAOj1eg+3hIiIyDWWzzLLZ1trGBAcYDAYAABvvfWWh1viWTk5ORg2bJinmyFLfG/bF9/P9sH3se2k8N4ZDAYEBAS0er8gXilCEMxmM2pqauDv7w9BEDzdHI9JSEjAr7/+6ulmyBLf2/bF97N98H1sO29+70RRhMFgQEhICBSK1mcasAfBAQqFAhqNxtPN8DhBEKBWqz3dDFnie9u++H62D76Pbeft793leg4sOEmRHPbYY495ugmyxfe2ffH9bB98H9tODu8dhxiIiIjIDnsQiIiIyA4DAhEREdlhQCCrTZs2QRAEu59+/frZnHfs2DFMnDgROp0OYWFhmD59Ok6fPu2hVkuDo+/tihUrMHDgQAQHByMmJgYLFixg/Y3LcOR3MSsrC4mJiQgMDESfPn2wZMkSD7XWOznyHv79739HXFwc1Go1oqOjMWfOHFRWVnqoxd7D2b+Fr776KkJCQtzYQtdwFQNZ5efnQ6vVYu3atTbHg4ODrf9dVFSEcePGITo6Gh988AFqamrwyiuvYOLEidi9e/dll8z4Mkfe23feeQdz5szBU089hVGjRuHAgQNYvHgxTp06hf/7v/9zd5O9niO/i9nZ2Zg0aRImT56M9PR07Ny5E8899xz8/PwwZ84cT78Ej3PkPVyyZAmeffZZpKWlYeHChTh58iQWL16M7du3IycnByqVytMvwyOc/Vu4d+9ezJ8/H/7+/h5qcRuIRM2eeOIJMSkp6bLnzJo1S+zZs6dYX19vPZaTkyMOHz5cPHjwYEc3UbIceW/79+8v3nXXXTbHFi5cKKpUKrGhoaEjmydJjvwu3njjjeKQIUNEs9lsPeevf/2rGB4eLhqNRre32ds48h5GRUWJKSkpNo/77rvvRADi559/7tb2ehNn/hbW19eL/fv3FzUajRgcHOzuprYZv+6RVX5+vrXL22g0tnjOunXrMHPmTAQEBMBkMsFsNmPo0KH4+eefER8f787mSooj7+358+fRuXNnm2NhYWFobGxEbW1th7dRaq70u2gwGLB161ZMnz7dpsDZjBkzUFpaij179niw9d7hSu9hRUUFCgsLceutt9o8buzYsVAqlTh27JiHWu55zvwtfOGFFyCKIh555BEPtbZtGBDIKj8/H0VFRUhISIBarYZGo8GsWbNQVVUFACgsLERZWRkCAwMxceJEBAQEICAgAKmpqSgsLPRw673bld5bAJg5cyZWrVqFzMxMlJaWYvv27Xj99dcxdepUu+Dg6xz5XczPz4fBYMCAAQNsHmu5ffjwYbe325s48h4GBwdj165dmDFjhs1j9+7dC5PJhF69enmi6R7nzN/CrKwsvP/++/j0008dKk7kVTzdhUHewWQyiWq1WgwPDxeXLFkibt68WczIyBADAgKs3YvZ2dkiANHf31+cOnWq+PXXX4sfffSRGBkZKcbExNh0tdEFjry3oiiKRqNRTElJEQFYf+Li4sSqqioPtt47OfK7+NNPP4kAxJ9//tnmsY2NjSIA8Z133vFQ671DW/8979+/X+zbt68YFRUl1tXVubnV3sHR9668vFzs0aOH+Morr4iiKIrp6emSGmJgQCBRFEVRr9eLn3/+uZifn29z/IMPPhABiD/88IO4Y8cOEYA4duxYm3N27twpAhBXrVrlziZLhiPvrSiK4r333itqNBrxtddeE//73/+K77zzjtitWzdx5MiRosFg8ETTvZYjv4s//vijCEDcuXOnzTkGg0EEIC5dutSdTfY6zv57Li8vF5988klRpVKJvXv3Fvfv3+/O5noVR9+7tLQ0ccSIEdb5LlILCBxiIACAv78/pk6diujoaJvjlrHH33//3TrjfvLkyTbnjBgxAhqNBr///rt7Gisxjry3u3fvxqpVq/DBBx/gf/7nfzBhwgTMnj0b69atw86dO7F+/XoPtNx7OfK72KlTJwBARUWFzTnl5eUAAJ1O1+Ht9GbO/HvevHkz4uPj8e677+Kxxx7D77//bjd040scee8yMzOxfv16vPfee6ivr0dNTY11Z+Camho0NDS4vd3OYkAgAEBxcTF+/PFHmM1mm+NKpRIAEBISgr59+0IQhBbX5ZvNZpsle3SBI+9tfn4+gKY/MBcbNmwYFAqF9X5q4sjvYt++faFSqZCXl2dzv+W2r0+qdfTf8+eff47U1FTExsZi//79eOuttxAaGuru5noVR9673377DQ0NDUhMTERoaChCQ0OxePFi1NbWIjQ0FGlpaR5ouXMYEAgAsH//fiQnJyMzM9PmeGZmJgRBQEpKCjQaDYYPH47PPvvM5sNu06ZNqKmpwZgxY9zdbElw5L2Ni4sDAPzwww825+zYsQNmsxmxsbFua68UOPK7qFarkZKSgtWrV9ucs3r1akRERCApKckTTfcajryHJpMJTz75JJKTk5GVlcXfw2aOvHcPPPAAduzYYfNz//33IzAwEDt27MDixYs9+Aoc5OkxDvIOZrNZHD58uKjVasXFixeLX375pfjSSy+JAQEB4lNPPWU975tvvhEFQRD/9Kc/iatXrxb/93//V9RqteItt9ziwdZ7N0ff2z//+c9icHCwOG/ePHHdunXia6+9Jnbp0kUcOnSo2NjY6MFX4J0c+V3csWOHqFQqxWnTpolffvml+Pzzz4uCIIgffPCBB1vuPa70Hu7atUsEIL700kvi5s2b7X6OHj3q4VfgOW35Wyi1OQgMCGRVXl4uPvLII2LPnj1FtVotxsfHi0uXLrUpMiOKovjll1+KgwcPFtVqtdi9e3fx+eefZyGfK3Dkva2vrxfnzp0rRkdHi2q1WoyJiRFfeOEFsaamxoMt926O/C5+/fXX4qBBg0S1Wi1GR0eL7777roda650u9x5+8cUXNqtqLv1JT0/3bOM9zNm/hVILCNzumYiIiOxwDgIRERHZYUAgIiIiOwwIREREZIcBgYiIiOwwIBAREZEdBgQiIiKyw4BAREREdhgQiIiIyA4DAhEREdlhQCAiIiI7DAhERERkhwGBiIiI7DAgEHmJlStXQhCEK/6sXLnS002VrZSUFOv7rNPp3HbdJ5980ub/4+PHj7vt2kSt8fN0A4jI1tChQzFs2LBW77/66qvd2BrfExoainvuuQdBQUHWY4IgAAAut/lt7969ceLECRQUFKB3795OXTM5ORlGoxE5OTn45Zdf2tRuovbGgEDkZW655RbMnz/f083wWZ07d8ayZcvces3bb78dt99+O+bPn8+AQF6DQwxERERkhwGBSIJSUlIQHx+PyspK3HnnnQgKCsL69eut9585cwYPP/wwevToAbVajaioKDz66KMoLS21e64//vgDd911F8LCwhAUFITrrrsOGzduxP/+7/9au9Yt12yt61wQBNx33302xxxpg+V1NDY24qWXXkKPHj0QHByMpKQkfP3113bXOXnyJO655x5EREQgJCQECQkJWLZsGYxGIwDgnnvugSAIKCgosHvsvffeC0EQcPDgwcu9te2qd+/el51Pcul7RuRNOMRAJFEmkwlTpkzB/v37MWHCBPTp0wcAUFhYiOuvvx6nTp3C2LFj8ac//Qm//fYb3n//fWRlZeGXX36BRqMBAOTn52PEiBEoLS3FmDFjEBMTg19++QWTJ0/Gdddd1+a2OdMGoOnDOysrC+PGjUNtbS3+85//4LbbbsOOHTswYsQIAEBBQYG1rTfddBPCw8Oxbds2PP744zh69CiWLl2KO+64A5988gm++OILPPfcc9bnb2hoQGZmJq699lq3zuF48sknUVFRYXd8w4YN2LNnj808ByJvw4BA5GU2bdqEc+fOtXjf1KlTkZKSAgA4duwY+vbti/z8fJsP29mzZ6OkpATff/+99VwAeOutt/D0009jyZIlyMjIAAA8/vjjKC0txYcffoi//vWvAJom4j3yyCP44IMP2vwanGnDsWPHoFKpsH//foSHhwMAPv30U8ycORPr1q2zBgTLc3799ddITU0F0PTBP2bMGLz99tt45plnMGHCBGi1Wnz55Zc2AWHDhg2orq7GzJkz2/yaAFx2bkhLQeDJJ5+0O7ZlyxYsWrQI/fr1w4IFC1xqD1GHEonIK3z88ccigMv+vPXWW6IoiuKYMWNEAGJ2drbNc5w+fVoUBEGcNWtWi9dITEwU4+PjRVEUxeLiYlEQBHHEiBF255WVlYnBwcHixX8ixowZI/bq1avF5wUg3nvvvU63wfI6NmzYYHNOXV2dzXNa2jp27Fi759uwYYM4fPhwMSsrSxRFUZw5c6YoCIJ48uRJ6zlTpkwR/fz8xJKSkhbbdKXXeKX/Xy7+KSgoaPX59+3bJ2o0GrFLly7ikSNH7O5PT0+/4nMQuQt7EIi8THp6usOrGAYPHmxzOzc3F6Io4sCBA5g9e7bd+TU1NcjPz0dDQwP27NkDURRx8803253XuXNnJCUlYfv27U6335k2WIwcOdLmnMDAQJvblrbecMMNds83adIkTJo0yXp72rRp+OSTT7Bu3TrMmTMHlZWV2Lx5M2688UZ07drV6ddzMdGBZY6tOX36NCZOnAi9Xo+NGzciJibGpbYQdTQGBCKJUiqVUKvVNscs3dw7d+7Ezp07W31seXk5ysvLAQBXXXVVi+e0dvxKnGkD0PQ6OnfufNnntJzbrVu3K15/woQJ0Gg0+OKLLzBnzhx88cUX0Ov1Lg8vuKK2thaTJk1CYWEh/vWvfyE5OdljbSFyFFcxEMlISEgIAGDp0qUQRbHVn6uuugrBwcEAmr7ZtqSkpMSha549e7bNbXD2dbU2N+NiarUakydPxs6dO3HmzBmsXr0aGo0GkydPdvh67clsNuOuu+7Cnj178Morr+Cuu+7ySDuInMWAQCQjCQkJAIBdu3a1eP+8efPwxhtvAAAGDBgAAPjmm2/szisvL0dubq7d8YuHBSwu7SVwpg2OGjRoEAC0OOSxefNm+Pn54d1337UemzZtGsxmM9577z1s27YNU6dOtRu2cJcnn3wSGzZswAMPPICXXnrJI20gagsGBCIZ6dWrF0aPHo1///vf2LBhg819H3zwARYuXGgdAoiNjcWQIUOwc+dOfPzxx9bzRFHECy+8gOrqapvHd+nSBSUlJTYf0mfOnLH70HOmDY6Kjo7GyJEjsWnTJvz3v/+1HjcYDHjttddgNpsxduxY6/Ebb7wRGo0Gixcvhtls9tjwwt///ne88847uOGGG7B8+XKPtIGorTgHgUhmli9fjuTkZEyePBmjR49Gv379cOjQIezcuRMDBgzA//zP/1jPfe+99zB27Fg88MAD+Oc//4mYmBjk5OQgLy8PAwYMwIEDB6znTps2DevWrcOECRMwadIkKJVKZGVlYejQodZCRW1pg7Ova+LEibjpppvQtWtXbN++Hfn5+ZgzZw769+9vPVetViM1NRX/+te/EBUVhTFjxrThnXTNmTNn8Mwzz0AQBAwcOBCLFi2yOychIQFTpkxxe9uIHOLeRRNE1BrLMsf09PQrnjtmzBhRqVS2en9+fr44c+ZMMSIiQlSr1WJsbKz4/PPPi2VlZXbn5ubmirfccouo0WjEwMBAccSIEeL3338v3nvvveKlfyJWrFghDhgwQAwICBAjIyPFZ555Rqyvrxfj4uKsSxKdacPlXgcuWuZocfDgQXHatGli586dRZVKJcbGxoqvv/66aDQa7R7/6aefigDEF198sdX36VJXWuZ4Ob169bJZolhQUHDFJZGXvj4ucyRvIojiZdbtEJHPuu+++7Bq1arLLu3zZg8++CA+/PBD5OXlOVw9MSUlBcePH/fYdsvz589HRkZGm3aEJGpvnINARLJTVlaGtWvXIikpidtjE7UR5yAQkWycPn0a999/P/bt24fq6mo8//zzTj/H+fPnMXv2bAQFBTm92qKtvvzyS2zduhU5OTluuR6RIxgQiEg2BEHA7t27IQgCFi9ejDvuuMPp56iursa7774LrVbrtoCwY8cOm2WaRN6AcxCIiIjIDucgEBERkR0GBCIiIrLDgEBERER2GBCIiIjIDgMCERER2WFAICIiIjsMCERERGSHAYGIiIjsMCAQERGRHQYEIiIissOAQERERHb+P3pRZdtC1UPVAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def plot_predictions(clean, raw, start=55, stop=65):\n", + " raw = raw[:len(clean)]\n", + " raw = TimeSeries(raw, sample_rate=sample_rate)\n", + " clean = TimeSeries(clean, sample_rate=sample_rate)\n", + "\n", + " raw_asd = raw.asd(fftlength=2, method=\"median\", window=\"hann\")\n", + " clean_asd = clean.asd(fftlength=2, method=\"median\", window=\"hann\")\n", + " raw_asd = raw_asd.crop(start, stop)\n", + " clean_asd = clean_asd.crop(start, stop)\n", + " ratio = clean_asd / raw_asd\n", + " ratio.plot()\n", + " plt.show()\n", + "\n", + "\n", + "def plot_timeseries(start=55, stop=65, **timeseries):\n", + " for label, ts in timeseries.items():\n", + " ts = TimeSeries(ts, sample_rate=sample_rate)\n", + " asd = ts.asd(fftlength=2, method=\"median\", window=\"hann\")\n", + " plt.plot(asd.crop(start, stop), label=label)\n", + " plt.legend()\n", + " plt.yscale(\"log\")\n", + " plt.show()\n", + "\n", + "\n", + "plot_predictions(clean, y)\n", + "plot_timeseries(\n", + " noise_predictions=predictions,\n", + " raw=y,\n", + " clean=clean\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "8b8a7582", + "metadata": {}, + "source": [ + "## Offline loading, online predictions, offline clean\n", + "Ok, now let's load data offline, but build noise predictions online before doing cleaning offline." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "e69e8e9b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "state = get_state()\n", + "predictions = []\n", + "for i, y_hat in enumerate(do_inference(offline_it())):\n", + " frame = state.update(y_hat, i)\n", + " if frame is not None:\n", + " predictions.append(frame)\n", + "predictions = np.concatenate(predictions)\n", + "clean = y[:len(predictions)] - predictions\n", + "plot_predictions(clean, y)\n", + "plot_timeseries(\n", + " noise_predictions=predictions,\n", + " raw=y,\n", + " clean=clean\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "3d0d6990", + "metadata": {}, + "source": [ + "## Offline loading, online cleaning\n", + "Ok, now let's do offline data loading with full online cleaning machinery. To do this, we'll need to monkeypatch a few utility methods." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "c8e9e234", + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "import time\n", + "from contextlib import contextmanager\n", + "from unittest.mock import patch\n", + "\n", + "from cleaner import writer as writer_utils\n", + "\n", + "\n", + "class Predictions:\n", + " def __init__(self):\n", + " self.value = []\n", + "\n", + " def write_patch(self):\n", + " def patch(obj, _):\n", + " key = f\"{channels[0]}_DEEPCLEAN_CANARY\"\n", + " value = obj[key].value\n", + " self.value.append(value)\n", + " return patch\n", + "\n", + " def finalize(self):\n", + " return np.concatenate(self.value)\n", + "\n", + "\n", + "def make_request_patch(_, __):\n", + " return 2\n", + "\n", + "\n", + "def getmtime_patch(_):\n", + " return time.time()\n", + "\n", + "\n", + "@contextmanager\n", + "def dummy(predictions):\n", + " mod = \"cleaner.writer\"\n", + " patch1 = patch(f\"{mod}.TimeSeriesDict.write\", new=predictions.write_patch())\n", + " patch2 = patch(\n", + " f\"{mod}.ExportClient._make_request\",\n", + " new=make_request_patch\n", + " )\n", + " patch3 = patch(f\"{mod}.os.path.getmtime\", new=getmtime_patch)\n", + " with patch1, patch2, patch3:\n", + " yield\n", + "\n", + "\n", + "def get_monitor_and_writer(strain_it, level=logging.WARNING):\n", + " utils = reload(writer_utils)\n", + " monitor = utils.ASDRMonitor(\n", + " buffer_length=8,\n", + " freq_low=58,\n", + " freq_high=62,\n", + " fftlength=2\n", + " )\n", + " with dummy(predictions):\n", + " writer = utils.Writer(\n", + " Path(\".\"),\n", + " strain_generator=strain_it,\n", + " monitor=monitor,\n", + " export_endpoint=\"\",\n", + " )\n", + " writer.logger.setLevel(level)\n", + " return monitor, writer" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "3af2bf1c", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def strain_it():\n", + " strains = np.split(y, test_duration)\n", + " for i, frame in enumerate(strains):\n", + " ts = TimeSeries(\n", + " frame,\n", + " sample_rate=sample_rate,\n", + " channel=channels[0],\n", + " t0=t0 + i\n", + " )\n", + " fname = f\"{ifo}-llhoft-{test_start + i}-{1}.gwf\"\n", + " yield ts, Path(fname)\n", + "\n", + "\n", + "predictions = Predictions()\n", + "state = get_state()\n", + "monitor, writer = get_monitor_and_writer(strain_it())\n", + "\n", + "for i, y_hat in enumerate(do_inference(offline_it())):\n", + " frame = state.update(y_hat, i)\n", + " if frame is not None:\n", + " with dummy(predictions):\n", + " writer(**{\"deepclean-canary\": frame})\n", + "predictions = predictions.finalize()\n", + "plot_predictions(predictions, y)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "29eb38c9", + "metadata": {}, + "source": [ + "## Fully online\n", + "Finally, let's recreate the entire clean-time workflow in-memory" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "2009faa4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cleaner.dataloader import get_data_generators\n", + "\n", + "def online_it(witness_it, N=None):\n", + " i = 0\n", + " while True:\n", + " if N is not None and i >= N:\n", + " break\n", + " try:\n", + " yield next(witness_it)[0]\n", + " except StopIteration:\n", + " break\n", + " i += 1\n", + "\n", + "predictions = Predictions()\n", + "witness_it, strain_it = get_data_generators(\n", + " run_dir / \"live-data\",\n", + " ifo,\n", + " channels,\n", + " sample_rate=sample_rate,\n", + " inference_sampling_rate=inference_sampling_rate,\n", + " batch_size=infer_batch_size,\n", + " t0=t0,\n", + " timeout=0,\n", + ")\n", + "\n", + "state = get_state()\n", + "monitor, writer = get_monitor_and_writer([])\n", + "it = online_it(witness_it, N=128)\n", + "strains = []\n", + "for i, y_hat in enumerate(do_inference(it)):\n", + " frame = state.update(y_hat, i)\n", + " if frame is not None:\n", + " strain, fname = next(strain_it)\n", + " strains.append(strain)\n", + " writer.strain_generator = iter([(strain, fname)])\n", + " with dummy(predictions):\n", + " writer(**{\"deepclean-canary\": frame})\n", + "predictions = predictions.finalize()\n", + "strains = np.concatenate([i.value for i in strains])\n", + "plot_predictions(predictions, strains)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/projects/microservice/sandbox/sandbox.py b/projects/microservice/sandbox/sandbox.py new file mode 100644 index 0000000..e69de29