v1.0-beta.1

New features:

• Support for TensorFlow Decision Forests
• 'Unrolled' Xilinx HLS optimization for much faster C Synthesis time, enabled by default with Unroll configuration parameter (see performance plots on the PR)
• Synthesis report reading for HLS and VHDL backends: conifer_model.read_report() for models of those backends
• new_config parameter of conifer.model.load_model to override a saved model's configuration (e.g. to change backend or precision)
• Simulator discovery for VHDL backend (use whichever is installed)
• Model metadata saved with model JSON export for provenance tracking - conifer version, model conversion time
• Documentation webpages at https://ssummers.web.cern.ch/conifer/
• Significantly overhauled internal representation

Bug fixes:

• Fix to sklearn converter for newer sklearn versions

v0.4

New features:

• Model save/load functionality. model.save() to export a JSON file, conifer.model.load_model(‘my_prj.json’) to load a saved model. The JSON file can also be used for C++ evaluation.
• Better agreement of output predictions between VHDL backend and others using new FixedPointConverter module
• model.build returns success status

Bug fixes:

• Fix crash when writing project to existing directory

v0.2-beta.0

New features:

• ONNX converter
• Different data types for inputs/thresholds and scores
• GHDL simulation support for VHDL backend
• C++ backend for emulation
• logging messages

Bug fixes:

• Support named features in xgboost

v0.1

What's new:

• Make VHDL backend code compatible with Vivado xsim (remove VHDL 2008 usage)
• Add xsim option for VHDL backend. The simulator can be set with conifer.backends.vhdl.simulator = conifer.backends.vhdl.Simulators.{xsim, modelsim}

v0.0

First conifer release.
Available converters:

• sklearn
• xgboost
• TMVA

Available backends:

• vivadohls
• vitishls
• xilinxhls (use whichever Vitis/Vivado HLS is available)
• vhdl