Releases: arrayfire/arrayfire-python
Releases · arrayfire/arrayfire-python
Python wrapper for ArrayFire v3.8
New Features/Functions
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fp16- half precision floating point support has been added - #221 -
Confidence Connected Components
confidenceCC- #221 -
Deconvolutions - #221
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Reduction using keys - #221
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Neural network based convolution and gradient functions - #221
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Support for uniform ranges in approx1 and approx2 functions - #234
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Array class methods - #233
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New Examples
Breaking APIs
Fixes
- Fixed wrapper validations in
create_sparse_from_host- #198 - Added a workaround for
bench_cgexample on less capable GPUs - #200 - Fixed missing info in
Array.device_ptrfunction documentation - #210 - Corrected invert operation to use non-in-place bit wise inversion - #228
Python wrapper for ArrayFire v3.6
- Feature parity with ArrayFire v3.6. Refer to the release notes for more information regarding upstream library improvements in v3.6.
anisotropic_diffusion(): Anisotropic diffusion filter.topk(): Returns top-K elements given an array.
- Bug fixes:
- Fixed
sift()andgloh(), which were improperly calling the library.
- Fixed
- Enhancements:
- Added
len()method, which returnsarray.elements().
- Added
- Documentation:
- Documented statistics API.
- Corrected
sign()documentation. - Modified
helloworldexample to match C++ lib.
Second bugfix release for 3.5
- Bug fixes when using v3.5 of arrayfire libs + graphics
First bugfix release for 3.5
Includes fix for arrayfire.canny.
Python wrapper for ArrayFire 3.5
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Feature parity with ArrayFire 3.5.
canny: Canny Edge detectorArray.scalar: Return the first element of the arraydot: Now support option to return scalarprint_mem_info: Prints memory being used / locked by arrayfire memory manager.Array.allocated: Returs the amount of memory allocated for the given buffer.set_fft_plan_cache_size: Sets the size of the fft plan cache.
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Bug Fixes:
sort_by_keyhad key and value flipped in documentation.
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Improvements and bugfixes from upstream include:
- CUDA backend uses nvrtc instead of nvvm
- Performance improvements to arrayfire.reorder
- Faster unified backend
- You can find more information at arrayfire's release notes
Second bugfix release for 3.4
- Bugfix: Fixes typo in
approx1. - Bugfix: Fixes typo in
hamming_matcherandnearest_neighbour. - Bugfix: Added necessary copy and lock mechanisms in interop.py.
- Example / Benchmark: New conjugate gradient benchmark.
- Feature: Added support to create arrayfire arrays from numba.
- Behavior change: af.print() only prints full arrays for smaller sizes.
First bugfix release for 3.4
- Fixing memory leak in array creation.
- Supporting 16 bit integer types in interop.
Python wrapper for arrayfire 3.4
- Feature parity with ArrayFire 3.4 libs
- Sparse matrix support
create_sparsecreate_sparse_from_densecreate_sparse_from_hostconvert_sparse_to_denseconvert_sparsesparse_get_infosparse_get_nnzsparse_get_valuessparse_get_row_idxsparse_get_col_idxsparse_get_storage
- Random Engine support
- Three new random engines,
RANDOM_ENGINE.PHILOX,RANDOM_ENGINE.THREEFRY, andRANDOM_ENGINE.MERSENNE. randuandrandnnow accept an additional engine parameter.set_default_random_engine_typeget_default_random_engine
- Three new random engines,
- New functions
- Behavior changes
evalnow supports fusing kernels.
- Graphics updates
plotupdated to take new parameters.plot2added.scatterupdated to take new parameters.scatter2added.vector_fieldadded.set_axes_limitsadded.
- Sparse matrix support
- Bug fixes
- Further Improvements from upstream can be read in the arrayfire release notes.
Fifth bugfix release for 3.3
- Adding 16 bit integer support
- Adding support for sphinx documentation
Fourth bugfix release for 3.3
v3.3.20160516
- Bugfix: Increase arrayfire's priority over numpy for mixed operations
- Added new library functions
get_backendreturns backend name