Releases: arrayfire/arrayfire-python
Releases · arrayfire/arrayfire-python
Python wrapper for ArrayFire v3.8
New Features/Functions
- 
fp16- half precision floating point support has been added - #221
- 
Confidence Connected Components confidenceCC- #221
- 
Deconvolutions - #221 
- 
Reduction using keys - #221 
- 
Neural network based convolution and gradient functions - #221 
- 
Support for uniform ranges in approx1 and approx2 functions - #234 
- 
Array class methods - #233 
- 
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
- 
Feature parity with ArrayFire 3.5. - canny: Canny Edge detector
- Array.scalar: Return the first element of the array
- dot: Now support option to return scalar
- print_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.
 
- 
Bug Fixes: - sort_by_keyhad key and value flipped in documentation.
 
- 
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_sparse
- create_sparse_from_dense
- create_sparse_from_host
- convert_sparse_to_dense
- convert_sparse
- sparse_get_info
- sparse_get_nnz
- sparse_get_values
- sparse_get_row_idx
- sparse_get_col_idx
- sparse_get_storage
 
- Random Engine support
- Three new random engines, RANDOM_ENGINE.PHILOX,RANDOM_ENGINE.THREEFRY, andRANDOM_ENGINE.MERSENNE.
- randuand- randnnow accept an additional engine parameter.
- set_default_random_engine_type
- get_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