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_cg
example on less capable GPUs - #200 - Fixed missing info in
Array.device_ptr
function 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
helloworld
example 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 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.
-
Bug Fixes:
sort_by_key
had 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_matcher
andnearest_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
. randu
andrandn
now accept an additional engine parameter.set_default_random_engine_type
get_default_random_engine
- Three new random engines,
- New functions
- Behavior changes
eval
now supports fusing kernels.
- Graphics updates
plot
updated to take new parameters.plot2
added.scatter
updated to take new parameters.scatter2
added.vector_field
added.set_axes_limits
added.
- 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_backend
returns backend name