AArch64 | |
ppc64le | |
s390x | |
x86-64 |
- Update to 1.4: * Performance improvements all over the board - Rebase patch cmake-no-install-ocl-cmake.patch
- Add constraints to not crash during testing on OOM
- Do not disable LTO there is no actual reason for that - Export LD_LIBRARY_PATH to fix older releases build
- There is no actual reason to not use github tag for tarball fetching -> remove the service - Format with spec-cleaner - Use proper %cmake macros everywhere - Add configure options for cmake to set it up in a way we really want - Add patch from Debian to not install OpenCL cmake finder: * cmake-no-install-ocl-cmake.patch
- enabled tests
- packaged separate benchnn packae with its input files - updated to v1.1.3 which includes * Fixed the mean and variance memory descriptors in layer normalization (65f1908) * Fixed the layer normalization formula (c176ceb)
- updated to v1.1.2 * Fixed threading over the spatial in bfloat16 batched normalization (017b6c9) * Fixed read past end-of-buffer error for int8 convolution (7d6f45e) * Fixed condition for dispatching optimized channel blocking in fp32 backward convolution on Intel Xeon Phi(TM) processor (846eba1) * Fixed fp32 backward convolution for shapes with spatial strides over the depth dimension (002e3ab) * Fixed softmax with zero sizes on GPU (936bff4) * Fixed int8 deconvolution with dilation when ih <= dh (3e3bacb) * Enabled back fp32 -> u8 reorder for RNN (a2c2507) * Fixed segmentation fault in bfloat16 backward convolution from kd_padding=0 computation (52d476c) * Fixed segmentation fault in bfloat16 forward convolution due to push/pop imbalance (4f6e3d5) * Fixed library version for OS X build (0d85005) * Fixed padding by channels in concat (a265c7d) * Added full text of third party licenses and copyright notices to LICENSE file (79f204c) * Added separate README for binary packages (28f4c96) * Fixed computing per-oc mask in RNN (ff3ffab) * Added workaround for number of cores calculation in Xbyak (301b088)
- added ARCH_OPT_FLAGS=""
- Initial checking of the Intel(R) Math Kernel Library for Deep Neural Networks which can be used by: * tensorflow * Caffee * PyTorch and other machine learning tools