Questo è il messaggio ricevuto dall'esecuzione di uno script per verificare se Tensorflow funziona:
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcudnn.so.5 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:125] successfully opened CUDA library libcurand.so.8.0 locally
W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
W tensorflow/core/platform/cpu_feature_guard.cc:95] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
Ho notato che ha menzionato SSE4.2 e AVX,
- Cosa sono SSE4.2 e AVX?
- In che modo questi SSE4.2 e AVX possono migliorare i calcoli della CPU per le attività di Tensorflow.
- Come compilare Tensorflow usando le due librerie?
NOTE on gcc 5 or later: the binary pip packages available on the TensorFlow website are built with gcc 4, which uses the older ABI. To make your build compatible with the older ABI, you need to add --cxxopt="-D_GLIBCXX_USE_CXX11_ABI=0" to your bazel build command. ABI compatibility allows custom ops built against the TensorFlow pip package to continue to work against your built package.
da qui tensorflow.org/install/install_sources
bazel build -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mfpmath=both --config=cuda -k //tensorflow/tools/pip_package:build_pip_package
su Xeon E5 v3 che mi dà un miglioramento 3x nella velocità della CPU matmul 8k rispetto alla versione ufficiale (0,35 -> 1,05 T ops / sec)