Este es el mensaje recibido al ejecutar un script para verificar si Tensorflow está funcionando:
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
Noté que ha mencionado SSE4.2 y AVX,
- ¿Qué son SSE4.2 y AVX?
- ¿Cómo estos SSE4.2 y AVX mejoran los cálculos de la CPU para las tareas de Tensorflow?
- ¿Cómo hacer que Tensorflow compile usando las dos bibliotecas?
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.
de aquí 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
en Xeon E5 v3 que me da una mejora de 3x en la velocidad de CPU de 8k matmul en comparación con el lanzamiento oficial (0.35 -> 1.05 T ops / seg)