Use bazel to make the TensorFlow package builder with CPU-only support: bazel build -c opt -copt=-mavx -copt=-mavx2 -copt=-mfma -copt=-msse4.2 //tensorflow/tools/pip_package:build_pip_package The Tensorflow build options expose flags to enable building for platform-specific CPU instruction sets: bazel-0.26.0-installer-darwin-x86_64.sh -user export PATH="$PATH:$HOME/bin" bazel versionĬonfigure your system build by running the following at the root of your TensorFlow source tree. In my case, after downloading bazel-0.26.0-installer-darwin-x86_64.sh: chmod +x bazel-0.26.0-installer-darwin-x86_64.sh. Install Bazel, the build tool used to compile TensorFlow. Pip3 install -U -user keras_preprocessing=1.0.5 -no-deps Pip3 install -U -user keras_applications=1.0.6 -no-deps Install the TensorFlow pip package dependencies: pip3 install -U -user pip six numpy wheel setuptools mock future>=0.17.1 In a temp folder, clone Tensorflow: git clone We will start with uninstalling the default version of Tensorflow: sudo pip3 uninstall protobuf We won’t ignore the warning message and we will compile TF from source. Almost every machine-learning training involves a great deal of these operations, hence will be faster on a CPU that supports AVX and FMA (up to 300%). AVX provides new features, new instructions, and a new coding scheme.ĪVX introduces fused multiply-accumulate (FMA) operations, which speed up linear algebra computation, namely dot-product, matrix multiply, convolution, etc. I’ve received the following warning message: I tensorflow/core/platform/cpu_feature_:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMAĪdvanced Vector Extensions ( AVX) are extensions to the x86 instruction set architecture for microprocessors from Intel and AMD proposed by Intel in March 2008 and first supported by Intel with the Sandy Bridge processor shipping in Q1 2011 and later on by AMD with the Bulldozer processor shipping in Q3 2011. After installing Tensorflow using pip3 install: sudo pip3 install tensorflow
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