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Build the pip package TensorFlow 2.xīazel build to create the TensorFlow 2.x package with CPU-only support:īazel build //tensorflow/tools/pip_package:build_pip_package Note: Starting with TensorFlow 1.6, binaries use AVX instructions which may not -config=v1 -Build TensorFlow 1.x instead of 2.x.-config=monolithic -Configuration for a mostly static, monolithic build.There are some preconfigured build configs available that can be added to the However, if building TensorFlow forĪ different CPU type, consider a more specific optimization flag.
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Generated code for your machine's CPU type.
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Update your CUDA library paths, this configuration step must be run again beforeįor compilation optimization flags, the default ( -march=native) optimizes the configure creates symbolic links to your system's CUDA libraries-so if you If your system has multiple versions of CUDA orĬuDNN installed, explicitly set the version instead of relying on the default. config=nonccl # Disable NVIDIA NCCL support.įor GPU support, set cuda=Y during configuration and specify the config=noaws # Disable AWS S3 filesystem support. Preconfigured Bazel build configs to DISABLE default on features: config=v2 # Build TensorFlow 2.x instead of 1.x. config=dynamic_kernels # (Experimental) Build kernels into separate shared objects.
config=ngraph # Build with Intel nGraph support. config=monolithic # Config for mostly static monolithic build. You can use any of the below by adding "-config=" to your build command.
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Not configuring the WORKSPACE for Android builds. Would you like to interactively configure. Please specify optimization flags to use during compilation when bazel option "-config=opt" is specified : Please specify which gcc should be used by nvcc as the host compiler. Please note that each additional compute capability significantly increases your build time and binary size, and that TensorFlow only supports compute capabilities >= 3.5 : 6.1ĭo you want to use clang as CUDA compiler? : You can find the compute capability of your device at: Each capability can be specified as "x.y" or "compute_xy" to include both virtual and binary GPU code, or as "sm_xy" to only include the binary code. Please specify a list of comma-separated CUDA compute capabilities you want to build with. No TensorRT support will be enabled for TensorFlow. No ROCm support will be enabled for TensorFlow.ĭo you wish to build TensorFlow with CUDA support? : YĬUDA support will be enabled for TensorFlow.ĭo you wish to build TensorFlow with TensorRT support? : No OpenCL SYCL support will be enabled for TensorFlow.ĭo you wish to build TensorFlow with ROCm support? : Default is ĭo you wish to build TensorFlow with OpenCL SYCL support? : Please input the desired Python library path to use. Session may differ): View sample configuration session
In both cases you can change the default. If using a virtual environment, python configure.py prioritizes paths This script prompts you for the location of TensorFlowĭependencies and asks for additional build configuration options (compiler
Git checkout branch_name # r2.2, r2.3, etc.Ĭonfigure your system build by running the. The repo defaults to the master development branch. TensorFlow repository: git clone cd tensorflow Note: It is easier to set up one of TensorFlow's GPU-enabled Docker images.
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Software required to run TensorFlow on a GPU.
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Read the GPU support guide to install the drivers and additional Install GPU support (optional, Linux only) _TF_MIN_BAZEL_VERSION and _TF_MAX_BAZEL_VERSION as specified in Sure to install a supported Bazel version: any version between If Bazelisk is not available, you can manually ForĮase of use, add Bazelisk as the bazel executable in your PATH. To build TensorFlow, you will need to install Bazel.īazel and automatically downloads the correct Bazel version for TensorFlow. Additional required dependencies are listed in theįile under REQUIRED_PACKAGES. Install the TensorFlow pip package dependencies (if using a virtualĮnvironment, omit the -user argument): pip install -U -user pip numpy wheel packaging requests opt_einsum pip install -U -user keras_preprocessing -no-deps Note: A pip version >19.0 is required to install the TensorFlow 2. Install using the Homebrew package manager: /usr/bin/ruby -e "$(curl -fsSL )" export PATH="/usr/local/opt/python/libexec/bin:$PATH" # if you are on macOS 10.12 (Sierra) use export PATH="/usr/local/bin:/usr/local/sbin:$PATH" brew install python Ubuntu sudo apt install python3-dev python3-pip macOS
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