- #Compressor 3.5 windows install
- #Compressor 3.5 windows drivers
- #Compressor 3.5 windows update
- #Compressor 3.5 windows windows
#Compressor 3.5 windows windows
MSYS automatically converts arguments that look like Unix paths to Windows Make the changes listedīelow, then follow the previous instructions for the Windows native command line
TensorFlow can also be built using the MSYS shell.
#Compressor 3.5 windows install
Pip3 install C:/tmp/tensorflow_pkg/tensorflow- version-cp36-cp36m-win_amd64.whl Use pip3 install to install the package, for example: whl file depends on the TensorFlow version and These two configurations in the same source tree. Same source tree, we recommend running bazel clean when switching between whl package in the C:/tmp/tensorflow_pkg directory:īazel-bin\tensorflow\tools\pip_package\build_pip_package C:/tmp/tensorflow_pkgĪlthough it is possible to build both CUDA and non-CUDA configs under the Is the program that builds the pip package. The bazel build command creates an executable named build_pip_package-this If building with GPU support, add -copt=-nvcc_options=disable-warnings Memory-constrained, limit Bazel's RAM usage with: -local_ram_resources=2048. Use this option when building to avoid issue with package creation:īuilding TensorFlow from source can use a lot of RAM. To make the TensorFlow package builder with GPU support:īazel build -config=opt -config=cuda -define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package Use bazel to make the TensorFlow package builder with CPU-only support:īazel build -config=opt //tensorflow/tools/pip_package:build_pip_package To build the 1.x version of TensorFlow from master, useīazel build -config=v1 to create a TensorFlow 1.x package.īazel build -config=v1 //tensorflow/tools/pip_package:build_pip_package Tensorflow:master repo has been updated to build 2.x by default.īazel build to create the TensorFlow package.īazel build //tensorflow/tools/pip_package:build_pip_package Note: Starting with TensorFlow 1.6, binaries use AVX instructions which may not This configuration step must be run again before building.
#Compressor 3.5 windows update
Links to your system's CUDA libraries-so if you update your CUDA library paths, Version instead of relying on the default./configure.py creates symbolic System has multiple versions of CUDA or cuDNN installed, explicitly set the Would you like to override eigen strong inline for some C++ compilation to reduce the compilation time? :įor GPU support, specify the versions of CUDA and cuDNN. Please specify optimization flags to use during compilation when bazel option "-config=opt" is specified : Please note that each additional compute capability significantly increases your build time and binary size. You can find the compute capability of your device at: Please specify a list of comma-separated Cuda compute capabilities you want to build with. Please specify the location where cuDNN 7 library is installed. Please specify the cuDNN version you want to use. Please specify the location where CUDA 9.0 toolkit is installed. Please specify the CUDA SDK version you want to use. Default is ĭo you wish to build TensorFlow with CUDA support? : YĬUDA support will be enabled for TensorFlow. Please input the desired Python library path to use.
Starting local Bazel server and connecting to it. configure.py (your session mayĭiffer): View sample configuration session Theįollowing shows a sample run of python. This script prompts you for the location of TensorFlow dependencies and asks forĪdditional build configuration options (compiler flags, for example). Configure the buildĬonfigure your system build by running the following at the root of your Key Point: If you're having build problems on the latest development branch, tryĪ release branch that is known to work. Git checkout branch_name # r1.9, r1.10, etc. The repo defaults to the master development branch. ( git is installed with MSYS2): git clone cd tensorflow
#Compressor 3.5 windows drivers
See the Windows GPU support guide to install the drivers andĪdditional software required to run TensorFlow on a GPU. Note: TensorFlow is tested against the Visual Studio 2019. Microsoft Visual C++ 2019 Redistributable.Select Redistributables and Build Tools,.If MSYS2 is installed to C:\msys64, addĬ:\msys64\usr\bin to your %PATH% environment variable. Install MSYS2 for the bin tools needed toīuild TensorFlow. Install Bazel, the build tool used to compileĪdd the location of the Bazel executable to your %PATH% environment variable. Install the TensorFlow pip package dependencies: pip3 install -U six numpy wheel pip3 install -U keras_preprocessing -no-depsįile under REQUIRED_PACKAGES.
Select pip as an optional feature and add it to your %PATH% environmental Install Python and the TensorFlow package dependencies Install the following build tools to configure your Windows developmentĮnvironment. Note: We already provide well-tested, pre-built Build a TensorFlow pip package from source and install it on Windows.
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