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‎site/en/install/pip.md‎

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@@ -26,9 +26,12 @@ step-by-step instructions.
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for more information about this collaboration.
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```bash
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conda install -c conda-forge cudatoolkit=11.2.2 cudnn=8.1.0
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
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python3 -m pip install tensorflow
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conda install -c conda-forge cudatoolkit=11.8.0
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python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.*
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mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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# Verify install:
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python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
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```
@@ -72,9 +75,12 @@ step-by-step instructions.
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for CUDA in WSL.
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```bash
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conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
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python3 -m pip install tensorflow
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conda install -c conda-forge cudatoolkit=11.8.0
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python3 -m pip install nvidia-cudnn-cu11==8.6.0.163 tensorflow==2.12.*
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mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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# Verify install:
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python3 -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
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```
@@ -145,7 +151,7 @@ Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards.
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## Software requirements
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* Python 3.8–3.11
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* pip version 19.0 or higher for Linux (requires `manylinux2010` support) and
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* pip version 19.0 or higher for Linux (requires `manylinux2014` support) and
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Windows. pip version 20.3 or higher for macOS.
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* Windows Native Requires
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[Microsoft Visual C++ Redistributable for Visual Studio 2015, 2017 and 2019](https://support.microsoft.com/help/2977003/the-latest-supported-visual-c-downloads){:.external}
@@ -155,8 +161,8 @@ The following NVIDIA® software are only required for GPU support.
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* [NVIDIA® GPU drivers](https://www.nvidia.com/drivers){:.external}
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version 450.80.02 or higher.
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* [CUDA® Toolkit 11.2](https://developer.nvidia.com/cuda-toolkit-archive){:.external}.
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* [cuDNN SDK 8.1.0](https://developer.nvidia.com/cudnn){:.external}.
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* [CUDA® Toolkit 11.8](https://developer.nvidia.com/cuda-toolkit-archive){:.external}.
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* [cuDNN SDK 8.6.0](https://developer.nvidia.com/cudnn){:.external}.
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* *(Optional)*
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[TensorRT](https://docs.nvidia.com/deeplearning/tensorrt/archives/index.html#trt_7){:.external}
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to improve latency and throughput for inference.
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nvidia-smi
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```
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Then install CUDA and cuDNN with conda.
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Then install CUDA and cuDNN with conda and pip.
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```bash
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conda install -c conda-forge cudatoolkit=11.2.2 cudnn=8.1.0
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conda install -c conda-forge cudatoolkit=11.8.0
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pip install nvidia-cudnn-cu11==8.6.0.163
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```
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Configure the system paths. You can do it with the following command every time
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you start a new terminal after activating your conda environment.
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```bash
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
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CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib
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```
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For your convenience it is recommended that you automate it with the following
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```bash
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mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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```
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### 5. Install TensorFlow
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PyPI.
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```bash
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pip install tensorflow==2.11.*
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pip install tensorflow==2.12.*
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```
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### 6. Verify install
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conda install -c nvidia cuda-nvcc=11.3.58
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# Configure the XLA cuda directory
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mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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printf 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/\nexport XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib/\n' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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printf 'export XLA_FLAGS=--xla_gpu_cuda_data_dir=$CONDA_PREFIX/lib/\n' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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source $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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# Copy libdevice file to the required path
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mkdir -p $CONDA_PREFIX/lib/nvvm/libdevice
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nvidia-smi
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```
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Then install CUDA and cuDNN with conda.
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Then install CUDA and cuDNN with conda and pip.
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```bash
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conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0
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conda install -c conda-forge cudatoolkit=11.8.0
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pip install nvidia-cudnn-cu11==8.6.0.163
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```
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Configure the system paths. You can do it with following command everytime
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your start a new terminal after activating your conda environment.
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```bash
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/
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CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))
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export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib
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```
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For your convenience it is recommended that you automate it with the following
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```bash
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mkdir -p $CONDA_PREFIX/etc/conda/activate.d
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/' > $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'CUDNN_PATH=$(dirname $(python -c "import nvidia.cudnn;print(nvidia.cudnn.__file__)"))' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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echo 'export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/:$CUDNN_PATH/lib' >> $CONDA_PREFIX/etc/conda/activate.d/env_vars.sh
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```
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### 5. Install TensorFlow
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PyPI.
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```bash
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pip install tensorflow
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pip install tensorflow==2.12.*
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```
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### 6. Verify install
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<table>
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<tr><th>Version</th><th>URL</th></tr>
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<tr class="alt"><td colspan="2">Linux</td></tr>
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<tr>
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<td>Python 3.7 GPU&nbsp;support</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.11.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.7 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.11.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.8 GPU&nbsp;support</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.8 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.11.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.12.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.9 GPU&nbsp;support</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.9 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.11.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.10 GPU&nbsp;support</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-2.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-2.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.10 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.11.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow_cpu-2.12.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl</td>
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</tr>
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<tr class="alt"><td colspan="2">macOS (CPU-only)</td></tr>
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<tr>
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<td>Python 3.7</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.11.0-cp37-cp37m-macosx_10_14_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.8</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.11.0-cp38-cp38-macosx_10_14_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.12.0-cp38-cp38-macosx_10_15_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.9</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.11.0-cp39-cp39-macosx_10_14_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.12.0-cp39-cp39-macosx_10_15_x86_64.whl</td>
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</tr>
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<tr>
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<td>Python 3.10</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.11.0-cp310-cp310-macosx_10_14_x86_64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-2.12.0-cp310-cp310-macosx_10_15_x86_64.whl</td>
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</tr>
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<tr class="alt"><td colspan="2">Windows</td></tr>
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<tr>
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<td>Python 3.7 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.11.0-cp37-cp37m-win_amd64.whl</td>
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</tr>
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<tr>
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<td>Python 3.8 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.11.0-cp38-cp38-win_amd64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.12.0-cp38-cp38-win_amd64.whl</td>
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</tr>
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<tr>
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<td>Python 3.9 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.11.0-cp39-cp39-win_amd64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.12.0-cp39-cp39-win_amd64.whl</td>
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</tr>
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<tr>
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<td>Python 3.10 CPU-only</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.11.0-cp310-cp310-win_amd64.whl</td>
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<td class="devsite-click-to-copy">https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow_cpu-2.12.0-cp310-cp310-win_amd64.whl</td>
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</tr>
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</table>

‎site/en/install/source_windows.md‎

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@@ -255,7 +255,9 @@ The filename of the generated `.whl` file depends on the TensorFlow version and
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your platform. Use `pip3 install` to install the package, for example:
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<pre class="devsite-terminal tfo-terminal-windows prettyprint lang-bsh">
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pip3 install C:/tmp/tensorflow_pkg/tensorflow-<var>version</var>-cp310-cp310-win_amd64.whl
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pip3 install C:/tmp/tensorflow_pkg/tensorflow-<var>version</var>-<var>tags</var>.whl
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e.g. pip3 install C:/tmp/tensorflow_pkg/tensorflow-2.12.0-cp310-cp310-win_amd64.whl
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</pre>
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Success: TensorFlow is now installed.
@@ -287,7 +289,7 @@ variable. If Bazel is installed to `C:\tools\bazel.exe`, and Python to
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<pre class="prettyprint lang-bsh">
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# Use Unix-style with ':' as separator
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<code class="devsite-terminal">export PATH="/c/tools:$PATH"</code>
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<code class="devsite-terminal">export PATH="/c/Python310:$PATH"</code>
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<code class="devsite-terminal">export PATH="/c/path/to/Python:$PATH"</code>
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</pre>
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For GPU support, add the CUDA and cuDNN bin directories to your `$PATH`:

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