Note: GPU support is available for Ubuntu and Windows with CUDA®-enabled cards.

Windows WSL2 - Windows 10 19044 or higher (64-bit).

Windows Native - Windows 7 or higher (64-bit) (no GPU support after TF 2.10).

macOS 10.12.6 (Sierra) or higher (64-bit) (no GPU support).

You canĮnable compute capabilities by building TensorFlow from source. The TensorFlow package does not contain PTX for your architecture. Note: The error message 'Status: device kernel image is invalid' indicates that

Packages do not contain PTX code except for the latest supported CUDA®Īrchitecture therefore, TensorFlow fails to load on older GPUs when.

For GPUs with unsupported CUDA® architectures, or to avoid JIT compilationįrom PTX, or to use different versions of the NVIDIA® libraries, see the.

The following GPU-enabled devices are supported: Hardware requirements Note: TensorFlow binaries use Nightly python3 -m pip install tf-nightly There may be delays if the third party fails to release the pip package. Tensorflow will use reasonableĮfforts to maintain the availability and integrity of this pip package. conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0ĬPU Note: Starting with TensorFlow 2.10, Windows CPU-builds for x86/圆4 You can get the latest update from here:įor CUDA in WSL. This corresponds to Windows 10 version 21H2, the November 2021 Windows WSL2 Note: TensorFlow with GPU access is supported for WSL2 on Windows 10 19044 or Python -c 'import tensorflow as tf print(tf.config.list_physical_devices('GPU'))' # Anything above 2.10 is not supported on the GPU on Windows Native TensorFlow-DirectML-Plugin conda install -c conda-forge cudatoolkit=11.2 cudnn=8.1.0 Or install tensorflow-cpu and, optionally, try the Starting with TensorFlow 2.11, you will need to install Windows Native Caution: TensorFlow 2.10 was the last TensorFlow release that Python3 -c 'import tensorflow as tf print(tf.reduce_sum(tf.random.normal()))' MacOS # There is currently no official GPU support for MacOS.

Python3 -c 'import tensorflow as tf print(tf.config.list_physical_devices('GPU'))' conda install -c conda-forge cudatoolkit=11.2.2 cudnn=8.1.0Įxport LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CONDA_PREFIX/lib/ Seeįor more information about this collaboration. The third party fails to release the pip package. The availability and integrity of this pip package. Tensorflow will use reasonable efforts to maintain Processors are built, maintained, tested and released by a third party: Linux Note: Starting with TensorFlow 2.10, Linux CPU-builds for Aarch64/ARM64

Ubuntu install python 3.9