above) was tested for TensorFlow 2.104 and PyTorch 1. Python -c "import sklearn print(sklearn._version_)" Zero is an acceptable result if you do not have a CUDA-compatible NVIDIA GPU.ĩ.Install scikit-learn by entering in a command terminal:ġ0.Test scikit-learn by entering in a command terminal: This test returns the number of compatible GPUs available for PAI. Python -c "import tensorflow as tf print(\"Num GPUs Available: \", len(tf._physical_devices('GPU')))" CUDA Toolkit 11.2 (TensorFlow) 11.6 (PyTorch). Container Device Interface (CDI) Support As of the v1.12.0 release the NVIDIA Container Toolkit includes support for generating Container Device Interface (CDI) specificiations for use with CDI-enabled container engines and CLIs. ĥ.Upgrade pip by entering in a command terminal:Ħ.Install TensorFlow by entering in a command terminal:ħ.Check that tensorflow appears in the list of installed packages:Ĩ.Test TensorFlow by entering in a command terminal: If your GPU is compatible, install NVIDIA drivers, Toolkit and models for TensorFlow and PyTorch. The CUDA release notes includes a table of the minimum driver and CUDA Toolkit versions. Tables of compatible GPUs are available on ģ.If your GPU is compatible, install NVIDIA drivers, Toolkit and models for TensorFlow and PyTorch:ī.CUDA Toolkit 11.2 (TensorFlow) 11.6 (PyTorch): Ĭ.cuDNN 8.1 for CUDA 11.2 (TensorFlow) and cuDNN 8.6 for CUDA 11.6: Ĥ.Install Python 3.8 64-bit (select “Add Python to PATH”, enable pip option and long paths). TensorFlow only supports NVIDIA GPUs in combination with NVIDIA’s CUDA Toolkit. Ģ.Check whether you have a compatible GPU. The additional packages required for PAI on Windows should be installed from their respective websites.ġ.Install Microsoft Visual Studio 2015, 2017, 2019 Runtime (i.e VC_).
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