The additional packages required for PAI on Windows should be installed from their respective websites.
Please follow these steps:
1.Install Microsoft Visual Studio 2015, 2017, 2019 Runtime (i.e VC_redist.x86.exe). https://support.microsoft.com/en-us/help/2977003/the-latest-supported-visual-c-downloads
2.Check whether you have a compatible GPU. TensorFlow only supports NVIDIA GPUs in combination with NVIDIA’s CUDA Toolkit. Tables of compatible GPUs are available on https://developer.nvidia.com/cuda-gpus
3.If your GPU is compatible, install NVIDIA drivers, Toolkit and models for TensorFlow 2.4:
a.Drivers 11.0: https://www.nvidia.com/download/index.aspx?lang=en-us
b.CUDA Toolkit 11.0: https://developer.nvidia.com/cuda-toolkit-archive
c.cuDNN 8.0.4 for CUDA 11.0: https://developer.nvidia.com/rdp/cudnn-download
4.Install Python 3.8 64-bit (select “Add Python to PATH”, enable pip option and long paths). https://www.python.org/downloads/windows/
5.Upgrade pip by entering in a command terminal:
python -m pip install --upgrade pip
6.Install TensorFlow by entering in a command terminal:
python -m pip install --upgrade tensorflow
7.Check that tensorflow appears in the list of installed packages:
python -m pip list
8.Test TensorFlow by entering in a command terminal:
python -c "import tensorflow as tf;print(\"Num GPUs Available: \", len(tf.config.experimental.list_physical_devices('GPU')))"
This test returns the number of compatible GPUs available for PAI. Zero is an acceptable result if you do not have a CUDA-compatible NVIDIA GPU.
Note: GPU support for Windows (point 3. above) was tested for TensorFlow 2.4.