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tensorflow-usage.md

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Using TensorFlow based APIs

In order to run any TensorFlow based ML.Net APIs you must first add a NuGet dependency on the TensorFlow redist library. There are currently two versions you can use. One which is compiled for GPU support, and one which has CPU support only.

CPU only

CPU based TensorFlow is currently supported on:

  • Linux
  • MacOS
  • Windows

To get TensorFlow working on the CPU only all that is to take a NuGet dependency on SciSharp.TensorFlow.Redist v1.14.0

GPU support

GPU based TensorFlow is currently supported on:

  • Windows
  • Linux As of now TensorFlow does not support running on GPUs for MacOS, so we cannot support this currently.

Prerequisites

You must have at least one CUDA compatible GPU, for a list of compatible GPUs see Nvidia's Guide.

Install CUDA v10.1 and CUDNN v7.6.4.

Make sure you install CUDA v10.1, not any other newer version. After downloading CUDNN v7.6.4 .zip file and unpacking it, you need to do the following steps:

copy <CUDNN_zip_files_path>\cuda\bin\cudnn64_7.dll to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\bin

For C/C++ development:

Copy <CUDNN_zip_files_path>\cuda\ include\cudnn.h to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\include

Copy <CUDNN_zip_files_path>\cuda\lib\x64\cudnn.lib to <YOUR_DRIVE>\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.1\lib\x64

For further details in cuDNN you can follow the cuDNN Installation guide.

Usage

To use TensorFlow with GPU support take a NuGet dependency on the following package depending on your OS:

  • Windows -> SciSharp.TensorFlow.Redist-Windows-GPU
  • Linux -> SciSharp.TensorFlow.Redist-Linux-GPU

No code modification should be necessary to leverage the GPU for TensorFlow operations.

Troubleshooting

If you are not able to use your GPU after adding the GPU based TensorFlow NuGet, make sure that there is only a dependency on the GPU based version. If you have a dependency on both NuGets, the CPU based TensorFlow will run instead.