Using TensorFlow on Windows 10 with Nvidia RTX 3000 series GPUs

Using TensorFlow on Windows 10 with Nvidia RTX 3000 series GPUs

A step by step guide to building CUDA/cuDNN from source to use for GPU accelerated deep learning


Installing TensorFlow/CUDA/cuDNN for use with accelerating hardware like a GPU can be non-trivial, especially for novice users on a windows machine. The below describes how to build the CUDA/cuDNN packages from source so that TensorFlow tasks can be accelerated with a Nvidia RTX 30XX GPU.

This article assumes that

  • you are using an IDE like IntelliJ, PyCharm, or Spyder/Anaconda
  • you have used pip/conda to install packages
  • you have already physically installed your GPU

We will cover

  • briefly, checking system/hardware requirements
  • installing TensorFlow using pip
  • in depth, building CUDA/cuDNN from source so that TensorFlow can be used with GPU support

Before beginning it is worthwhile to note that TensorFlow and associated dependencies require very specific driver/package versions, as such it is recommended to use the exact versions specified in this article.

tensorflow python cuda nvidia gpu

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