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

Overview

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

The Easy-Peasy Tensorflow-GPU Installation(Tensorflow 2.1, CUDA 11.0, and cuDNN) on Windows 10. The simplest way to install Tensorflow GPU on Windows 10.

Writing this article to help out those who have trouble in setting up Cuda enabled TensorFlow deep learning environment. If you don’t have Nvidia GPU configured in your system then this article is not for you

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