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