The ultimate guide on installing PyTorch with CUDA support in all possible ways
→ Using Pip, Conda, Poetry, Docker, or directly on the system
We all know that one of the most annoying things in Deep Learning is installing PyTorch with CUDA support.
Nowadays, installing PyTorch & CUDA using pip or conda is relatively easy. Unfortunately, many production projects require you to use Poetry or Docker. That is where things get more complicated.
That is why I am writing this article as a practical living document showing how to install these 2 beasts in all possible ways.
This tutorial is a living document that I plan to use to install PyTorch & CUDA myself. Thus, I will update this doc whenever I test something I like. Also, in the comments section, feel free to add any other methods you use to install torch & CUDA or troubleshoot potential issues. Let’s create the go-to document that makes installing PyTorch & CUDA a piece of cake!
Important observation: I am mainly using Ubuntu. Thus, I will use concrete examples based on it. But this article can easily be extrapolated to other operating systems.
Another important observation: I have used Python 3.10, torch 2.0.1 and CUDA 11.8 in most examples. Feel free to change it with…
