A requirements.txt file that contains the dependencies of the project. A Dockerfile to generate the container. The article also provides instructions to use their packages on Ubuntu.įor this tutorial I’m using a toy project with just 3 files: In this article, you can find how to setup CUDA and cuDNN very easily on their platform. If you are still setting up your Linux machine and you are not willing to research much about it I usually recommend Pop!_OS. I will assume that you already installed CUDA on your machine. I will assume that you already installed Docker on your machine. Using Docker as a remote Python interpreter with P圜harm is possible only with the Professional edition. 
At the time of writing, I was unable to use CUDA inside of Docker in Windows 10 Home (even with the Insider build) so this tutorial has been implemented with Linux in mind even though there’s basically nothing that is platform-specific.
This tutorial is aimed to show you how to set up a basic Docker-based Python development environment with CUDA support in P圜harm or Visual Studio Code. Setting up a development environment is not easy if you are inexperienced, especially if a lot of technologies you wish to learn are involved.
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