Tensorflow Docker Dev WSL

Tensorflow GPU with Docker Dev Environments in WSL

Requirements

• Windows 11
• Windows Subsystem for Linux (WSL 2)
• Git on WSL (optional)
• Docker Desktop WSL 2
• Latest NVIDIA Windows GPU Driver
• Visual Studio Code with Dev Container extension

How to use

• Create a new repository from the template or clone the code
• In Dockerfile:
  • Update USERNAME ARG to create a non-root user
  • Update USER_UID and USER_GID ARGs (use id command in your Terminal to get them)
  • Update tensorflow image version (optional)
  • Update required packages (optional)
• Update pip packages in requirements.txt (optional)
• Docker Desktop > Dev Environments > Create (from a Git repo or a Local directory)

After the Dev Environment has been created:

• Docker Desktop > Dev Environments > OPEN IN VSCODE
• VSCODE > Terminal > New Terminal

  python main.py
  

Note. If you update the configuration files after the Dev Environment has been created, you must delete and recreate it