AI Development Environment

Setup Docker container for genric AI development with PyTorch. Remember tests, colab notebooks etc.

The tests are performed in a Docker container that also works in the Windows Subsystem for Linux (WSL). An NVIDIA graphics card with at least 4 GB VRAM is recommended, depending on the models used. CUDA is part of the Docker image, only the NVIDIA graphics driver needs to be installed.

Docker must have CUDA enabled (e.g. for WSL see https://docs.nvidia.com/cuda/wsl-user-guide/index.html).

Start as local service with Test-UI

• Clone git@github.com:andrePankraz/ai-development.git

  $ cd ai-development
  $ TARGET=run docker compose up

  • Will take some time at first start (images & packages are downloaded, >10 GB)
  • Wait & check if up and running
• Go to URL: http://localhost:8200/
  • Will take some time at first start (models are downloaded, several GB)

Start for Development

• Clone git@github.com:andrePankraz/ai-development.git

  $ cd ai-development
  $ docker compose up

  • Will take some time at first start (images & packages are downloaded, >10 GB)
  • Wait & check if up and running
• Install VS Code
  • Install following Extensions
    • Dev Containers
• Attach VS Code to Docker Container
  • Attach to running containers... (Lower left edge in VS Code)
    • select /ai-development
  • Explorer Open folder -> /opt/ai-development
  • VS Code will autoinstall extensions: acknowledge and restart (takes some time)
  • Run / Start Debug
    • Use Launch Configuration Python:FastAPI (Under "Run & Debugging")
• Go to URL: http://localhost:8200/
  • Will take some time at first start (models are downloaded, several GB)