📦 Developing FastVideo on RunPod

You can easily use the FastVideo Docker image as a custom container on RunPod for development or experimentation.

Creating a new pod

Choose a GPU that supports CUDA 12.4

When creating your pod template, use this image:

ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest

Paste Container Start Command to support SSH (RunPod Docs):

bash -c "apt update;DEBIAN_FRONTEND=noninteractive apt-get install openssh-server -y;mkdir -p ~/.ssh;cd $_;chmod 700 ~/.ssh;echo \"$PUBLIC_KEY\" >> authorized_keys;chmod 700 authorized_keys;service ssh start;sleep infinity"

After deploying, the pod will take a few minutes to pull the image and start the SSH service.

Working with the pod

After SSH’ing into your pod, you’ll find the fastvideo-dev Conda environment already activated.

To pull in the latest changes from the GitHub repo:

cd /FastVideo
git pull

If you have a persistent volume and want to keep your code changes, you can move /FastVideo to /workspace/FastVideo, or simply clone the repository there.

Run your development workflows as usual:

# Run linters
pre-commit run --all-files

# Run tests
pytest tests/