hao-ai-lab-FastVideo/docs/contributing/developer_env/docker.md

🐳 Using the FastVideo Docker Image

If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:

Images: ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest

The published FastVideo tags are multi-platform Linux images for amd64 and arm64; Docker automatically pulls the matching architecture. The py3.12-latest and global latest tags select CUDA 12.6.3 with the cu126 PyTorch backend. py3.12-cuda12.6.3-latest is the explicit alias for that same image; use py3.12-cuda13.0.0-latest for CUDA 13 with cu130.

On ARM64, the CUDA 13 image targets DGX Spark (sm_121), while the CUDA 12.6 image targets GH200-class hardware (sm_90a). These published-tag defaults are distinct from an unparameterized local docker build: docker/Dockerfile itself still defaults to CUDA 13 and cu130.

DGX Spark users must therefore select py3.12-cuda13.0.0-latest; the default CUDA 12.6 ARM64 image does not contain an sm_121 FastVideo kernel.

Starting the container

docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest

This will:

• Start the container with GPU access
• Drop you into a shell with the FastVideo virtual environment activated

Building locally

The same Dockerfile builds both architectures and defaults to CUDA 13. On a native ARM64 host, build the CUDA 13 DGX Spark image with:

docker build --platform linux/arm64 -f docker/Dockerfile \
  --build-arg TORCH_CUDA_ARCH_LIST=12.1 \
  -t fastvideo-dev:spark .

This uses the prebuilt Linux ARM64 FlashAttention wheel. FastVideo's in-tree CUDA kernel is still compiled for sm_121 as part of the image build.

Using the container

# The FastVideo virtual environment should already be active
# FastVideo package installed in editable mode

# Pull the latest changes from remote
cd /FastVideo
git pull

# Run linters and tests
pre-commit run --all-files
pytest tests/