VisualStudio Code server
- Hosted on GitHub Container Registry (ghcr.io) to avoid DockerHub pull limitations, and easily deploy on clusters (such as Kubernetes).
Image based on https://github.com/cdr/code-server
In the container:
- User, with
sudoprivileges:coder - Workspace path:
/home/coder/project - Additionally installed: Python3, NodeJS (npm, yarn), Java JDK 17s, PHP, Fortran
docker run --rm -it -p 8080:8080 -e PASSWORD=password -v $(pwd):/home/coder/project ghcr.io/maastrichtu-ids/code-server:latestYou can also provide the URL of a git repository to be cloned at start, if a requirements.txt, yarn.lock or package-lock.json are present, they will be automatically installed
docker run --rm -it -p 8080:8080 -e PASSWORD=password -e GIT_URL=https://github.com/MaastrichtU-IDS/play-fair ghcr.io/maastrichtu-ids/code-server:latestdocker build -t ghcr.io/maastrichtu-ids/code-server:latest .docker push ghcr.io/maastrichtu-ids/code-server:latestImages based on base-images provided by Nvidia:
- Tensorflow: https://ngc.nvidia.com/catalog/containers/nvidia:tensorflow
- PyTorch: https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
In the containers:
- Workspace path:
/home/coder/project
Run TensorFlow:
docker run -it --rm -p 8081:8081 -e PASSWORD=password ghcr.io/maastrichtu-ids/code-server:tensorflow-21.05-tf2-py3Run PyTorch:
docker run -it --rm -p 8081:8081 -e PASSWORD=password ghcr.io/maastrichtu-ids/code-server:pytorch:25.02-py3Build TensorFlow:
docker build --build-arg NVIDIA_IMAGE=nvcr.io/nvidia/tensorflow:25.02-tf2-py3 -t ghcr.io/maastrichtu-ids/code-server:tensorflow:25.02-tf2-py3 -f tensorflow.Dockerfile .Build PyTorch:
docker build --build-arg NVIDIA_IMAGE=nvcr.io/nvidia/pytorch:25.02-py3 -t ghcr.io/maastrichtu-ids/code-server:pytorch:25.02-py3 -f pytorch.Dockerfile .Push TensorFlow:
docker push ghcr.io/maastrichtu-ids/code-server:tensorflow-21.05-tf2-py3Push PyTorch:
docker push ghcr.io/maastrichtu-ids/code-server:pytorch:25.02-py3