Docker Container Debug with VScode

When using the docker container, one may want to use the feature in the vscode to remotely work on the code debug. For personal server, this would guarantee a relatively clean environment together with the debug feature.

1. Install Prerequisites

Before setting up VS Code with Docker, ensure you have:

• Docker installed ✅
• NVIDIA Container Toolkit installed ✅
• VS Code installed ✅
• VS Code Extensions:
  • Remote - Containers (ms-vscode-remote.remote-containers)
  • Docker (ms-azuretools.vscode-docker)

To install VS Code extensions, open VS Code and press:
Ctrl + Shift + X → Search for Remote - Containers and install it.

2. Mount Your Project Folder into Docker

If you want to edit files directly from VS Code while they run in the container, bind a local folder to the container:

docker run --gpus '"device=0,1,2"' -it -v ~/cuda_workspace:/workspace cuda-container bash

• This links ~/cuda_workspace from the host to /workspace inside the container.
• Any changes made in /workspace will reflect in ~/cuda_workspace on your host.

3. Attach VS Code to the Running Container

1. Start your container in the background:

   docker run --gpus '"device=0,1,2"' -itd -v ~/cuda_workspace:/workspace --name cuda_env cuda-container
   

   • -itd → Runs interactively but detached (in background).
   • --name cuda_env → Names the container for easier access.
2. Open VS Code & Attach to Container
   • Open VS Code.
   • Press Ctrl + Shift + P → Search for "Remote-Containers: Attach to Running Container".
   • Select cuda_env from the list.
3. VS Code Opens Inside the Container 🎉
   • You can now access files inside /workspace and use the terminal inside VS Code to run CUDA commands.

4. Running Jupyter Notebook in Docker + VS Code

If you’re working with Jupyter Notebook, start a container with Jupyter:

docker run --gpus '"device=0,1,2"' -it -v ~/cuda_workspace:/workspace -p 8888:8888 cuda-container bash -c "pip install notebook && jupyter notebook --ip=0.0.0.0 --port=8888 --allow-root"

• Access Jupyter Notebook from http://localhost:8888 on your host.

🚀 Summary: How to Use VS Code with Docker

| Action | Command | |——–|———| | Run container with a bind mount | docker run --gpus '"device=0,1,2"' -it -v ~/cuda_workspace:/workspace cuda-container bash | | Run in background (detach mode) | docker run --gpus '"device=0,1,2"' -itd -v ~/cuda_workspace:/workspace --name cuda_env cuda-container | | Attach VS Code to running container | Open VS Code → Ctrl + Shift + P → “Remote-Containers: Attach to Running Container” | | Stop the container | docker stop cuda_env | | Restart and reattach | docker start -ai cuda_env |