Nvidia PyTorch Container Extend for Kubeflow
- Dockerfile 49.4%
- Shell 32.7%
- Python 17.9%
| .forgejo/workflows | ||
| .github/workflows | ||
| authorized_keys | ||
| build.sh | ||
| docker-compose.yml | ||
| Dockerfile | ||
| jupyter_lab_config.py | ||
| README.md | ||
| start-notebook.sh | ||
| supervisord.conf | ||
Kubeflow PyTorch Notebook Container
GPU-accelerated PyTorch development environment optimized for Kubeflow with JupyterLab and SSH access.
Quick Start
In Kubeflow GUI
Notebooks -> + New Container / + New Notebook
Container Image:
# CUDA 12.9
git.kyu.sh/diverselab/kubeflow-nvcr-pytorch:v25.06-2
# CUDA 13.0
git.kyu.sh/diverselab/kubeflow-nvcr-pytorch:v25.11-2
Startup Command(If needed):
/usr/bin/supervisord -n -c /etc/supervisor/supervisord.conf
Docker Compose
# Your SSH public key (optional)
export SSH_PUBLIC_KEY="$(cat ~/.ssh/id_rsa.pub)"
docker-compose up -d
Docker Directly
# with Jupyter(8888) and SSH(22)
docker run -d \
-p 8888:8888 \
-p 22:22 \
--gpus all \
-e SSH_PUBLIC_KEY="$(cat ~/.ssh/id_rsa.pub)" \
-v $(pwd)/notebooks:/home/jovyan/work \
ghcr.io/KNU-PLML-Lab/kubeflow-nvcr-pytorch:latest
Access
- Jupyter Notebook: (http 8888)
- SSH: (port 22)
Environment Variables
| Variable | Description | Default |
|---|---|---|
SSH_PUBLIC_KEY |
SSH public key for authentication | None |
NB_PREFIX |
Base URL prefix for Jupyter | / |
JUPYTER_ENABLE_LAB |
Enable JupyterLab interface | yes |
Building from Source
# Clone the repository
git clone https://github.com/KNU-PLML-Lab/kubeflow-nvcr-pytorch.git
cd kubeflow-nvcr-pytorch
# Build the image
./build.sh