pytorch — PyTorch + Jupyter
A GPU-ready PyTorch environment with JupyterHub, SSH, and common computer-vision libraries. Use this when you want a clean PyTorch sandbox without committing to a specific framework on top.
Image tag
docker.io/manvarharsh/pytorch:cuda12
What’s in this image
- Base:
nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04 - Python 3.10 (conda)
- PyTorch (CUDA 12), torchvision, torchaudio
- ultralytics (YOLO), opencv-python-headless
- NumPy, Pandas, Matplotlib, scikit-learn
- JupyterHub with the Podstack authenticator
- OpenSSH server
Default ports
| Port | Service |
|---|---|
| 22 | SSH |
| 8000 | JupyterHub |
Use cases
- General-purpose PyTorch development
- Training computer-vision models (detection, segmentation, classification)
- Notebook-driven ML experimentation
- A solid base for installing any PyTorch ecosystem package
Environment variables
| Variable | Description |
|---|---|
ENABLE_SSH | Enable SSH server |
ENABLE_JUPYTERHUB | Enable JupyterHub on port 8000 |
PODSTACK_API_URL | Backend URL for JupyterHub token validation |
SSH_PUBLIC_KEY | Public key for SSH |
Persistence
Mount at /data. Notebooks under /data/notebooks/, models under /data/models/, datasets under /data/datasets/.