tensorflow — TensorFlow + Jupyter
A GPU-ready TensorFlow environment with JupyterHub, SSH, and standard data-science packages. Ships in two flavors for newer and older NVIDIA hardware.
Image tags
docker.io/manvarharsh/tensorflow:cuda12— CUDA 12.4 + TensorFlow 2.18 (RTX 40xx, L4, L40S, H100)docker.io/manvarharsh/tensorflow:cuda11— CUDA 11.8 + TensorFlow 2.13 (RTX 30xx, Ampere, Volta, Turing)
What’s in this image
- Base:
nvidia/cuda:12.4.1-cudnn-devel-ubuntu22.04(or 11.8 variant) - Python 3.10 (conda)
- TensorFlow 2.18 (cuda12) or 2.13 (cuda11)
- NumPy, Pandas, Matplotlib, scikit-learn
- JupyterHub with the Podstack authenticator
- NVDashboard GPU monitoring
- OpenSSH server
Default ports
| Port | Service |
|---|---|
| 22 | SSH |
| 8000 | JupyterHub |
Use cases
- TensorFlow / Keras model training
- TFRecord-based pipelines
- TPU-compatible code paths
- General notebook-driven TF experimentation
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/.