Notebooks

A docker-based starter kit for machine learning via jupyter notebooks. Designed for those who just want a runtime environment and get on with machine learning.

Docker Images

To support both old and new environments, docker images cover various combinations of

• machine learning frameworks (Keras, TensorFlow, PyTorch)
• CUDA v8, v9, v10 and v11.

Check this compatibility chart for the required version of Nvidia graphics driver for your host system.

Python 3 only as Python 2 is end-of-life, so deprecated.

All of the images include:

• JupyterLab
• scikit-learn
• pandas

Visualization libraries:

• matplotlib
• seaborn

Vision-centric libraries:

• OpenCV
• scikit-image

NLP libraries:

• Natural Language Toolkit

Tags

If you are reading this on Docker Hub, the links to Dockefile's will not work. Please start from project page instead.

Note: the default 'latest' tag does not exist. This is a design choice. Please choose a tag from below.

PyTorch

Images of PyTorch version 1.5 and higher include PyTorch Lightning.

Tag (OS-based python)	Comment	Dockerfile	Info
pytorch2.9.1	CPU-only, Python 3.13.9	Dockerfile
pytorch2.9.1-cuda13.0	Python 3.11.14	Dockerfile
pytorch2.8.0-cuda12.6	Minimum required Nvidia Driver >= 525.60.13 (Linux) 528.33 (Windows). Toolkit Driver Version >= 560.28.03 (Linux) 560.76 (Windows).	Dockerfile
pytorch2.6.0-cuda12.4	Minimum required Nvidia Driver >= 525.60.13 (Linux) 528.33 (Windows). Toolkit Driver Version >= 550.54.15 (Linux) 551.78 (Windows).	Dockerfile
pytorch2.6.0-cuda11.8	Minimum required Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows). Toolkit Driver Version >= 520.61.05 (Linux) 520.06 (Windows).	Dockerfile
pytorch2.5.1-cuda12.1	Minimum required Nvidia Driver >= 525.60.13 (Linux) 528.33 (Windows). Toolkit Driver Version >= 530.30.02 (Linux) 531.14 (Windows).	Dockerfile
pytorch2.0.1-cuda11.7	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile

Tag (Conda-based python)	Comment	Dockerfile	Info
jupyter-pytorch1.3-conda3	CPU-only	Dockerfile
jupyter-pytorch1.3-conda3-cuda92	Nvidia Driver >= 396.37	Dockerfile
jupyter-pytorch1.1-conda3-cuda9	Nvidia Driver >= 384.xx	Dockerfile
jupyter-pytorch1.0-conda3-cuda8	Nvidia Driver >= 375.xx	Dockerfile

TensorFlow (including Keras)

Tag (OS-based python)	Comment	Dockerfile	Info
tf2.20.0	CPU-only, Python 3.11.13	Dockerfile
tf2.19.0-cuda12.3	Minimum required Nvidia Driver >= 525.60.13 (Linux) 528.33 (Windows). Toolkit driver version >= 545.23.06 (Linux) 545.84 (Windows).	Dockerfile
tf2.15.0-cuda11.8	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
tf2.11.1-cuda11.2	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
tf2.5.0-cuda11	Nvidia Driver >= 450.36.06	Dockerfile
tf2.3.4-cuda101	Nvidia Driver >= 418.xx	Dockerfile
tf2.0.4-cuda10	Nvidia Driver >= 410.xx	Dockerfile
tf1.15.5	CPU-only	Dockerfile

Tag (Conda-based python)	Comment	Dockerfile	Info
jupyter-keras-tf1.14.0-conda3	CPU-only	Dockerfile
jupyter-keras-tf1.14.0-conda3-cuda10	Nvidia Driver >= 410.xx	Dockerfile
jupyter-keras-tf1.12.0-conda3-cuda9	Nvidia Driver >= 384.xx	Dockerfile
jupyter-keras-tf1.4.1-conda3-cuda8	Nvidia Driver >= 375.xx	Dockerfile

Internal Tags

For intermediate Docker images, from which final images are build from, see INTERNAL.md.

Deprecated Tags

For deprecated tags, see deprecated/README.md.

Usage

Step 1: pull pre-built images:

docker pull wqael/notebooks:<tag>

Step 2: launch image:

docker run -it -v $2:/notebooks -p 8888:8888 -p 6006:6006 $1

or, for GPU support

nvidia-docker run -it -v $2:/notebooks -p 8888:8888 -p 6006:6006 $1

where:

• $1 is the tag for a docker image, e.g. wqael/notebooks:latest.
• $2 is the folder containing the notebooks on the host file system, e.g. clone this repo and use ~/notebooks.

Step 3: From the log, copy-and-paste the line similar to the following to your favorite browser:

    Copy/paste this URL into your browser when you connect for the first time,
    to login with a token:
        http://localhost:8888/?token=<token string>

Bonus step: Use next generation Jupyter: