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.

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

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.

Images of Pytorch version 1.5 and higher include Pytorch Lightning.

Tag (OS-based python)	Comment	Dockerfile	Info
pytorch2.4.1	CPU-only	Dockerfile
pytorch2.3.0-cuda12.1	Minimum required Nvidia Driver >= 525.60.13 (Linux) 528.33 (Windows). Toolkit Driver Version >= 530.30.02 (Linux) 531.14 (Windows).	Dockerfile
pytorch2.3.0-cuda11.8	Minimum required Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows). Toolkit Driver Version >= 520.61.05 (Linux) 520.06 (Windows)	Dockerfile
pytorch2.0.1-cuda11.7	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
pytorch1.13.1	CPU-only	Dockerfile
pytorch1.13.1-cuda11.7	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
pytorch1.13.1-cuda11.6	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
pytorch1.12.1-cuda11.3.1	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
pytorch1.12.1-cuda10.2	Nvidia Driver >= 440.33 (Linux) 441.22 (Windows)	Dockerfile
pytorch1.9.1-cuda11.1.1	Nvidia Driver >= 450.80.02 (Linux) 452.39 (Windows)	Dockerfile
pytorch1.7.1-cuda11	Nvidia Driver >= 450.36.06 (Linux) 451.22 (Windows)	Dockerfile
pytorch1.7.1-cuda101	Nvidia Driver >= 418.xx	Dockerfile
pytorch1.7.1-cuda92	Nvidia Driver >= 396.xx	Dockerfile
jupyter-pytorch1.2-py3-cuda10	Nvidia Driver >= 410.xx	Dockerfile
jupyter-pytorch1.1-py3-cuda9	Nvidia Driver >= 384.xx	Dockerfile
jupyter-pytorch1.0-py3-cuda8	Nvidia Driver >= 375.xx	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

Tag (OS-based python)	Comment	Dockerfile	Info
tf2.17.0	CPU-only	Dockerfile
tf2.16.1-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
tf1.15.5-cuda10	Nvidia Driver >= 410.xx	Dockerfile
jupyter-keras-tf1.12.3-py3-cuda9	Nvidia Driver >= 384.xx	Dockerfile
jupyter-keras-tf1.4.1-py3-cuda8	Nvidia Driver >= 375.xx	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

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

For deprecated tags, see deprecated/README.md.

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:
        https://localhost:8888/?token=<token string>

Bonus step: Use next generation Jupyter:

After jupyter home page is loaded, i.e. https://localhost:8888/tree, browse to https://localhost:8888/lab.

Step 4: How to shutdown the docker image:

In the running image terminal (step 3), hit Ctrl+C twice.

If this project helps your research, don't hesitate to spread the word! Click on the badge below and find citation formats (e.g., BibTeX and etc) at the bottom of that page.

MIT