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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 Hub:

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Notebooks

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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.

notebooks_screenshot

Docker Images

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

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:

Visualization libraries:

Vision-centric libraries:

NLP libraries:

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.

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

Tensorflow (including Keras)

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

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:

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

jupyter_lab_screenshot

Step 4: How to shutdown the docker image:

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

Citation

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.

DOI

License

MIT