GPU-enabled docker container with Jupyterlab and Qiskit.

General information

Project name: An accessible infrastructure for quantum computing with Qiskit using a docker-based Jupyterlab in Galaxy.

Project home page: https://github.com/thepineapplepirate/qiskit-jupyter-galaxy_docker.git,

Originally forked from: https://github.com/anuprulez/ml-jupyter-notebook.git

Docker file: https://raw.githubusercontent.com/thepineapplepirate/qiskit-jupyter-galaxy_docker/main/Dockerfile

Container at Docker hub: https://hub.docker.com/r/thepineapplepirate/qiskit_galaxy (tag: 1.0.0)

Data: This copies and imports most of Qiskit's tutorials and jupyter notebooks.

Operating system(s): Linux

Programming language(s): Python, Docker, XML

Other requirements: Docker 20.10.13, (Optional) CUDA 11.6, CUDA DNN 8

License: MIT License

Running steps:

1. Download container: docker pull thepineapplepirate/qiskit_galaxy:1.0.0

2. Run container (on host without Nvidia GPU): docker run -it -p 8888:8888 -v <<path to local folder>>:/import thepineapplepirate/qiskit_galaxy:1.0.0

3. Run container (on host with Nvidia GPU): docker run -it --gpus all -p 8888:8888 -v <<path to local folder>>:/import thepineapplepirate/qiskit_galaxy:1.0.0

4. Open the link to the Jupyterlab (e.g. http://<<host>>:8888/ipython/lab)

List of packages

• Python
• Jupyterlab
• Jupyterlab-git
• CUDA
• CUDA DNN
• Bqplot
• Bokeh
• Voila
• Numpy
• Jupyterlab-nvdashboard
• Bioblend
• Qiskit (all) and Qiskit-research
• many more ...