QHub is an open source project from Quansight that enables organizations to build and maintain cost-effective and scalable compute/data science platforms on-premise or on any cloud provider with minimal in-house experience. It is a focused JupyterHub distribution that integrates many open source libraries into a coherent platform. The components that form QHub can be rearranged and customized to support many different enterprise use cases. For more information see https://www.quansight.com/post/announcing-qhub.
Quansight LLC is a data science and analytics consulting firm specializing in open source software around the PyData community including Jupyter, scikit-learn, Dask, Pandas, PyTorch, NumPy, SciPy, and much more. Quansight was co-founded by Travis Oliphant, founder of Anaconda, NumFOCUS, and PyData and creator of SciPy, NumPy, and Numba. For more information see https://www.quansight.com/about-us.
qhub is installed as a command line application in Python. It requires you to choose your the cloud provider you desire. once you've decided on a provider qhub will walk you through the following steps to configure your deployment:
- initialize
- render
- deploy
these steps realized using the qhub CLI.
qhub init do
qhub init aws
qhub init gcp
The qhub init command will generate configuration files for that service. The configutation files can be tailored to the needs of your organization. Each file specifies general project information, security, infrastructure settings, computational resource profiles and data science environments. See documentation on modifying your configuration file for all of the cloud providers: Configuration File
The configuration file is your user interface into deploying and scaling your data science environment. Each change triggers Github Action that will seamlessly update your infrastructure.
Check out the qhub documentation for more detailed information.
we need more information here.
qhub render -c qhub-config.yaml -o ./ --forcewhat is this business?
The qhub api normalizes with the nuances of configuring interactive data science environments across multiple client providers. The python command line interfaces define an initial environment state that is modified, and its changes are propogated by continuous integration.
Each qhub cloud provider has different configuration specifications; more details can be found at the following links about the Digital Ocean, AWS, and GCP configurations.
qhub is a pure python package that can be downloaded from the pypi.
pip install qhubRemove all infrastructure that qhub has created
qhub destroy -c qhub-config.yamlqhub is an open source project and welcomes issues and pull requests.
Creating a release:
- Increment the version number in
qhub/VERSION - Ensure that the version number in
qhub/VERSIONis used in pinning qhub in the github actionsqhub/template/{{ cookiecutter.repo_directory }}/.github/workflows/qhub-config.yaml