Updated: Mar 25
Today, we are announcing the release of QHub, a new open source project from Quansight that enables teams to build and maintain a cost-effective and scalable compute/data science platform in the cloud or on-premises. QHub can be deployed with minimal in-house DevOps experience.
See the demonstration here:
Flexible, Accessible, and Scalable
Deploying and maintaining a scalable computational platform in the cloud is difficult. There is a critical need in organizations for a shared compute platform that is flexible, accessible, and scalable. JupyterHub is an excellent platform for shared computational environments and Dask enables researchers to scale computations beyond the limits of their local machines. However, deploying and maintaining a scalable cluster for teams with Dask on JupyterHub is a fairly difficult task. QHub is designed to solve this problem without charging a large premium over infrastructure costs like many commercial platform vendors do OR requiring the heavy DevOps expertise that a roll-your-own solution typically does.
QHub provides the following:
Easy installation and maintenance controlled by a single configuration file
Autoscaling JupyterHub installation deployed on the cloud provider of your choice
Choice of compute instance types: normal; high memory; GPU; etc.
Big Data via autoscaling Dask compute clusters using any instance type and Python environment
Shell access and remote editing access (i.e. VS Code Remote)
Full Linux-style permissions allowing for different shared folders for different user groups
Data Science environment handling allowing for prebuilt and ad-hoc environment creation
Integrated video conferencing, using Jitsi