NumFOCUS is pleased to announce the newest addition to our fiscally sponsored projects: Dask.
Dask is an open source library for natively scaling Python. It provides advanced parallelism for analytics, enabling performance at scale for the tools you love. Dask builds on existing Python libraries like NumPy, pandas, and scikit-learn to enable scalable computation on large datasets. In addition, Dask provides a general purpose framework to enable advanced users to build their own parallel applications. Dask enables analysts to scale from their multi-core laptop to thousand-node cluster.
Dask is applied across a wide set of applications, just like the full PyData stack itself. Dask was designed to scale the PyData stack generally and not for a specific application. Today, Dask is used to:
- Analyze our changing climate
- Track malaria-carrying mosquito populations in Africa
- Manage credit risk in large banks
- Process satellite imagery at scale
- Build production ETL workflows
- Model wireless networks
- Build machine learning models for urban development
The Dask leadership body for NumFOCUS consists of Matthew Rocklin, James Bourbeau, Jim Crist, Tom Augspurger, and Stephan Hoyer.
With the addition of Dask, the NumFOCUS fiscal sponsorship program now encompasses 29 open source scientific computing projects.