python-dask-doc – Minimal task scheduling abstraction documentation

Dask is a flexible parallel computing library for analytics, containing two components.

  1. Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. 2. “Big Data” collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of the dynamic task schedulers.

This contains the documentation

Package availability chart
Distribution Base version Our version Architectures
Debian GNU/Linux 10.0 (buster) 1.0.0+dfsg-2    
Debian GNU/Linux 11.0 (bullseye) 2021.01.0+dfsg-1    
Debian testing (bookworm) 2022.12.1+dfsg-2    
Debian unstable (sid) 2022.12.1+dfsg-2    
Ubuntu 18.04 “Bionic Beaver” (bionic) 0.16.0-1 0.17.5-2~nd18.04+1 i386, amd64, sparc, armel
Ubuntu 20.04 “Focal Fossa” (focal) 2.8.1+dfsg-0.4    
Ubuntu 21.10 “Impish Indri” (impish) 2021.01.0+dfsg-1ubuntu1    
Ubuntu 22.04 “Jammy Jellyfish” (jammy) 2022.01.0+dfsg-1ubuntu1    

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