Ubuntu
dask package

dask 2023.12.1+dfsg-1 source package in Ubuntu

Changelog

dask (2023.12.1+dfsg-1) unstable; urgency=medium

  * Team upload
  * New upstream version (closes: #1053947, #1056239)
  * Skip test known to fail on Python 3.12

 -- Julian Gilbey <email address hidden>  Tue, 02 Jan 2024 21:51:10 +0000

Upload details

Uploaded by:
Debian Python Team
Uploaded to:
Sid
Original maintainer:
Debian Python Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Builds

Noble: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
dask_2023.12.1+dfsg-1.dsc 3.1 KiB 4ba6e34418effb35e69ea828071c1d728007aa889d0c56c82bb53c188b15e7bb
dask_2023.12.1+dfsg.orig.tar.xz 7.8 MiB cbb5a2c6860116b2a08be2de3c01e30f3f47bbed172f53612fa6de94e0d1bd16
dask_2023.12.1+dfsg-1.debian.tar.xz 44.4 KiB b074edc6dda685625d3714a493bee74ed1015c9ffcc6caa5b7e1854e6d2c1b1b

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Binary packages built by this source

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

python3-dask: Minimal task scheduling abstraction for Python 3

 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 Python 3 version.