Ubuntu
dask package

dask 2.8.0-0ubuntu2 source package in Ubuntu

Changelog

dask (2.8.0-0ubuntu2) focal; urgency=medium

  * New upstream version.
  * Add python3-packaging to the autopkg test dependencies.

 -- Matthias Klose <email address hidden>  Tue, 19 Nov 2019 06:29:18 +0100

Upload details

Uploaded by:
Matthias Klose
Uploaded to:
Focal
Original maintainer:
Debian Python Modules Team
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Builds

Focal: [FULLYBUILT] amd64

Downloads

File Size SHA-256 Checksum
dask_2.8.0.orig.tar.gz 2.4 MiB eeaca21cb925faef7d142031bbf9eecc25546defc57b9c7bc899b6febe996583
dask_2.8.0-0ubuntu2.debian.tar.xz 6.6 KiB 7c21c7f7bf458cbd9c119ab0b8efcac6b7623a4d18ede22509c1ce93da467719
dask_2.8.0-0ubuntu2.dsc 2.8 KiB 8816f0fbf94e39fbbd92fb5d374f08e489a4efe3835e5e6a5df8ee687d8f7afe

Available diffs

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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.