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dask package

dask 2022.01.0+dfsg-1ubuntu1 source package in Ubuntu

Changelog

dask (2022.01.0+dfsg-1ubuntu1) jammy; urgency=medium

  * Cherry-pick upstream commit for scipy 1.8 compatibility

 -- Graham Inggs <email address hidden>  Tue, 05 Apr 2022 11:34:20 +0000

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Uploaded by:
Graham Inggs
Uploaded to:
Jammy
Original maintainer:
Ubuntu Developers
Architectures:
all
Section:
misc
Urgency:
Medium Urgency

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Series Pocket Published Component Section
Jammy release universe misc

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Jammy: [FULLYBUILT] amd64

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File Size SHA-256 Checksum
dask_2022.01.0+dfsg.orig.tar.xz 4.1 MiB 77f81416b9e618cb33bd038679fec36371bf7c5796c9234d7de1f605ffe56bd9
dask_2022.01.0+dfsg-1ubuntu1.debian.tar.xz 24.3 KiB 6a21c9a840a59ae203a6b0705295da2c29c673c64841b48694b6ae7f6f40db8d
dask_2022.01.0+dfsg-1ubuntu1.dsc 3.1 KiB 45365aca9c05b3d7ff0209f64e1e9b01f7d42aef28083ead5ea598630593ccff

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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.