Ubuntu
dask package

dask 2.8.1+dfsg-0.2 source package in Ubuntu

Changelog

dask (2.8.1+dfsg-0.2) unstable; urgency=medium

  * Non-maintainer upload.
  * Add python3-packaging to the autopkg test dependencies.

 -- Matthias Klose <email address hidden>  Wed, 04 Dec 2019 20:38:16 +0100

Upload details

Uploaded by:
Debian Python Modules Team
Uploaded to:
Sid
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_2.8.1+dfsg-0.2.dsc 2.8 KiB 9f87ae993cfd438ffdd2e22d734a32fea5594b65828b36dd431cf22da5bde79b
dask_2.8.1+dfsg.orig.tar.xz 2.0 MiB 43bacb7cd500630eb19495c010299df984fad5a9ca3f5620b57509ab2e019c46
dask_2.8.1+dfsg-0.2.debian.tar.xz 6.6 KiB a3cc6e0ab2f64902535ef37c41d96b006a188c2f9fabfb2bf8b5bfc9cd75bf28

No changes file available.

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.