dask 2023.8.0+dfsg-2 source package in Ubuntu
Changelog
dask (2023.8.0+dfsg-2) unstable; urgency=medium
* Add force-little-endian-random.patch to try and initialize random
number generator on s380x the same as on x86_64 (Closes: #1050526)
* Depend on dask-sphinx-theme 3.0.5-2 to avoid accidentally including
googletagmanager in the documentation.
-- Diane Trout <email address hidden> Fri, 25 Aug 2023 21:22:33 -0700
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 | Published | Component | Section | |
|---|---|---|---|---|
| Mantic | release | universe | misc |
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| dask_2023.8.0+dfsg-2.dsc | 3.1 KiB | c8bdbf6ae0877b94cf9d8de847dcd02aad96a0dee36080ded4921024a747135b |
| dask_2023.8.0+dfsg.orig.tar.xz | 7.4 MiB | d3051ddea3ea189f125227b8b302883b11ef32196c2f3d1ac36446d63be723fa |
| dask_2023.8.0+dfsg-2.debian.tar.xz | 45.9 KiB | 728208955351b365f963dd7f38e82ee93ca76effc05b49ff2706aaee484b6b54 |
Available diffs
- diff from 2022.12.1+dfsg-2 to 2023.8.0+dfsg-2 (311.9 KiB)
- diff from 2023.8.0+dfsg-1 to 2023.8.0+dfsg-2 (1.0 KiB)
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.
