dask 1.0.0+dfsg-2 source package in Ubuntu
Changelog
dask (1.0.0+dfsg-2) unstable; urgency=medium * Support numpy 1.16's multifield copy->view (Closes: 918204) * Support numpy 1.16's extended gufunc signatures -- Diane Trout <email address hidden> Thu, 10 Jan 2019 17:03:25 -0500
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 | Published | Component | Section |
|---|
Downloads
| File | Size | SHA-256 Checksum |
|---|---|---|
| dask_1.0.0+dfsg-2.dsc | 2.7 KiB | 7db6462a76ad3e5ce8b9b1a7b011888343abde5d8c92e9d554ae15ddee0c02db |
| dask_1.0.0+dfsg.orig.tar.xz | 2.0 MiB | 1f89d8fc13e55e23f807b84dced140a977f671d9ee445a7594f0461352172cbb |
| dask_1.0.0+dfsg-2.debian.tar.xz | 8.1 KiB | 4bcbbd281cc8041d6abc5a4ce35130c23dddc45066156445f6228aaae4b6f8c3 |
Available diffs
- diff from 1.0.0+dfsg-1 to 1.0.0+dfsg-2 (2.1 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.
