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Source: dask
Maintainer: Debian Python Team <team+python@tracker.debian.org>
Uploaders: Diane Trout <diane@ghic.org>
Section: python
Priority: optional
Build-Depends: debhelper-compat (= 13),
dh-python,
node-js-yaml <!nodoc>,
pybuild-plugin-pyproject,
python-asyncssh-doc <!nodoc>,
python-pandas-doc <!nodoc>,
python-fsspec-doc <!nodoc>,
python3-all,
python3-cloudpickle <!nodoc>,
python3-dask-sphinx-theme (>= 3.0.5-2) <!nodoc>,
python3-distributed (>= 2023.12.1) <!nodoc>,
python3-doc <!nodoc>,
python3-fsspec,
python3-importlib-metadata <!nodoc>,
python3-ipython <!nodoc>,
python3-numpydoc <!nodoc>,
python3-pandas (>= 1.3) <!nodoc>,
python3-partd <!nodoc>,
python3-scipy <!nodoc>,
python3-setuptools,
# python3-sparse (>= 0.11) <!nocheck>,
python3-sphinx <!nodoc>,
python3-sphinx-click <!nodoc>,
python3-sphinx-copybutton <!nodoc>,
python3-sphinx-design <!nodoc>,
python3-sphinx-remove-toctrees <!nodoc>,
python3-sphinx-tabs <!nodoc>,
python3-toolz <!nodoc>,
python3-versioneer,
sphinx-common,
tzdata-legacy,
Standards-Version: 4.7.0
Vcs-Browser: https://salsa.debian.org/python-team/packages/dask
Vcs-Git: https://salsa.debian.org/python-team/packages/dask.git
Homepage: https://github.com/dask/dask
Rules-Requires-Root: no
Package: python3-dask
Architecture: all
Depends: python3-fsspec,
python3-toolz,
${misc:Depends},
${python3:Depends}
Recommends: python3-cloudpickle,
python3-numpy,
python3-pandas,
python3-partd,
python3-requests
Suggests: ipython,
python-dask-doc <!nodoc>,
python3-blosc,
python3-boto3,
python3-distributed,
python3-graphviz,
python3-h5py,
python3-jinja2,
python3-psutil,
python3-scipy,
python3-skimage,
python3-sklearn,
python3-sqlalchemy,
python3-tables
Description: 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.
Package: python-dask-doc
Architecture: all
Section: doc
Depends: libjs-mathjax,
node-js-yaml,
${misc:Depends},
${sphinxdoc:Depends}
Description: 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
Build-Profiles: <!nodoc>
|
