# -*- coding: utf-8; mode: tcl; tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 4 -*- vim:fenc=utf-8:ft=tcl:et:sw=4:ts=4:sts=4
python.versions 27 35 36 37 38
maintainers {stromnov @stromnov} openmaintainer
description Minimal task scheduling abstraction.
long_description Dask provides multi-core execution on larger-than-memory \
datasets using blocked algorithms and task scheduling. \
It maps high-level NumPy, Pandas, and list operations on \
large datasets on to many operations on small in-memory \
datasets. It then executes these graphs in parallel on a \
single machine. Dask lets us use traditional NumPy, \
Pandas, and list programming while operating on \
inconveniently large data in a small amount of space.
homepage https://github.com/dask/dask/
checksums rmd160 1340de54dbfb3bc66ed7feb2ec7a092b901ac9c1 \
sha256 cb9c9af462f45fe4ad7d02180d58deaf56e0593be308491d7fde74bf51c630f0 \
if {${name} ne ${subport}} {
port:py${python.version}-setuptools
if {${python.version} eq 27} {
distname ${python.rootname}-${version}
checksums rmd160 94cce1974d6ad2830a6fd3d745cbe5d4100ff5ba \
sha256 5e7876bae2a01b355d1969b73aeafa23310febd8c353163910b73e93dc7e492c \
if {${python.version} eq 35} {
distname ${python.rootname}-${version}
checksums rmd160 29c49c566dfbb45c0908dd40d969538390ad231a \
sha256 81c7891f0d2e7ac03d1f7fabf1f639360a1db52c03a7155ba9b08e9ee6280f2b \