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Marius Schamschula
53 lines
2.0 KiB
Tcl
53 lines
2.0 KiB
Tcl
# -*- 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
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PortSystem 1.0
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PortGroup python 1.0
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name py-dask
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version 2025.9.1
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revision 0
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categories-append devel
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license BSD
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supported_archs noarch
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platforms {darwin any}
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python.versions 310 311 312 313 314
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maintainers {stromnov @stromnov} openmaintainer
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description Minimal task scheduling abstraction.
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long_description Dask provides multi-core execution on larger-than-memory \
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datasets using blocked algorithms and task scheduling. \
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It maps high-level NumPy, Pandas, and list operations on \
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large datasets on to many operations on small in-memory \
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datasets. It then executes these graphs in parallel on a \
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single machine. Dask lets us use traditional NumPy, \
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Pandas, and list programming while operating on \
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inconveniently large data in a small amount of space.
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homepage https://github.com/dask/dask/
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checksums rmd160 252a7664bcd768be0241f999906dda2b3120a1b0 \
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sha256 718df73e1fd3d7e2b8546e0f04ce08e1ed7f9aa3da1eecd0c1f44c8b6d52f7e0 \
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size 10973663
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if {${name} ne ${subport}} {
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depends_build-append \
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port:py${python.version}-versioneer
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depends_lib-append port:py${python.version}-click \
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port:py${python.version}-cloudpickle \
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port:py${python.version}-fsspec \
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port:py${python.version}-packaging \
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port:py${python.version}-partd \
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port:py${python.version}-toolz \
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port:py${python.version}-yaml
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if {${python.version} < 312} {
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depends_lib-append port:py${python.version}-importlib-metadata
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}
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livecheck.type none
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}
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