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Source: numba
Maintainer: Debian Science Maintainers <debian-science-maintainers@lists.alioth.debian.org>
Uploaders: Diane Trout <diane@ghic.org>
Section: science
Priority: optional
Build-Depends: architecture-is-64-bit,
debhelper-compat (= 13),
dh-python,
dh-sequence-sphinxdoc,
libtbb-dev (>= 2021.4.0),
llvmlite-doc <!nodoc>,
python3-all-dev,
python3-colorama (>= 0.3.9~),
python3-doc <!nodoc>,
python3-llvmlite (>= 0.44~),
python3-numpy-dev (>= 1:2.2),
python3-numpydoc,
python3-pip,
python3-scipy,
python3-setuptools,
python3-sphinx-rtd-theme,
python3-versioneer,
sphinx
Standards-Version: 4.6.2
Vcs-Browser: https://salsa.debian.org/science-team/numba
Vcs-Git: https://salsa.debian.org/science-team/numba.git
Homepage: https://numba.pydata.org/
Rules-Requires-Root: no
Package: python3-numba
Architecture: any
Section: python
Depends: python3-llvmlite,
python3-scipy,
${misc:Depends},
${python3:Depends},
${shlibs:Depends}
Recommends: numba-doc, python3-pip
Suggests: nvidia-cuda-toolkit [amd64 ppc64el]
Description: native machine code compiler for Python 3
Numba compiles native machine code instructions from Python programs at
runtime using the LLVM compiler infrastructure. It could be easily employed
by decorating individual computation intensive functions in the Python code.
Numba could significantly speed up the performance of computations, and
optionally supports compilation to run on GPU processors through Nvidia's
CUDA platform.
It integrates well with the Python scientific software stack, and
especially recognizes Numpy arrays.
.
This package contains the modules for Python 3.
Package: numba-doc
Architecture: all
Section: doc
Depends: ${misc:Depends}, ${sphinxdoc:Depends}
Recommends: python3-numba
Description: native machine code compiler for Python (docs)
Numba compiles native machine code instructions from Python programs at
runtime using the LLVM compiler infrastructure. Just-in-time compilation with
Numba could be easily employed by decorating individual computation intensive
functions in the Python code.
Numba could significantly speed up the performance of computations, and
optionally supports compilation to run on GPU processors through Nvidia's
CUDA platform.
It integrates well with the Python scientific software stack, and
especially recognizes Numpy arrays.
.
This package contains the documentation and examples.
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