compiling Python code using LLVM

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A Just-In-Time Compiler for Numerical Functions in Python

Numba is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax.

Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.

For more information about Numba, see the Numba homepage: http://numba.pydata.org

Dependencies

  • llvmlite

  • NumPy (version 1.9 or higher)

  • funcsigs (for Python 2)

Installing

The easiest way to install Numba and get updates is by using the Anaconda Distribution: https://www.anaconda.com/download

$ conda install numba

For more options, see the Installation Guide: http://numba.pydata.org/numba-doc/latest/user/installing.html

Documentation

http://numba.pydata.org/numba-doc/latest/index.html

Mailing Lists

Join the Numba mailing list numba-users@continuum.io: https://groups.google.com/a/continuum.io/d/forum/numba-users

Some old archives are at: http://librelist.com/browser/numba/

Continuous Integration

Travis CI AppVeyor

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0.61.0rc1 pre-release

0.60.0

0.60.0rc1 pre-release

0.59.1

0.59.0

0.59.0rc1 pre-release

0.58.1

0.58.0

0.58.0rc2 pre-release

0.58.0rc1 pre-release

0.57.1

0.57.1rc1 pre-release

0.57.0

0.57.0rc1 pre-release

0.56.4

0.56.3

0.56.2

0.56.0

0.56.0rc1 pre-release

0.55.2

0.55.1

0.55.0

0.55.0rc1 pre-release

0.54.1

0.54.1rc1 pre-release

0.54.0

0.54.0rc3 pre-release

0.54.0rc2 pre-release

0.53.1

0.53.0

0.53.0rc3 pre-release

0.53.0rc2 pre-release

0.53.0rc1.post1 pre-release

0.52.0

0.52.0rc3 pre-release

0.52.0rc2 pre-release

0.51.2

0.51.1

0.51.0

0.51.0rc1 pre-release

0.50.1

0.50.0

0.50.0rc1 pre-release

0.49.1

0.49.1rc1 pre-release

0.49.0

0.48.0

0.47.0

0.46.0

0.45.1

0.45.0

0.44.1

0.44.0

0.43.1

0.43.0

0.42.1

This version

0.42.0

0.41.0

0.40.1

0.40.0

0.39.0

0.38.1

0.38.0

0.37.0

0.36.2

0.36.1

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