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compiling Python code using LLVM

Project description

Numba

Numba is an Open Source NumPy-aware optimizing compiler for Python sponsored by Continuum Analytics, Inc. It uses the remarkable LLVM compiler infrastructure to compile Python syntax to machine code.

It is aware of NumPy arrays as typed memory regions and so can speed-up code using NumPy arrays. Other, less well-typed code will be translated to Python C-API calls effectively removing the “interpreter” but not removing the dynamic indirection.

Numba is also not a tracing JIT. It compiles your code before it gets run either using run-time type information or type information you provide in the decorator.

Numba is a mechanism for producing machine code from Python syntax and typed data structures such as those that exist in NumPy.

Dependencies

  • llvmlite
  • numpy (version 1.6 or higher)
  • argparse (for pycc in python2.6)

Installing

The easiest way to install numba and get updates is by using the Anaconda Distribution: https://store.continuum.io/cshop/anaconda/

`bash $ conda install numba `

If you wanted to compile Numba from source, it is recommended to use conda environment to maintain multiple isolated development environments. To create a new environment for Numba development:

`bash $ conda create -p ~/dev/mynumba python numpy llvmlite `

To select the installed version, append “=VERSION” to the package name, where, “VERSION” is the version number. For example:

`bash $ conda create -p ~/dev/mynumba python=2.7 numpy=1.6 llvmlite `

to use Python 2.7 and Numpy 1.6.

Note: binary packages for llvmlite are currently available from Numba’s own binstar account, so you’ll have to add it to your channels first:

`bash $ conda config --add channels numba `

Custom Python Environments

If you’re not using conda, you will need to build llvmlite yourself:

  • Building and installing llvmlite

See https://github.com/numba/llvmlite for the most up-to-date instructions. You will need a build of LLVM 3.5.

`bash $ git clone https://github.com/numba/llvmlite $ cd llvmlite $ python setup.py install `

  • Installing Numba

`bash $ git clone https://github.com/numba/numba.git $ cd numba $ pip install -r requirements.txt $ python setup.py build_ext --inplace $ python setup.py install `

or simply

`bash $ pip install numba `

If you want to enable CUDA support, you will need CUDA Toolkit 5.5+ (which contains libnvvm). After installing the Toolkit, you might have to specify a few environment variables according to http://numba.pydata.org/numba-doc/dev/CUDASupport.html

Mailing Lists

Join the numba mailing list numba-users@continuum.io:

https://groups.google.com/a/continuum.io/d/forum/numba-users

or access it through the Gmane mirror: http://news.gmane.org/gmane.comp.python.numba.user

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

Website

See if our sponsor can help you (which can help this project): http://www.continuum.io

http://numba.pydata.org

Continuous Integration

https://travis-ci.org/numba/numba

Project details


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numba-0.16.0.tar.gz (892.1 kB) Copy SHA256 hash SHA256 Source None Dec 16, 2014

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