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NumPy aware dynamic Python compiler using LLVM
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Merge pull request #1185 from sklam/fix/issue_1184

Fix #1184 and #1180: rewrite pass mishandle types.Dispatcher
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benchmarks Merge remote-tracking branch 'origin/master' into devel
bin Fix problem with conda-build that entrypoint scripts are not installed
buildscripts Remove obsolete conda-build recipes and fix the build dependencies so…
continuous-integration/appveyor Add support for Appveyor builds with Python 2.7, 3.3, and 3.4
docs Add a FAQ subsection to the docs
examples Fix fbcorr example
numba Fix #1184 and #1180: rewrite pass mishandle types.Dispatcher
tutorials vectorize default target is now called 'cpu' as in guvectorize and jit.
.binstar.yml Add funcsigs for Python 2
.gitattributes add versioneer
.gitignore Fix the gh-pages script for Python 3 and for non-US locales
.travis.yml Also hardwire 1.7 and 1.8 to Python 2.7 and 3.3
AUTHORS Update AUTHORS based on git history.
CHANGE_LOG ReST needs a blank link in the CHANGE_LOG before the bulleted list. Remove references to the numba-dev mailing-list, replace with numba-u…
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README.rst Update CUDA requirements and instructions
appveyor.yml Add support for Appveyor builds with Python 2.7, 3.3, and 3.4 Add option for quick test on all python version
coverage.conf Add a script for code coverage testing using the "coverage" module.
requirements.txt Update the README and requirements.txt Add a script for code coverage testing using the "coverage" module. Use a __name__ guard in, to avoid infinite spawning loop … Merge pull request #1143 from pitrou/install_requires Fix #1141: add install_requires to



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.


  • llvmlite
  • numpy (version 1.6 or higher)
  • argparse (for pycc in python2.6)
  • funcsigs (for Python 2)


The easiest way to install numba and get updates is by using the Anaconda Distribution:

$ 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:

$ 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:

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

to use Python 2.7 and Numpy 1.6.

If you need CUDA support, you should also install the CUDA toolkit:

$ conda install cudatoolkit

This installs the CUDA Toolkit version 6.0, which requires driver version 331.00 or later to be installed.

Custom Python Environments

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

Building and installing llvmlite

See for the most up-to-date instructions. You will need a build of LLVM 3.5.

$ git clone
$ cd llvmlite
$ python install

Installing Numba

$ git clone
$ cd numba
$ pip install -r requirements.txt
$ python build_ext --inplace
$ python install

or simply

$ pip install numba

If you want to enable CUDA support, you will need to install CUDA Toolkit 6.0. After installing the toolkit, you might have to specify environment variables in order to override the standard search paths:

Path to the CUDA driver shared library
Path to the CUDA libNVVM shared library file
Path to the CUDA libNVVM libdevice directory which contains .bc files


Mailing Lists

Join the numba mailing list

or access it through the Gmane mirror:

Some old archives are at:


See if our sponsor can help you (which can help this project):

Continuous Integration

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