NumPy aware dynamic Python compiler using LLVM
Python C Jupyter Notebook HTML C++ Batchfile Other
Latest commit 5d1afe8 Jan 23, 2017 @seibert seibert committed on GitHub Merge pull request #2252 from sklam/fix/filename_prefix_test
Fix path separator in test
Failed to load latest commit information.
benchmarks Merge remote-tracking branch 'origin/master' into devel May 22, 2014
bin Fix problem with conda-build that entrypoint scripts are not installed Apr 16, 2015
buildscripts Bump llvmlite requirement to 0.16 and add install_name_tool_fixer to … Dec 30, 2016
docs Merge pull request #2175 from stuartarchibald/stage/trace_outer_kron Nov 28, 2016
examples Fix style in example and printing of unsorted array Oct 14, 2016
numba Merge pull request #2252 from sklam/fix/filename_prefix_test Jan 23, 2017
tutorials vectorize default target is now called 'cpu' as in guvectorize and jit. Feb 12, 2014
.binstar.yml Revert unwanted changes in binstar.yml Jun 30, 2015
.coveragerc Ignore vendored packages in coverage Mar 15, 2016
.gitattributes add versioneer Feb 2, 2013
.gitignore Added vscode directory to gitignore Sep 26, 2016
.travis.yml Add python3.6 to TravisCi Dec 28, 2016
AUTHORS Added new author. Sep 2, 2015
CHANGE_LOG Fix changelog Dec 20, 2016 Remove references to the numba-dev mailing-list, replace with numba-u… Oct 8, 2014
LICENSE Add make_ufunc function. Mar 8, 2012
LICENSES.third-party Add licenses for vendored libraries Mar 15, 2016 Include in Nov 2, 2015
README.rst Fix README rendering in pypi Dec 20, 2016
appveyor.yml Make sure system info tool runs. Nov 17, 2016
codecov.yml Also disable the "changes" status May 3, 2016 Add option for quick test on all python version Jun 3, 2014
requirements.txt Bump llvmlite requirements to 0.16 Jan 5, 2017 Try out Mar 9, 2016 Fix parallel testing under Windows Feb 1, 2016 Bump llvmlite requirement to 0.16 and add install_name_tool_fixer to … Dec 30, 2016 Fix #1141: add install_requires to May 13, 2015



A compiler for Python array and numerical functions

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 1.7 or higher)
  • 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=1.9 llvmlite

to use Python 2.7 and Numpy 1.9.

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

$ conda install cudatoolkit

This installs the CUDA Toolkit version 7.5, which requires driver version 352.79 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 3.7.

$ 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 7.5. 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