Skip to content

HTTPS clone URL

Subversion checkout URL

You can clone with HTTPS or Subversion.

Download ZIP
NumPy aware dynamic Python compiler using LLVM
Python C HTML Other

Merge pull request #1056 from sklam/fix_slicing

Fix uninitialized array attributes
latest commit e6ada6317b
@seibert seibert authored
Failed to load latest commit information.
benchmarks Merge remote-tracking branch 'origin/master' into devel
bin Rename annotate html flags:
buildscripts Add funcsigs to jenkins meta.yaml
continuous-integration/appveyor Add support for Appveyor builds with Python 2.7, 3.3, and 3.4
docs Update reference document to mention partial support for partial inde…
examples Remove example "findmulti"
numba Merge branch 'master' into fix_slicing
tutorials vectorize default target is now called 'cpu' as in guvectorize and jit.
.gitattributes add versioneer
.gitignore Remove docs/source/modules from .gitignore
.travis.yml Pin llvmdev version to 3.5 in travis CI
AUTHORS Update AUTHORS based on git history.
CHANGE_LOG Update changelog for 0.17.0
CONTRIBUTING.md Remove references to the numba-dev mailing-list, replace with numba-u…
LICENSE Add make_ufunc function.
MANIFEST.in Switch README to reStructuredText.
README.rst Update CUDA requirements and instructions
appveyor.yml Add support for Appveyor builds with Python 2.7, 3.3, and 3.4
condatestall.py 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
run_coverage.py Add a script for code coverage testing using the "coverage" module.
runtests.py Use a __name__ guard in runtests.py, to avoid infinite spawning loop …
setup.py Fix build issue
versioneer.py Added from __future and utf-8 encoding directive to all files (except…

README.rst

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)
  • funcsigs (for Python 2)

Installing

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

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

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

Installing Numba

$ 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

$ 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:

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

Documentation

http://numba.pydata.org/numba-doc/dev/index.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

Something went wrong with that request. Please try again.