Skip to main content

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

  • LLVM 3.3
  • llvmpy (from llvmpy/llvmpy fork)
  • 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 llvmpy `

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 llvmpy `

to use Python 2.7 and Numpy 1.6.

Custom Python Environments

If you’re not using anaconda, you will need LLVM with RTTI enabled:

  • Compile LLVM 3.3

See https://github.com/llvmpy/llvmpy for the most up-to-date instructions.

`bash $ wget http://llvm.org/releases/3.3/llvm-3.3.src.tar.gz $ tar zxvf llvm-3.3.src.tar.gz $ cd llvm-3.3.src $ ./configure --enable-optimized --prefix=LLVM_BUILD_DIR $ # It is recommended to separate the custom build from the default system $ # package. $ # Be sure your compiler architecture is same as version of Python you will use $ #  e.g. -arch i386 or -arch x86_64.  It might be best to be explicit about this. $ REQUIRES_RTTI=1 make install `

  • Install llvmpy

`bash $ git clone https://github.com/llvmpy/llvmpy $ cd llvmpy $ LLVM_CONFIG_PATH=LLVM_BUILD_DIR/bin/llvm-config 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 `

NOTE: Make sure you install distribute instead of setuptools. Using setuptools
may mean that source files do not get cythonized and may result in an error during installation.

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/0.13/CUDASupport.html

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


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Filename, size & hash SHA256 hash help File type Python version Upload date
numba-0.13.4.tar.gz (791.4 kB) Copy SHA256 hash SHA256 Source None

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN SignalFx SignalFx Supporter DigiCert DigiCert EV certificate StatusPage StatusPage Status page