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compiling Python code using LLVM

Project description


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.


  • LLVM 3.3
  • llvmpy (from llvmpy/llvmpy fork)
  • numpy (version 1.6 or higher)
  • argparse (for pycc in python2.6)


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

`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 for the most up-to-date instructions.

`bash $ wget $ 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 $ cd llvmpy $ LLVM_CONFIG_PATH=LLVM_BUILD_DIR/bin/llvm-config python install `

  • Installing Numba

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

Mailing Lists

Join the numba mailing list

or access it through the Gmane mirror:

Some old archives are at:


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