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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 byte-code to machine code especially for use in the NumPy run-time and SciPy modules. Not all Python syntax is supported, currently. And long-term, the best that will be done with some Python constructs is to eliminate the "interpreter". Numba is going to work well first with NumPy arrays that are large arrays of typed information. If you are looking for a full Python compiler to handle all Python syntax, look at PyPy, ShedSkin, Nutika, or other projects. Numba is also not a tracing jit. It *compiles* your code before it gets run using type information you provide in the decorator. Dependencies: * LLVM 3.1 * llvm-py (from llvmpy/llvmpy fork) * numpy * Meta (from numba/Meta fork) * Compile LLVM 3.1: wget http://llvm.org/releases/3.1/llvm-3.1.src.tar.gz tar zxvf llvm-3.1.src.tar.gz ./configure --enable-optimized # 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. make install * Clone LLVM-py from github git clone https://github.com/llvmpy/llvmpy.git python setup.py install * Clone Meta from github git clone https://github.com/numba/Meta.git python setup.py install * Be sure to initialize the minivect submodule (in numba top-level dir) git submodule init git submodule update * Build Numba python setup.py install * Follow Numba Join the numba mailinglist email@example.com https://groups.google.com/a/continuum.io/d/forum/numba-users Some old archives are at http://librelist.com/browser/numba/ * See if our sponsor can help you (which can help this project) http://www.continuum.io