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NumPy aware dynamic Python compiler using LLVM
Home Page: http://numba.pydata.org/
License: BSD 2-Clause "Simplified" License
This project forked from numba/numba
NumPy aware dynamic Python compiler using LLVM
Home Page: http://numba.pydata.org/
License: BSD 2-Clause "Simplified" License
===== Numba ===== 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. Dependencies ============ * llvmlite * numpy (version 1.7 or higher) * 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=1.9 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 https://github.com/numba/llvmlite for the most up-to-date instructions. You will need a build of LLVM 3.7. :: $ 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 7.5. 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 [email protected]: 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
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