Metadata-Version: 1.1
Name: numba
Version: 0.37.0+0.gb6b965afa.dirty
Summary: compiling Python code using LLVM
Home-page: http://numba.github.com
Author: Anaconda, Inc.
Author-email: numba-users@continuum.io
License: BSD
Description-Content-Type: UNKNOWN
Description: *****
        Numba
        *****
        
        .. image:: https://badges.gitter.im/numba/numba.svg
           :target: https://gitter.im/numba/numba?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
           :alt: Gitter
        
        A compiler for Python array and numerical functions
        ###################################################
        
        Numba is an Open Source NumPy-aware optimizing compiler for Python
        sponsored by Anaconda, 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 1.9 or higher)
        * funcsigs (for Python 2)
        
        
        Installing
        ==========
        
        The easiest way to install numba and get updates is by using the Anaconda
        Distribution: https://www.anaconda.com/download
        
        ::
        
           $ 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 5.0.x.
        
        ::
        
           $ 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 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): https://www.anaconda.com
        
        http://numba.pydata.org
        
        
        Continuous Integration
        ======================
        
        https://travis-ci.org/numba/numba
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Software Development :: Compilers
