============================ Compiling code ahead of time

.. _pycc:

While Numba's main use case is :term:Just-in-Time compilation, it also provides a facility for :term:Ahead-of-Time compilation (AOT).

.. note:: To use this feature the setuptools package is required at compilation time, but it is not a runtime dependency of the extension module produced.

.. note:: This module is pending deprecation. Please see :ref:deprecation-numba-pycc for more information.

Overview

Benefits

#. AOT compilation produces a compiled extension module which does not depend on Numba: you can distribute the module on machines which do not have Numba installed (but NumPy is required).

#. There is no compilation overhead at runtime (but see the @jit :ref:cache <jit-cache> option), nor any overhead of importing Numba.

.. seealso:: Compiled extension modules are discussed in the Python packaging user guide.

Limitations

#. AOT compilation only allows for regular functions, not :term:ufuncs <ufunc>.

#. You have to specify function signatures explicitly.

#. Each exported function can have only one signature (but you can export several different signatures under different names).

#. Exported functions do not check the types of the arguments that are passed to them; the caller is expected to provide arguments of the correct type.

#. AOT compilation produces generic code for your CPU's architectural family (for example "x86-64"), while JIT compilation produces code optimized for your particular CPU model.

Usage

Standalone example

::

from numba.pycc import CC

cc = CC('my_module')

Uncomment the following line to print out the compilation steps

#cc.verbose = True

@cc.export('multf', 'f8(f8, f8)') @cc.export('multi', 'i4(i4, i4)') def mult(a, b): return a * b

@cc.export('square', 'f8(f8)') def square(a): return a ** 2

if name == "main": cc.compile()

If you run this Python script, it will generate an extension module named my_module. Depending on your platform, the actual filename may be my_module.so, my_module.pyd, my_module.cpython-34m.so, etc.

The generated module has three functions: multf, multi and square. multi operates on 32-bit integers (i4), while multf and square operate on double-precision floats (f8)::

import my_module my_module.multi(3, 4) 12 my_module.square(1.414) 1.9993959999999997

Distutils integration

You can also integrate the compilation step for your extension modules in your setup.py script, using distutils or setuptools::

from distutils.core import setup

from source_module import cc

setup(..., ext_modules=[cc.distutils_extension()])

The source_module above is the module defining the cc object. Extensions compiled like this will be automatically included in the build files for your Python project, so you can distribute them inside binary packages such as wheels or Conda packages. Note that in the case of using conda, the compilers used for AOT need to be those that are available in the Anaconda distribution.

Signature syntax

The syntax for exported signatures is the same as in the @jit decorator. You can read more about it in the :ref:types <numba-types> reference.

Here is an example of exporting an implementation of the second-order centered difference on a 1d array::

@cc.export('centdiff_1d', 'f8[:](f8[:], f8)') def centdiff_1d(u, dx): D = np.empty_like(u) D[0] = 0 D[-1] = 0 for i in range(1, len(D) - 1): D[i] = (u[i+1] - 2 * u[i] + u[i-1]) / dx**2 return D

.. (example from http://nbviewer.ipython.org/gist/ketch/ae87a94f4ef0793d5d52)

You can also omit the return type, which will then be inferred by Numba::

@cc.export('centdiff_1d', '(f8[:], f8)') def centdiff_1d(u, dx): # Same code as above ...