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Nuitka Standard Plugins Documentation

Background: Nuitka Plugins

Plugins are a feature to modify the way how Nuitka compiles Python programs in extremely flexible ways.

Plugins can automatically include data files and additional shared libraries, import modules which are not detectable by source code analysis, modify or extend the to-be-compiled source code, gather statistics, change Nuitka's parameter defaults and much more.

Any number of plugins may be used in each compilation.

Plugins come in two variants: standard plugins and user plugins.

User plugins are not part of the Nuitka package: they must be provided otherwise. To use them in a compilation, Nuitka must be able to find them using their path / filename. If user plugins are specified, Nuitka will activate them before it activates any of its standard plugins.

Standard plugins are part of the Nuitka package and are therefore always available.

Nuitka also differentiates between "mandatory" and "optional" standard plugins.

Mandatory standard plugins are always enabled and "invisible" to the user. Their behaviour cannot be influenced other than by modifying them.

Optional standard plugins must be enabled via the command line parameter --enable-plugin=name, with an identifying string name. Even when not enabled however, optional standard plugins can detect, whether their use might have been "forgotten" and issue an appropriate warning.

Where appropriate, the behaviour of optional standard plugins (like with user plugins) can be controlled via options (see "Using Plugin Options").

A Word of Caution

Specifying required optional standard plugins is up to the user. Specifically for standalone compilation, selecting the right optional standard plugins is critical for success.

  • While user plugins are able to programmatically enable optional standard plugins, standard plugins technically cannot do this. The user must know the requirements of his script and specify all appropriate optional standard plugins, including any required options (see below).
  • There is currently no way to automatically react to interdependencies. For example, when compiling a script using the tensorflow package in --standalone mode, you must enable (at least) both, the tensorflow and the numpy plugin.
  • Like every compiler, Nuitka cannot always decide, whether a script will actually execute an import statement. This knowledge must be provided to the compile command -- by specifying plugins and plugin options.

In this repository folder you will find ways which automate the correct and complete specification of optional standard plugins and their options.

List of Optional Standard Plugins

Create a list of available optional standard plugins giving their identifier together with a short description via --plugin-list:

      The following optional standard plugins are available in Nuitka
--------------------------------------------------------------------------------
 gevent           Required by the gevent package
 multiprocessing  Required by Python's multiprocessing module
 numpy            Required for numpy, scipy, pandas, matplotlib, etc.
 pmw-freezer      Required by the Pmw package
 pylint-warnings  Support PyLint / PyDev linting source markers
 qt-plugins       Required by the PyQt and PySide packages
 tensorflow       Required by the tensorflow package
 tk-inter         Required by Python's Tk modules on Windows
 torch            Required by the torch / torchvision packages

Optional Standard Plugins Documentation

gevent

  • Required by the gevent package.
  • Options: none.

numpy

  • Required for numpy, scipy, pandas, matplotlib, scikit-learn, and many other packages.
  • Options: specify any combination of scipy, matplotlib or sklearn, eg. --enable-plugin=numpy=scipy,matplotlib.

pmw-freezer

  • Required by the Pmw package.
  • Options: none.

pylint-warnings

  • Support PyLint / PyDev linting source markers.
  • Options: none

qt-plugins

  • Required by the PyQt and PySide packages.
  • Options: sensible, styles, all, ???

tensorflow

  • Required by the tensorflow package. Note that this package requires numpy and potentially many other packages or options.
  • Options: none.

tk-inter

  • Required by Python's Tk modules on Windows.
  • Options: none.

torch

  • Required by the torch and torchvision packages. Torchvision requires numpy.
  • Options: none.