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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 are specified, Nuitka will activate them before it activates any of its standard plugins.

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

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

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

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

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

A Word of Caution

Almost all are relevant for standalone mode only. Specifying all the right plugins is up to the user and critical for success: For example, if you are using package numpy and forget to activate that plugin, then your compile will

  • end with no error, but a warning about missing numpy support,
  • not generate a working binary.

Also:

  • are able to programmatically enable , the reverse is not possible. The user must know the requirements of his script and specify all appropriate , 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 by you, e.g. with PGO information that is going to be supported.

List of Optional Standard Plugins

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

The following optional standard plugins are available in Nuitka:

anti-bloat            Patch stupid imports out of widely used library modules source codes.
data-files
data-hiding           Commercial: Hide program constant Python data from offline inspection of created binaries.
datafile-inclusion    Commercial: Load file trusted file contents at compile time.
dill-compat
enum-compat
ethereum              Commercial: Required for ethereum packages in standalone mode
eventlet              Support for including 'eventlet' dependencies and its need for 'dns' package monkey patching
gevent                Required by the gevent package
gi                    Support for GI dependencies
glfw                  Required for glfw in standalone mode
implicit-imports
multiprocessing       Required by Python's multiprocessing module
numpy                 Required for numpy, scipy, pandas, matplotlib, etc.
pbr-compat
pkg-resources         Resolve version numbers at compile time.
pmw-freezer           Required by the Pmw package
pylint-warnings       Support PyLint / PyDev linting source markers
pyqt5                 Required by the PyQt5 package.
pyside2               Required by the PySide2 package.
pyside6               Required by the PySide6 package for standalone mode.
pyzmq                 Required for pyzmq in standalone mode
tensorflow            Required by the tensorflow package
tk-inter              Required by Python's Tk modules
torch                 Required by the torch / torchvision packages
traceback-encryption  Commercial: Encrypt tracebacks (de-Jong-Stacks).
windows-service       Commercial: Create Windows Service files

Note

This list is continuously growing and most likely out of date.

Optional Standard Plugins Documentation

dill-compat

  • Required by the dill module. Dill extends Python's pickle module for serializing and de-serializing objects.
  • Options: none.

eventlet

  • Required by the eventlet package. Eventlet is a concurrent networking library.
  • Options: none.

gevent

  • Required by the gevent package. Gevent is a coroutine-based Python networking library that uses greenlet to provide a high-level synchronous API.
  • Options: none.

numpy

  • Required for numpy, scipy, pandas, matplotlib, xarray, sklearn, skimage, and most other scientific packages.
  • Options: Can disable some of the packages handled, e.g. --enable-plugin=numpy --noinclude-scipy --noinclude-matplotlib which disables the handling to make these actually usable.

pmw-freezer

  • Required by the Pmw package. Pmw is a toolkit for building high-level compound widgets.
  • Options: none.

pylint-warnings

  • Support PyLint / PyDev linting source markers. Python static code analysis tools which help enforcing a coding standard.
  • Options: none

pyside2, pyside6, pyqt5

  • Required by the PySide and PyQt and GUI packages, only one can be activated at a time.
  • Options: With --include-qt-plugins you can select which Qt plugins to include. By default a relatively small set, called sensible that is defined in the code is include, but you can add more, and even all, which will add a terrible amount of dependencies though. But without the proper Qt plugins, functionality of Qt might be broken, crashes can occur, or appearance can be inferior.
  • These plugins also inhibit other GUI frameworks from being included in standalone distributions.

tensorflow

  • Required by the tensorflow package. TensorFlow is an open source machine learning framework for everyone. Note that this package requires numpy and potentially many other packages.
  • Options: none.

tk-inter

  • Required by Python's Tk modules.
  • Options: Can override the automatic detection of Tcl and Tk directories with --tk-library-dir and --tcl-library-dir but that should not be needed.

torch

  • Required by the torch and torchvision packages. Tensors and Dynamic neural networks in Python with strong GPU acceleration. Torchvision requires numpy.
  • Options: none.