####################
 Nuitka User Manual
####################

**********
 Overview
**********

This document is the recommended first read if you are interested in
using Nuitka, understand its use cases, check what you can expect,
license, requirements, credits, etc.

Nuitka is **the** Python compiler. It is written in Python. It is a
seamless replacement or extension to the Python interpreter and compiles
**every** construct that CPython 2.6, 2.7, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9,
3.10, 3.11 have, when itself run with that Python version.

It then executes uncompiled code and compiled code together in an
extremely compatible manner.

You can use all Python library modules and all extension modules freely.

Nuitka translates the Python modules into a C level program that then
uses ``libpython`` and static C files of its own to execute in the same
way as CPython does.

All optimization is aimed at avoiding overhead, where it's unnecessary.
None is aimed at removing compatibility, although slight improvements
will occasionally be done, where not every bug of standard Python is
emulated, e.g. more complete error messages are given, but there is a
full compatibility mode to disable even that.

**************
 Requirements
**************

C Compiler
==========

You need a C compiler with support for C11 or alternatively a C++
compiler for C++03 [#]_.

Currently, this means, you need to use one of these compilers:

-  The MinGW64 C11 compiler, on Windows, must be based on gcc 11.2 or
   higher. It will be *automatically* downloaded if no usable C compiler
   is found, which is the recommended way of installing it, as Nuitka
   will also upgrade it for you.

-  Visual Studio 2022 or higher on Windows [#]_, older versions will
   work, but only supported for commercial users. Configure to use the
   English language pack for best results (Nuitka filters away garbage
   outputs, but only for English language). It will be used by default
   if installed.

-  On all other platforms, the ``gcc`` compiler of at least version 5.1,
   and below that the ``g++`` compiler of at least version 4.4 as an
   alternative.

-  The ``clang`` compiler on macOS X and most FreeBSD architectures.

-  On Windows, the ``clang-cl`` compiler on Windows can be used if
   provided by the Visual Studio installer.

.. [#]

   Support for this C11 is given with gcc 5.x or higher or any clang
   version.

   The MSVC compiler doesn't do it yet. But as a workaround, as the C++03
   language standard is significantly overlapping with C11, it is then used
   instead where the C compiler is too old. Nuitka used to require a C++
   compiler in the past, but it changed.

.. [#]

   Download for free from
   https://www.visualstudio.com/en-us/downloads/download-visual-studio-vs.aspx
   (the community editions work just fine).

   The latest version is recommended, but not required. On the other hand,
   there is no need to except to support pre-Windows 10 versions, and they
   might work for you, but support of these configurations is only
   available to commercial users.

Python
======

Python Version 2.6, 2.7 or 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11 are
supported. If at any moment, there is a stable Python release that is
not in this list, rest assured it is being worked on and will be added.

.. important::

   For Python 3.4 and *only* that version, we need other Python version
   as a *compile time* dependency.

   Nuitka itself is fully compatible with all listed versions, but Scons
   as an internally used tool is not.

   For these versions, you *need* a Python2 or Python 3.5 or higher
   installed as well, but only during the compile time. That is for use
   with Scons (which orchestrates the C compilation), which does not
   support the same Python versions as Nuitka.

   In addition, on Windows, Python2 cannot be used because ``clcache``
   does not work with it, there a Python 3.5 or higher needs to be
   installed.

   Nuitka finds these needed Python versions (e.g. on Windows via
   registry) and you shouldn't notice it as long as they are installed.

   Increasingly, other functionality is available when another Python
   has a certain package installed. For example, onefile compression
   will work for a Python 2.x when another Python is found that has the
   ``zstandard`` package installed.

.. admonition:: Moving binaries to other machines

   The created binaries can be made executable independent of the Python
   installation, with ``--standalone`` and ``--onefile`` options.

.. admonition:: Binary filename suffix

   The created binaries have an ``.exe`` suffix on Windows. On other
   platforms they have no suffix for standalone mode, or ``.bin``
   suffix, that you are free to remove or change, or specify with the
   ``-o`` option.

   The suffix for acceleration mode is added just to be sure that the
   original script name and the binary name do not ever collide, so we
   can safely overwrite the binary without destroying the original
   source file.

.. admonition:: It **has to** be CPython, Anaconda Python, or Homebrew

   You need the standard Python implementation, called "CPython", to
   execute Nuitka because it is closely tied to implementation details
   of it.

.. admonition:: It **cannot be** from the Windows app store

   It is known that Windows app store Python definitely does not work,
   it's checked against.

.. admonition:: It **cannot be** pyenv on macOS

   It is known that macOS "pyenv" does **not** work. Use Homebrew
   instead for self compiled Python installations. But note that
   standalone mode will be worse on these platforms and not be as
   backward compatible with older macOS versions.

Operating System
================

Supported Operating Systems: Linux, FreeBSD, NetBSD, macOS X, and
Windows (32 bits/64 bits/ARM).

Others will work as well. The portability is expected to be generally
good, but the e.g. Nuitka's internal Scons usage may have to be adapted
or need flags passed. Make sure to match Python and C compiler
architecture, or else you will get cryptic error messages.

Architecture
============

Supported Architectures are x86, x86_64 (amd64), and arm, likely many,
many more.

Other architectures are expected to also work, out of the box, as Nuitka
is generally not using any hardware specifics. These are just the ones
tested and known to be good. Feedback is welcome. Generally, the
architectures that Debian supports can be considered good and tested,
too.

*******
 Usage
*******

Command Line
============

The recommended way of executing Nuitka is ``<the_right_python> -m
nuitka`` to be absolutely certain which Python interpreter you are
using, so it is easier to match with what Nuitka has.

The next best way of executing Nuitka bare that is from a source
checkout or archive, with no environment variable changes, most
noteworthy, you do not have to mess with ``PYTHONPATH`` at all for
Nuitka. You just execute the ``nuitka`` and ``nuitka-run`` scripts
directly without any changes to the environment. You may want to add the
``bin`` directory to your ``PATH`` for your convenience, but that step
is optional.

Moreover, if you want to execute with the right interpreter, in that
case, be sure to execute ``<the_right_python> bin/nuitka`` and be good.

.. admonition:: Pick the right Interpreter

   If you encounter a ``SyntaxError`` you absolutely most certainly have
   picked the wrong interpreter for the program you are compiling.

Nuitka has a ``--help`` option to output what it can do:

.. code:: bash

   nuitka --help

The ``nuitka-run`` command is the same as ``nuitka``, but with a
different default. It tries to compile *and* directly execute a Python
script:

.. code:: bash

   nuitka-run --help

This option that is different is ``--run``, and passing on arguments
after the first non-option to the created binary, so it is somewhat more
similar to what plain ``python`` will do.

Installation
============

For most systems, there will be packages on the `download page
<https://nuitka.net/doc/download.html>`__ of Nuitka. But you can also
install it from source code as described above, but also like any other
Python program it can be installed via the normal ``python setup.py
install`` routine.

Notice for integration with GitHub workflows there is this
`Nuitka-Action <https://github.com/Nuitka/Nuitka-Action>`__ that you
should use that makes it really easy to integrate. You ought to start
with a local compilation though, but this will be easiest for cross
platform compilation with Nuitka.

License
=======

Nuitka is licensed under the Apache License, Version 2.0; you may not
use it except in compliance with the License.

You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.

*************************************
 Tutorial Setup and build on Windows
*************************************

This is basic steps if you have nothing installed, of course if you have
any of the parts, just skip it.

Setup
=====

Install Python
--------------

-  Download and install Python from
   https://www.python.org/downloads/windows

-  Select one of ``Windows x86-64 web-based installer`` (64 bits Python,
   recommended) or ``x86 executable`` (32 bits Python) installer.

-  Verify it's working using command ``python --version``.

Install Nuitka
--------------

-  ``python -m pip install nuitka``

-  Verify using command ``python -m nuitka --version``

Write some code and test
========================

Create a folder for the Python code
-----------------------------------

-  ``mkdir`` HelloWorld

-  make a python file named **hello.py**

.. code:: python

   def talk(message):
       return "Talk " + message


   def main():
       print(talk("Hello World"))


   if __name__ == "__main__":
       main()

Test your program
-----------------

Do as you normally would. Running Nuitka on code that works incorrectly
is not easier to debug.

.. code:: bash

   python hello.py

----

Build it using
--------------

.. code:: bash

   python -m nuitka hello.py

.. note::

   This will prompt you to download a C caching tool (to speed up
   repeated compilation of generated C code) and a MinGW64 based C
   compiler, unless you have a suitable MSVC installed. Say ``yes`` to
   both those questions.

Run it
------

Execute the ``hello.exe`` created near ``hello.py``.

Distribute
----------

To distribute, build with ``--standalone`` option, which will not output
a single executable, but a whole folder. Copy the resulting
``hello.dist`` folder to the other machine and run it.

You may also try ``--onefile`` which does create a single file, but make
sure that the mere standalone is working, before turning to it, as it
will make the debugging only harder, e.g. in case of missing data files.

***********
 Use Cases
***********

Use Case 1 - Program compilation with all modules embedded
==========================================================

If you want to compile a whole program recursively, and not only the
single file that is the main program, do it like this:

.. code:: bash

   python -m nuitka --follow-imports program.py

.. note::

   There are more fine-grained controls than ``--follow-imports``
   available. Consider the output of ``nuitka --help``. Including fewer
   modules into the compilation, but instead using normal Python for it,
   will make it faster to compile.

In case you have a source directory with dynamically loaded files, i.e.
one which cannot be found by recursing after normal import statements
via the ``PYTHONPATH`` (which would be the recommended way), you can
always require that a given directory shall also be included in the
executable:

.. code:: bash

   python -m nuitka --follow-imports --include-plugin-directory=plugin_dir program.py

.. note::

   If you don't do any dynamic imports, simply setting your
   ``PYTHONPATH`` at compilation time is what you should do.

   Use ``--include-plugin-directory`` only if you make ``__import__()``
   calls that Nuitka cannot predict, and that come from a directory, for
   everything from your Python installation, use ``--include-module`` or
   ``--include-package``.

.. note::

   The resulting filename will be ``program.exe`` on Windows,
   ``program.bin`` on other platforms, but ``--output-filename`` allows
   changing that.

.. note::

   The resulting binary still depends on CPython and used C extension
   modules being installed.

   If you want to be able to copy it to another machine, use
   ``--standalone`` and copy the created ``program.dist`` directory and
   execute the ``program.exe`` (Windows) or ``program`` (other
   platforms) put inside.

Use Case 2 - Extension Module compilation
=========================================

If you want to compile a single extension module, all you have to do is
this:

.. code:: bash

   python -m nuitka --module some_module.py

The resulting file ``some_module.so`` can then be used instead of
``some_module.py``.

.. important::

   The filename of the produced extension module must not be changed as
   Python insists on a module name derived function as an entry point,
   in this case ``PyInit_some_module`` and renaming the file will not
   change that. Match the filename of the source code to what the binary
   name should be.

.. note::

   If both the extension module and the source code of it are in the
   same directory, the extension module is loaded. Changes to the source
   code only have effect once you recompile.

.. note::

   The option ``--follow-import-to`` works as well, but the included
   modules will only become importable *after* you imported the
   ``some_module`` name. If these kinds of imports are invisible to
   Nuitka, e.g. dynamically created, you can use ``--include-module`` or
   ``--include-package`` in that case, but for static imports it should
   not be needed.

.. note::

   An extension module can never include other extension modules. You
   will have to create a wheel for this to be doable.

.. note::

   The resulting extension module can only be loaded into a CPython of
   the same version and doesn't include other extension modules.

Use Case 3 - Package compilation
================================

If you need to compile a whole package and embed all modules, that is
also feasible, use Nuitka like this:

.. code:: bash

   python -m nuitka --module some_package --include-package=some_package

.. note::

   The inclusion of the package contents needs to be provided manually;
   otherwise, the package is mostly empty. You can be more specific if
   you like, and only include part of it, or exclude part of it, e.g.
   with ``--nofollow-import-to='*.tests'`` you would not include the
   unused test part of your code.

.. note::

   Data files located inside the package will not be embedded by this
   process, you need to copy them yourself with this approach.
   Alternatively, you can use the `file embedding of Nuitka commercial
   <https://nuitka.net/doc/commercial/protect-data-files.html>`__.

Use Case 4 - Program Distribution
=================================

For distribution to other systems, there is the standalone mode, which
produces a folder for which you can specify ``--standalone``.

.. code:: bash

   python -m nuitka --standalone program.py

Following all imports is default in this mode. You can selectively
exclude modules by specifically saying ``--nofollow-import-to``, but
then an ``ImportError`` will be raised when import of it is attempted at
program run time. This may cause different behavior, but it may also
improve your compile time if done wisely.

For data files to be included, use the option
``--include-data-files=<source>=<target>`` where the source is a file
system path, but the target has to be specified relative. For the
standalone mode, you can also copy them manually, but this can do extra
checks, and for the onefile mode, there is no manual copying possible.

To copy some or all file in a directory, use the option
``--include-data-files=/etc/*.txt=etc/`` where you get to specify shell
patterns for the files, and a subdirectory where to put them, indicated
by the trailing slash.

To copy a whole folder with all files, you can use
``--include-data-dir=/path/to/images=images`` which will copy all files
including a potential subdirectory structure. You cannot filter here,
i.e. if you want only a partial copy, remove the files beforehand.

For package data, there is a better way, using
``--include-package-data``, which detects data files of packages
automatically and copies them over. It even accepts patterns in a shell
style. It spares you the need to find the package directory yourself and
should be preferred whenever available.

With data files, you are largely on your own. Nuitka keeps track of ones
that are needed by popular packages, but it might be incomplete. Raise
issues if you encounter something in these.

When that is working, you can use the onefile mode if you so desire.

.. code:: bash

   python -m nuitka --onefile program.py

This will create a single binary, that extracts itself on the target,
before running the program. But notice, that accessing files relative to
your program is impacted, make sure to read the section `Onefile:
Finding files`_ as well.

.. code:: bash

   # Create a binary that unpacks into a temporary folder
   python -m nuitka --onefile program.py

.. note::

   There are more platform-specific options, e.g. related to icons,
   splash screen, and version information, consider the ``--help``
   output for the details of these and check the section Tweaks_.

For the unpacking, by default a unique user temporary path one is used,
and then deleted, however this default
``--onefile-tempdir-spec="{TEMP}/onefile_{PID}_{TIME}"`` can be
overridden with a path specification that is using then using a cached
path, avoiding repeated unpacking, e.g. with
``--onefile-tempdir-spec="{CACHE_DIR}/{COMPANY}/{PRODUCT}/{VERSION}"``
which uses version information, and user-specific cache directory.

.. note::

   Using cached paths will be relevant, e.g. when Windows Firewall comes
   into play because otherwise, the binary will be a different one to it
   each time it is run.

Currently, these expanded tokens are available:

+----------------+-----------------------------------------------------------+---------------------------------------+
| Token          | What this Expands to                                      | Example                               |
+================+===========================================================+=======================================+
| {TEMP}         | User temporary file directory                             | C:\\Users\\...\\AppData\\Locals\\Temp |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {PID}          | Process ID                                                | 2772                                  |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {TIME}         | Time in seconds since the epoch.                          | 1299852985                            |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {PROGRAM}      | Full program run-time filename of executable.             | C:\\SomeWhere\\YourOnefile.exe        |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {PROGRAM_BASE} | No-suffix of run-time filename of executable.             | C:\\SomeWhere\\YourOnefile            |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {CACHE_DIR}    | Cache directory for the user.                             | C:\\Users\\SomeBody\\AppData\\Local   |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {COMPANY}      | Value given as ``--company-name``                         | YourCompanyName                       |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {PRODUCT}      | Value given as ``--product-name``                         | YourProductName                       |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {VERSION}      | Combination of ``--file-version`` & ``--product-version`` | 3.0.0.0-1.0.0.0                       |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {HOME}         | Home directory for the user.                              | /home/somebody                        |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {NONE}         | When provided for file outputs, ``None`` is used          | see notice below                      |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {NULL}         | When provided for file outputs, ``os.devnull`` is used    | see notice below                      |
+----------------+-----------------------------------------------------------+---------------------------------------+

.. important::

   It is your responsibility to make the path provided unique, on
   Windows a running program will be locked, and while using a fixed
   folder name is possible, it can cause locking issues in that case,
   where the program gets restarted.

   Usually, you need to use ``{TIME}`` or at least ``{PID}`` to make a
   path unique, and this is mainly intended for use cases, where e.g.
   you want things to reside in a place you choose or abide your naming
   conventions.

.. important::

   For disabling output and stderr with ``--force-stdout-spec`` and
   ``--force-stderr-spec`` the values ``{NONE}`` and ``{NULL}`` achieve
   it, but with different effect. With ``{NONE}``, the corresponding
   handle becomes ``None``. As a result, e.g. ``sys.stdout`` will be
   ``None``, which is different from ``{NULL}`` where it will be backed
   by a file pointing to ``os.devnull``, i.e. you can write to it.

   With ``{NONE}``, you may e.g. get ``RuntimeError: lost sys.stdout``
   in case it does get used; with ``{NULL}`` that never happens.
   However, some libraries handle this as input for their logging
   mechanism, and on Windows this is how you are compatible with
   ``pythonw.exe`` which is behaving like ``{NONE}``.

Use Case 5 - Setuptools Wheels
==============================

If you have a ``setup.py``, ``setup.cfg`` or ``pyproject.toml`` driven
creation of wheels for your software in place, putting Nuitka to use is
extremely easy.

Let's start with the most common ``setuptools`` approach, you can,
having Nuitka installed of course, simply execute the target
``bdist_nuitka`` rather than the ``bdist_wheel``. It takes all the
options and allows you to specify some more, that are specific to
Nuitka.

.. code:: python

   # For setup.py if you don't use other build systems:
   setup(
      # Data files are to be handled by setuptools and not Nuitka
      package_data={"some_package": ["some_file.txt"]},
      ...,
      # This is to pass Nuitka options.
      command_options={
         'nuitka': {
            # boolean option, e.g. if you cared for C compilation commands
            '--show-scons': True,
            # options without value, e.g. enforce using Clang
            '--clang': None,
            # options with single values, e.g. enable a plugin of Nuitka
            '--enable-plugin': "pyside2",
            # options with several values, e.g. avoiding including modules
            '--nofollow-import-to' : ["*.tests", "*.distutils"],
         },
      },
   )

   # For setup.py with other build systems:
   # The tuple nature of the arguments is required by the dark nature of
   # "setuptools" and plugins to it, that insist on full compatibility,
   # e.g. "setuptools_rust"

   setup(
      # Data files are to be handled by setuptools and not Nuitka
      package_data={"some_package": ["some_file.txt"]},
      ...,
      # This is to pass Nuitka options.
      ...,
      command_options={
         'nuitka': {
            # boolean option, e.g. if you cared for C compilation commands
            '--show-scons': ("setup.py", True),
            # options without value, e.g. enforce using Clang
            '--clang': ("setup.py", None),
            # options with single values, e.g. enable a plugin of Nuitka
            '--enable-plugin': ("setup.py", "pyside2"),
            # options with several values, e.g. avoiding including modules
            '--nofollow-import-to' : ("setup.py", ["*.tests", "*.distutils"]),
         }
      },
   )

If for some reason, you cannot or do not want to change the target, you
can add this to your ``setup.py``.

.. code:: python

   # For setup.py
   setup(
      ...,
      build_with_nuitka=True
   )

.. note::

   To temporarily disable the compilation, you could the remove above
   line, or edit the value to ``False`` by or take its value from an
   environment variable if you so choose, e.g.
   ``bool(os.getenv("USE_NUITKA", "True"))``. This is up to you.

Or you could put it in your ``setup.cfg``

.. code:: toml

   [metadata]
   build_with_nuitka = true

And last, but not least, Nuitka also supports the new ``build`` meta, so
when you have a ``pyproject.toml`` already, simple replace or add this
value:

.. code:: toml

   [build-system]
   requires = ["setuptools>=42", "wheel", "nuitka", "toml"]
   build-backend = "nuitka.distutils.Build"

   # Data files are to be handled by setuptools and not Nuitka
   [tool.setuptools.package-data]
   some_package = ['data_file.txt']

   [tool.nuitka]
   # These are not recommended, but they make it obvious to have effect.

   # boolean option, e.g. if you cared for C compilation commands, leading
   # dashes are omitted
   show-scons = true

   # options with single values, e.g. enable a plugin of Nuitka
   enable-plugin = "pyside2"

   # options with several values, e.g. avoiding including modules, accepts
   # list argument.
   nofollow-import-to = ["*.tests", "*.distutils"]

.. note::

   For the ``nuitka`` requirement above absolute paths like
   ``C:\Users\...\Nuitka`` will also work on Linux, use an absolute path
   with *two* leading slashes, e.g. ``//home/.../Nuitka``.

.. note::

   Whatever approach you take, data files in these wheels are not
   handled by Nuitka at all, but by setuptools. You can, however, use
   the data file embedding of Nuitka commercial. In that case, you
   actually would embed the files inside the extension module itself,
   and not as a file in the wheel.

Use Case 6 - Multidist
======================

If you have multiple programs, that each should be executable, in the
past you had to compile multiple times, and deploy all of these. With
standalone mode, this, of course, meant that you were fairly wasteful,
as sharing the folders could be done, but wasn't really supported by
Nuitka.

Enter ``Multidist``. There is an option ``--main`` that replaces or adds
to the positional argument given. And it can be given multiple times.
When given multiple times, Nuitka will create a binary that contains the
code of all the programs given, but sharing modules used in them. They
therefore do not have to be distributed multiple times.

Let's call the basename of the main path, and entry point. The names of
these must, of course, be different. Then the created binary can execute
either entry point, and will react to what ``sys.argv[0]`` appears to
it. So if executed in the right way (with something like ``subprocess``
or OS API you can control this name), or by renaming or copying the
binary, or symlinking to it, you can then achieve the miracle.

This allows to combine very different programs into one.

.. note::

   This feature is still experimental. Use with care and report your
   findings should you encounter anything that is undesirable behavior

This mode works with standalone, onefile, and mere acceleration. It does
not work with module mode.

Use Case 7 - Building with GitHub Workflows
===========================================

For integration with GitHub workflows there is this `Nuitka-Action
<https://github.com/Nuitka/Nuitka-Action>`__ that you should use that
makes it really easy to integrate. You ought to start with a local
compilation though, but this will be easiest for cross platform
compilation with Nuitka.

This is an example workflow that builds on all 3 OSes

.. code:: yaml

   jobs:
   build:
      strategy:
         matrix:
         os: [macos-latest, ubuntu-latest, windows-latest]

      runs-on: ${{ matrix.os }}

      steps:
         - name: Check-out repository
         uses: actions/checkout@v3

         - name: Setup Python
         uses: actions/setup-python@v4
         with:
            python-version: '3.10'
            cache: 'pip'
            cache-dependency-path: |
               **/requirements*.txt

         - name: Install your Dependencies
         run: |
            pip install -r requirements.txt -r requirements-dev.txt

         - name: Build Executable with Nuitka
         uses: Nuitka/Nuitka-Action@main
         with:
            nuitka-version: main
            script-name: your_main_program.py
            # many more Nuitka options available, see action doc, but it's best
            # to use nuitka-project: options in your code, so e.g. you can make
            # a difference for macOS and create an app bundle there.
            onefile: true

         - name: Upload Artifacts
         uses: actions/upload-artifact@v3
         with:
            name: ${{ runner.os }} Build
            path: | # match what's created for the 3 OSes
               build/*.exe
               build/*.bin
               build/*.app/**/*

If you app is a GUI, e.g. ``your_main_program.py`` should contain these
comments as explained in `Nuitka Options in the code`_ since on macOS
this should then be a bundle.

.. code:: python

   # Compilation mode, standalone everywhere, except on macOS there app bundle
   # nuitka-project-if: {OS} in ("Windows", "Linux", "FreeBSD"):
   #    nuitka-project: --onefile
   # nuitka-project-if: {OS} == "Darwin":
   #    nuitka-project: --standalone
   #    nuitka-project: --macos-create-app-bundle
   #
   # Debugging options, controlled via environment variable at compile time.
   # nuitka-project-if: os.getenv("DEBUG_COMPILATION", "no") == "yes"
   #     nuitka-project: --enable-console
   # nuitka-project-else:
   #     nuitka-project: --disable-console

********
 Tweaks
********

Icons
=====

For good looks, you may specify icons. On Windows, you can provide an
icon file, a template executable, or a PNG file. All of these will work
and may even be combined:

.. code:: bash

   # These create binaries with icons on Windows
   python -m nuitka --onefile --windows-icon-from-ico=your-icon.png program.py
   python -m nuitka --onefile --windows-icon-from-ico=your-icon.ico program.py
   python -m nuitka --onefile --windows-icon-template-exe=your-icon.ico program.py

   # These create application bundles with icons on macOS
   python -m nuitka --macos-create-app-bundle --macos-app-icon=your-icon.png program.py
   python -m nuitka --macos-create-app-bundle --macos-app-icon=your-icon.icns program.py

.. note::

   With Nuitka, you do not have to create platform-specific icons, but
   instead it will convert e.g. PNG, but also other formats on the fly
   during the build.

MacOS Entitlements
==================

Entitlements for an macOS application bundle can be added with the
option, ``--macos-app-protected-resource``, all values are listed on
`this page from Apple
<https://developer.apple.com/documentation/bundleresources/information_property_list/protected_resources>`__

An example value would be
``--macos-app-protected-resource=NSMicrophoneUsageDescription:Microphone
access`` for requesting access to a Microphone. After the colon, the
descriptive text is to be given.

.. note::

   Beware that in the likely case of using spaces in the description
   part, you need to quote it for your shell to get through to Nuitka
   and not be interpreted as Nuitka arguments.

Console Window
==============

On Windows, the console is opened by programs unless you say so. Nuitka
defaults to this, effectively being only good for terminal programs, or
programs where the output is requested to be seen. There is a difference
in ``pythonw.exe`` and ``python.exe`` along those lines. This is
replicated in Nuitka with the option ``--disable-console``. Nuitka
recommends you to consider this in case you are using ``PySide6`` e.g.
and other GUI packages, e.g. ``wx``, but it leaves the decision up to
you. In case, you know your program is console application, just using
``--enable-console`` which will get rid of these kinds of outputs from
Nuitka.

.. note::

   The ``pythonw.exe`` is never good to be used with Nuitka, as you
   cannot see its output.

Splash screen
=============

Splash screens are useful when program startup is slow. Onefile startup
itself is not slow, but your program may be, and you cannot really know
how fast the computer used will be, so it might be a good idea to have
them. Luckily, with Nuitka, they are easy to add for Windows.

For the splash screen, you need to specify it as a PNG file, and then
make sure to disable the splash screen when your program is ready, e.g.
has completed the imports, prepared the window, connected to the
database, and wants the splash screen to go away. Here we are using the
project syntax to combine the code with the creation, compile this:

.. code:: python

   # nuitka-project: --onefile
   # nuitka-project: --onefile-windows-splash-screen-image={MAIN_DIRECTORY}/Splash-Screen.png

   # Whatever this is, obviously
   print("Delaying startup by 10s...")
   import time, tempfile, os
   time.sleep(10)

   # Use this code to signal the splash screen removal.
   if "NUITKA_ONEFILE_PARENT" in os.environ:
      splash_filename = os.path.join(
         tempfile.gettempdir(),
         "onefile_%d_splash_feedback.tmp" % int(os.environ["NUITKA_ONEFILE_PARENT"]),
      )

      if os.path.exists(splash_filename):
         os.unlink(splash_filename)

   print("Done... splash should be gone.")
   ...

   # Rest of your program goes here.

Reports
=======

For analysis of your program and Nuitka packaging, there is the
`Compilation Report`_ available. You can also make custom reports by
providing your template, with a few of them built-in to Nuitka. These
reports carry all the detail information, e.g. when a module was
attempted to be imported, but not found, you can see where that happens.
For bug reporting, it is very much recommended to provide the report.

Version Information
===================

You can attach copyright and trademark information, company name,
product name, and so on to your compilation. This is then used in
version information for the created binary on Windows, or application
bundle on macOS. If you find something that is lacking, please let us
know.

******************
 Typical Problems
******************

Deployment Mode
===============

By default, Nuitka compiles without ``--deployment`` which leaves a set
of safe guards and helpers on, that are aimed at debugging wrong uses of
Nuitka.

This is a new feature, and implements a bunch of protections and
helpers, that are documented here.

Fork bombs (self-execution)
---------------------------

So after compilation, ``sys.executable`` is the compiled binary. In case
of packages like ``multiprocessing``, ``joblib``, or ``loky`` what these
typically do is to expect to run from a full ``python`` with
``sys.executable`` and then to be able to use its options like ``-c
command`` or ``-m module_name`` and then be able to launch other code
temporarily or permanently as a service daemon.

With Nuitka however, this executes your program again, and puts these
arguments, in ``sys.argv`` where you maybe ignore them, and then you
fork yourself again to launch the helper daemons. Sometimes this ends up
spawning CPU count processes that spawn CPU count processes that... this
is called a fork bomb, and with almost all systems, that freezes them
easily to death.

That is why e.g. this happens with default Nuitka:

.. code::

   ./hello.dist/hello.bin -l fooL -m fooM -n fooN -o fooO -p
   Error, the program tried to call itself with '-m' argument. Disable with '--no-deployment-flag=self-execution'.

Your program may well have its own command line parsing, and not use an
unsupported package that does attempt to re-execute. In this case, you
need at *compile time* to use ``--no-deployment-flag=self-execution``
which disables this specific guard.

Misleading Messages
-------------------

Some packages output what they think is helpful information about what
the reason of a failed import might mean. With compiled programs there
are very often just plain wrong. We try and repair those in
non-deployment mode. Here is an example, where we change a message that
asks to pip install (which is not the issue) to point the user to the
include command that makes an ``imageio`` plugin work.

.. code:: yaml

   - module-name: 'imageio.core.imopen'
     anti-bloat:
       - replacements_plain:
           '`pip install imageio[{config.install_name}]` to install it': '`--include-module={config.module_name}` with Nuitka to include it'
           'err_type = ImportError': 'err_type = RuntimeError'
         when: 'not deployment'

And much more
-------------

The deployment mode is relatively new and has constantly more features
added, e.g. something for ``FileNotFoundError`` should be coming soon.

Disabling All
-------------

All these helpers can of course be disabled at once with
``--deployment`` but keep in mind that for debugging, you may want to
re-enable it. You might want to use Nuitka Project options and an
environment variable to make this conditional.

Should you disable them all?

We believe, disabling should only happen selectively, but with PyPI
upgrades, your code changes, all of these issues can sneak back in. The
space saving of deployment mode is currently negligible, so attempt to
not do it, but review what exists, and if you know that it cannot affect
you, or if it does, you will not need it. Some of the future ones, will
clearly be geared at beginner level usage.

Windows Virus scanners
======================

Binaries compiled on Windows with default settings of Nuitka and no
further actions taken might be recognized by some AV vendors as malware.
This is avoidable, but only in Nuitka commercial there is actual support
and instructions for how to do it, seeing this as a typical commercial
only need. https://nuitka.net/doc/commercial.html

Linux Standalone
================

For Linux standalone it is pretty difficult to build a binary that works
on other Linux versions. This is mainly because on Linux, much software
is built specifically targeted to concrete DLLs. Things like glibc used,
are then encoded into the binary built, and it will not run with an
older glibc, just to give one critical example.

The solution is to build on the oldest OS that you want to see
supported. Picking that and setting it up can be tedious, so can be
login, and keeping it secure, as it's something you put your source code
on.

To aid that, Nuitka commercial has container based builds, that you can
use. This uses dedicated optimized Python builds, targets CentOS 7 and
supports even newest Pythons and very old OSes that way using recent C
compiler chains all turn key solution. The effort needs to be
compensated to support Nuitka development for Linux, there you need to
purchase it https://nuitka.net/doc/commercial.html but even a sponsor
license will be cheaper than doing it yourself.

Program crashes system (fork bombs)
===================================

A fork bomb is a program that starts itself over and over. This can
easily happen, since ``sys.executable`` for compiled programs is not a
Python interpreter, and packages that try to do multiprocessing in a
better way, often relaunch themselves through this, and Nuitka needs and
does have handling for these with known packages. However, you may
encounter a situation where the detection of this fails. See deployment
option above that is needed to disable this protection.

When this fork bomb happens easily all memory, all CPU of the system
that is available to the user is being used, and even the most powerful
build system will go down in flames sometimes needing a hard reboot.

For fork bombs, we can use ``--experimental=debug-self-forking`` and see
what it does, and we have a trick, that prevents fork bombs from having
any actual success in their bombing. Put this at the start of your
program.

.. code:: python

   import os, sys

   if "NUITKA_LAUNCH_TOKEN" not in os.environ:
      sys.exit("Error, need launch token or else fork bomb suspected.")
   else:
      del os.environ["NUITKA_LAUNCH_TOKEN"]

Actually Nuitka is trying to get ahold of them without the deployment
option already, finding "-c" and "-m" options, but it may not be perfect
or not work well with a package (anymore).

Memory issues and compiler bugs
===============================

In some cases, the C compilers will crash saying they cannot allocate
memory or that some input was truncated, or similar error messages,
clearly from it. There are several options you can explore here:

Ask Nuitka to use less memory
-----------------------------

There is a dedicated option ``--low-memory`` which influences decisions
of Nuitka, such that it avoids high usage of memory during compilation
at the cost of increased compile time.

Avoid 32 bit C compiler/assembler memory limits
-----------------------------------------------

Do not use a 32 bit compiler, but a 64 bit one. If you are using Python
with 32 bits on Windows, you most definitely ought to use MSVC as the C
compiler, and not MinGW64. The MSVC is a cross-compiler, and can use
more memory than gcc on that platform. If you are not on Windows, that
is not an option, of course. Also, using the 64 bit Python will work.

Use a minimal virtualenv
------------------------

When you compile from a living installation, that may well have many
optional dependencies of your software installed. Some software will
then have imports on these, and Nuitka will compile them as well. Not
only may these be just the troublemakers, they also require more memory,
so get rid of that. Of course, you do have to check that your program
has all the needed dependencies before you attempt to compile, or else
the compiled program will equally not run.

Use LTO compilation or not
--------------------------

With ``--lto=yes`` or ``--lto=no`` you can switch the C compilation to
only produce bytecode, and not assembler code and machine code directly,
but make a whole program optimization at the end. This will change the
memory usage pretty dramatically, and if your error is coming from the
assembler, using LTO will most definitely avoid that.

Switch the C compiler to clang
------------------------------

People have reported that programs that fail to compile with gcc due to
its bugs or memory usage work fine with clang on Linux. On Windows, this
could still be an option, but it needs to be implemented first for the
automatic downloaded gcc, that would contain it. Since MSVC is known to
be more memory effective anyway, you should go there, and if you want to
use Clang, there is support for the one contained in MSVC.

Add a larger swap file to your embedded Linux
---------------------------------------------

On systems with not enough RAM, you need to use swap space. Running out
of it is possibly a cause, and adding more swap space, or one at all,
might solve the issue, but beware that it will make things extremely
slow when the compilers swap back and forth, so consider the next tip
first or on top of it.

Limit the amount of compilation jobs
------------------------------------

With the ``--jobs`` option of Nuitka, it will not start many C compiler
instances at once, each competing for the scarce resource of RAM. By
picking a value of one, only one C compiler instance will be running,
and on an 8 core system, that reduces the amount of memory by factor 8,
so that's a natural choice right there.

Dynamic ``sys.path``
====================

If your script modifies ``sys.path``, e.g. inserts directories with
source code relative to it, Nuitka will not be able to see those.
However, if you set the ``PYTHONPATH`` to the resulting value, it will
be able to compile it and find the used modules from these paths as
well.

Manual Python File Loading
==========================

A very frequent pattern with private code is that it scans plugin
directories of some kind, and e.g. uses ``os.listdir``, then considers
Python filenames, and then opens a file and does ``exec`` on them. This
approach works for Python code, but for compiled code, you should use
this much cleaner approach, that works for pure Python code and is a lot
less vulnerable.

.. code:: python

   # Using a package name, to locate the plugins. This is also a sane
   # way to organize them into a directory.
   scan_path = scan_package.__path__

   for item in pkgutil.iter_modules(scan_path):
      importlib.import_module(scan_package.__name__ + "." + item.name)

      # You may want to do it recursively, but we don't do this here in
      # this example. If you'd like to, handle that in this kind of branch.
      if item.ispkg:
         ...

Missing data files in standalone
================================

If your program fails to find data file, it can cause all kinds of
different behavior, e.g. a package might complain it is not the right
version because a ``VERSION`` file check defaulted to an unknown. The
absence of icon files or help texts, may raise strange errors.

Often the error paths for files not being present are even buggy and
will reveal programming errors like unbound local variables. Please look
carefully at these exceptions, keeping in mind that this can be the
cause. If your program works without standalone, chances are data files
might be the cause.

The most common error indicating file absence is of course an uncaught
``FileNotFoundError`` with a filename. You should figure out what
package is missing files and then use ``--include-package-data``
(preferably), or ``--include-data-dir``/``--include-data-files`` to
include them.

Missing DLLs/EXEs in standalone
===============================

Nuitka has plugins that deal with copying DLLs. For NumPy, SciPy,
Tkinter, etc.

These need special treatment to be able to run on other systems.
Manually copying them is not enough and will give strange errors.
Sometimes newer version of packages, esp. NumPy can be unsupported. In
this case, you will have to raise an issue, and use the older one.

If you want to manually add a DLL or an EXE because it is your project
only, you will have to use user Yaml files describing where they can be
found. This is described in detail with examples in the `Nuitka Package
Configuration <https://nuitka.net/doc/nuitka-package-config.html>`__
page.

Dependency creep in standalone
==============================

Some packages are a single import, but to Nuitka mean that more than a
thousand packages (literally) are to be included. The prime example of
Pandas, which does want to plug and use just about everything you can
imagine. Multiple frameworks for syntax highlighting everything
imaginable take time.

Nuitka will have to learn effective caching to deal with this in the
future. Presently, you will have to deal with huge compilation times for
these.

A major weapon in fighting dependency creep should be applied, namely
the ``anti-bloat`` plugin, which offers interesting abilities, that can
be put to use and block unneeded imports, giving an error for where they
occur. Use it e.g. like this ``--noinclude-pytest-mode=nofollow
--noinclude-setuptools-mode=nofollow`` and e.g. also
``--noinclude-custom-mode=setuptools:error`` to get the compiler to
error out for a specific package. Make sure to check its help output. It
can take for each module of your choice, e.g. forcing also that e.g.
``PyQt5`` is considered uninstalled for standalone mode.

It's also driven by a configuration file, ``anti-bloat.yml`` that you
can contribute to, removing typical bloat from packages. Please don't
hesitate to enhance it and make PRs towards Nuitka with it.

Standalone: Finding files
=========================

The standard code that normally works, also works, you should refer to
``os.path.dirname(__file__)`` or use all the packages like ``pkgutil``,
``pkg_resources``, ``importlib.resources`` to locate data files near the
standalone binary.

.. important::

   What you should **not** do, is use the current directory
   ``os.getcwd``, or assume that this is the script directory, e.g. with
   paths like ``data/``.

   If you did that, it was never good code. Links, to a program,
   launching from another directory, etc. will all fail in bad ways. Do
   not make assumptions about the directory your program is started
   from.

In case you mean to refer to the location of the ``.dist`` folder for
files that are to reside near the binary, there is
``__compiled__.containing_dir`` that also abstracts all differences with
``--macos-create-app-bundle`` and the ``.app`` folder a having more
nested structure.

.. code:: python

   # This will find a file *near* your app or dist folder
   try:
      open(os.path.join(__compiled__.containing_dir, "user-provided-file.txt"))
   except NameError:
      open(os.path.join(os.path.dirname(sys.argv[0]), "user-provided-file.txt"))

Onefile: Finding files
======================

There is a difference between ``sys.argv[0]`` and ``__file__`` of the
main module for the onefile mode, that is caused by using a bootstrap to
a temporary location. The first one will be the original executable
path, whereas the second one will be the temporary or permanent path the
bootstrap executable unpacks to. Data files will be in the later
location, your original environment files will be in the former
location.

Given 2 files, one which you expect to be near your executable, and one
which you expect to be inside the onefile binary, access them like this.

.. code:: python

   # This will find a file *near* your onefile.exe
   open(os.path.join(os.path.dirname(sys.argv[0]), "user-provided-file.txt"))
   # This will find a file *inside* your onefile.exe
   open(os.path.join(os.path.dirname(__file__), "user-provided-file.txt"))

   # This will find a file *near* your onefile binary and work for standalone too
   try:
      open(os.path.join(__compiled__.containing_dir, "user-provided-file.txt"))
   except NameError:
      open(os.path.join(os.path.dirname(sys.argv[0]), "user-provided-file.txt"))

Windows Programs without console give no errors
===============================================

For debugging purposes, remove ``--disable-console`` or use the options
``--force-stdout-spec`` and ``--force-stderr-spec`` with paths as
documented for ``--onefile-tempdir-spec`` above. These can be relative
to the program or absolute, so you can see the outputs given.

Deep copying uncompiled functions
=================================

Sometimes people use this kind of code, which for packages on PyPI, we
deal with by doing source code patches on the fly. If this is in your
own code, here is what you can do:

.. code:: python

   def binder(func, name):
      result = types.FunctionType(func.__code__, func.__globals__, name=func.__name__, argdefs=func.__defaults__, closure=func.__closure__)
      result = functools.update_wrapper(result, func)
      result.__kwdefaults__ = func.__kwdefaults__
      result.__name__ = name
      return result

Compiled functions cannot be used to create uncompiled ones from, so the
above code will not work. However, there is a dedicated ``clone``
method, that is specific to them, so use this instead.

.. code:: python

   def binder(func, name):
      try:
         result = func.clone()
      except AttributeError:
         result = types.FunctionType(func.__code__, func.__globals__, name=func.__name__, argdefs=func.__defaults__, closure=func.__closure__)
         result = functools.update_wrapper(result, func)
         result.__kwdefaults__ = func.__kwdefaults__

      result.__name__ = name
      return result

Modules: Extension modules are not executable directly
======================================================

A package can be compiled with Nuitka, no problem, but when it comes to
executing it, ``python -m compiled_module`` is not going to work and
give the error ``No code object available for AssertsTest`` because the
compiled module is not source code, and Python will not just load it.
The closest would be ``python -c "import compile_module"`` and you might
have to call the main function yourself.

To support this, the CPython ``runpy`` and/or ``ExtensionFileLoader``
would need improving such that Nuitka could supply its compiled module
object for Python to use.

******
 Tips
******

Nuitka Options in the code
==========================

One clean way of providing options to Nuitka, that you will always use
for your program, is to put them into the main file you compile. There
is even support for conditional options, and options using pre-defined
variables, this is an example:

.. code:: python

   # Compilation mode, support OS-specific options
   # nuitka-project-if: {OS} in ("Windows", "Linux", "Darwin", "FreeBSD"):
   #    nuitka-project: --onefile
   # nuitka-project-else:
   #    nuitka-project: --standalone

   # The PySide2 plugin covers qt-plugins
   # nuitka-project: --enable-plugin=pyside2
   # nuitka-project: --include-qt-plugins=qml

The comments must be at the start of lines, and indentation inside of
them is to be used, to end a conditional block, much like in Python.
There are currently no other keywords than the used ones demonstrated
above.

You can put arbitrary Python expressions there, and if you wanted to
e.g. access a version information of a package, you could simply use
``__import__("module_name").__version__`` if that would be required to
e.g. enable or disable certain Nuitka settings. The only thing Nuitka
does that makes this not Python expressions, is expanding ``{variable}``
for a pre-defined set of variables:

Table with supported variables:

+------------------+--------------------------------+------------------------------------------+
| Variable         | What this Expands to           | Example                                  |
+==================+================================+==========================================+
| {OS}             | Name of the OS used            | Linux, Windows, Darwin, FreeBSD, OpenBSD |
+------------------+--------------------------------+------------------------------------------+
| {Version}        | Version of Nuitka              | e.g. (1, 6, 0)                           |
+------------------+--------------------------------+------------------------------------------+
| {Commercial}     | Version of Nuitka Commercial   | e.g. (2, 1, 0)                           |
+------------------+--------------------------------+------------------------------------------+
| {Arch}           | Architecture used              | x86_64, arm64, etc.                      |
+------------------+--------------------------------+------------------------------------------+
| {MAIN_DIRECTORY} | Directory of the compiled file | some_dir/maybe_relative                  |
+------------------+--------------------------------+------------------------------------------+
| {Flavor}         | Variant of Python              | e.g. Debian Python, Anaconda Python      |
+------------------+--------------------------------+------------------------------------------+

The use of ``{MAIN_DIRECTORY}`` is recommended when you want to specify
a filename relative to the main script, e.g. for use in data file
options or user package configuration yaml files,

.. code:: python

   # nuitka-project: --include-data-files={MAIN_DIRECTORY}/my_icon.png=my_icon.png
   # nuitka-project: --user-package-configuration-file={MAIN_DIRECTORY}/user.nuitka-package.config.yml

Python command line flags
=========================

For passing things like ``-O`` or ``-S`` to Python, to your compiled
program, there is a command line option name ``--python-flag=`` which
makes Nuitka emulate these options.

The most important ones are supported, more can certainly be added.

Caching compilation results
===========================

The C compiler, when invoked with the same input files, will take a long
time and much CPU to compile over and over. Make sure you are having
``ccache`` installed and configured when using gcc (even on Windows). It
will make repeated compilations much faster, even if things are not yet
not perfect, i.e. changes to the program can cause many C files to
change, requiring a new compilation instead of using the cached result.

On Windows, with gcc Nuitka supports using ``ccache.exe`` which it will
offer to download from an official source and it automatically. This is
the recommended way of using it on Windows, as other versions can e.g.
hang.

Nuitka will pick up ``ccache`` if it's found in system ``PATH``, and it
will also be possible to provide if by setting ``NUITKA_CCACHE_BINARY``
to the full path of the binary, this is for use in CI systems where
things might be non-standard.

For the MSVC compilers and ClangCL setups, using the ``clcache`` is
automatic and included in Nuitka.

On macOS and Intel, there is an automatic download of a ``ccache``
binary from our site, for arm64 arches, it's recommended to use this
setup, which installs Homebrew and ccache in there. Nuitka picks that
one up automatically if it on that kind of machine. You need and should
not use Homebrew with Nuitka otherwise, it's not the best for standalone
deployments, but we can take ``ccache`` from there.

.. code:: bash

   export HOMEBREW_INSTALL_FROM_API=1
   /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install.sh)"
   eval $(/opt/homebrew/bin/brew shellenv)
   brew install ccache

Control where Caches live
=========================

The storage for cache results of all kinds, downloads, cached
compilation results from C and Nuitka, is done in a platform dependent
directory as determined by the ``appdirs`` package. However, you can
override it with setting the environment variable ``NUITKA_CACHE_DIR``
to a base directory. This is for use in environments where the home
directory is not persisted, but other paths are.

Runners
=======

Avoid running the ``nuitka`` binary, doing ``python -m nuitka`` will
make a 100% sure you are using what you think you are. Using the wrong
Python will make it give you ``SyntaxError`` for good code or
``ImportError`` for installed modules. That is happening, when you run
Nuitka with Python2 on Python3 code and vice versa. By explicitly
calling the same Python interpreter binary, you avoid that issue
entirely.

Fastest C Compilers
===================

The fastest binaries of ``pystone.exe`` on Windows with 64 bits Python
proved to be significantly faster with MinGW64, roughly 20% better
score. So it is recommended for use over MSVC. Using ``clang-cl.exe`` of
Clang7 was faster than MSVC, but still significantly slower than
MinGW64, and it will be harder to use, so it is not recommended.

On Linux, for ``pystone.bin``, the binary produced by ``clang6`` was
faster than ``gcc-6.3``, but not by a significant margin. Since gcc is
more often already installed, that is recommended to use for now.

Differences in C compilation times have not yet been examined.

Unexpected Slowdowns
====================

Using the Python DLL, like standard CPython does, can lead to unexpected
slowdowns, e.g. in uncompiled code that works with Unicode strings. This
is because calling to the DLL rather than residing in the DLL causes
overhead, and this even happens to the DLL with itself, being slower,
than a Python all contained in one binary.

So if feasible, aim at static linking, which is currently only possible
with Anaconda Python on non-Windows, Debian Python2, self compiled
Pythons (do not activate ``--enable-shared``, not needed), and installs
created with ``pyenv``.

.. note::

   On Anaconda, you may need to execute ``conda install
   libpython-static``

Standalone executables and dependencies
=======================================

The process of making standalone executables for Windows traditionally
involves using an external dependency walker to copy necessary libraries
along with the compiled executables to the distribution folder.

There are plenty of ways to find that something is missing. Do not
manually copy things into the folder, esp. not DLLs, as that's not going
to work. Instead, make bug reports to get these handled by Nuitka
properly.

Windows errors with resources
=============================

On Windows, the Windows Defender tool and the Windows Indexing Service
both scan the freshly created binaries, while Nuitka wants to work with
it, e.g. adding more resources, and then preventing operations randomly
due to holding locks. Make sure to exclude your compilation stage from
these services.

Windows standalone program redistribution
=========================================

Whether compiling with MingW or MSVC, the standalone programs have
external dependencies to Visual C Runtime libraries. Nuitka tries to
ship those dependent DLLs by copying them from your system.

Beginning with Microsoft Windows 10, Microsoft ships ``ucrt.dll``
(Universal C Runtime libraries) which handles calls to
``api-ms-crt-*.dll``.

With earlier Windows platforms (and wine/ReactOS), you should consider
installing Visual C runtime libraries before executing a Nuitka
standalone compiled program.

Depending on the used C compiler, you'll need the following redist
versions on the target machines. However, notice that compilation using
the 14.3 based version is recommended.

+------------------+-------------+-------------------------------+
| Visual C version | Redist Year | CPython                       |
+==================+=============+===============================+
| 14.3             | 2022        | 3.11                          |
+------------------+-------------+-------------------------------+
| 14.2             | 2019        | 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 |
+------------------+-------------+-------------------------------+
| 14.1             | 2017        | 3.5, 3.6, 3.7, 3.8            |
+------------------+-------------+-------------------------------+
| 14.0             | 2015        | 3.5, 3.6, 3.7, 3.8            |
+------------------+-------------+-------------------------------+
| 10.0             | 2010        | 3.4                           |
+------------------+-------------+-------------------------------+
| 9.0              | 2008        | 2.6, 2.7                      |
+------------------+-------------+-------------------------------+

When using MingGW64, you'll need the following redist versions:

+------------------+-------------+-------------------------------------+
| MingGW64 version | Redist Year | CPython                             |
+==================+=============+=====================================+
| 8.1.0            | 2015        | 3.5, 3.6, 3.7, 3.8, 3.9, 3.10, 3.11 |
+------------------+-------------+-------------------------------------+

Once the corresponding runtime libraries are installed on the target
system, you may remove all ``api-ms-crt-*.dll`` files from your Nuitka
compiled dist folder.

Detecting Nuitka at run time
============================

Nuitka does *not* ``sys.frozen`` unlike other tools because it usually
triggers inferior code for no reason. For Nuitka, we have the module
attribute ``__compiled__`` to test if a specific module was compiled,
and the function attribute ``__compiled__`` to test if a specific
function was compiled.

Providing extra Options to Nuitka C compilation
===============================================

Nuitka will apply values from the environment variables ``CCFLAGS``,
``LDFLAGS`` during the compilation on top of what it determines to be
necessary. Beware, of course, that is this is only useful if you know
what you are doing, so should this pose issues, raise them only with
perfect information.

Producing a 32 bit binary on a 64 bit Windows system
====================================================

Nuitka will automatically target the architecture of the Python you are
using. If this is 64 bit, it will create a 64 bit binary, if it is 32
bit, it will create a 32 bit binary. You have the option to select the
bits when you download the Python. In the output of ``python -m nuitka
--version`` there is a line for the architecture. It's ``Arch: x86_64``
for 64 bits, and just ``Arch: x86`` for 32 bits.

The C compiler will be picked to match that more or less automatically.
If you specify it explicitly, and it mismatches, you will get a warning
about the mismatch and informed that your compiler choice was rejected.

********************
 Compilation Report
********************

When you use ``--report=compilation-report.xml`` Nuitka will create an
XML file with detailed information about the compilation and packaging
process. This is growing in completeness with every release and exposes
module usage attempts, timings of the compilation, plugin influences,
data file paths, DLLs, and reasons why things are included or not.

At this time, the report contains absolute paths in some places, with
your private information. The goal is to make this blended out by
default because we also want to become able to compare compilation
reports from different setups, e.g. with updated packages, and see the
changes to Nuitka. The report is, however, recommended for your bug
reporting.

Also, another form is available, where the report is free form and
according to a Jinja2 template of yours, and one that is included in
Nuitka. The same information as used to produce the XML file is
accessible. However, right now, this is not yet documented, but we plan
to add a table with the data. For a reader of the source code that is
familiar with Jinja2, however, it will be easy to do it now already.

If you have a template, you can use it like this
``--report-template=your_template.rst.j2:your_report.rst`` and of
course, the usage of restructured text, is only an example. You can use
Markdown, your own XML, or whatever you see fit. Nuitka will just expand
the template with the compilation report data.

Currently, the following reports are included in Nuitka. You just use
the name as a filename, and Nuitka will pick that one instead.

+---------------+--------------+--------------------------------------------------------+
| Report Name   | Status       | Purpose                                                |
+===============+==============+========================================================+
| LicenseReport | experimental | Distributions used in a compilation with license texts |
+---------------+--------------+--------------------------------------------------------+

.. note::

   The community can and should contribute more report types and help
   enhancing the existing ones for good looks.

*************
 Performance
*************

This chapter gives an overview, of what to currently expect in terms of
performance from Nuitka. It's a work in progress and is updated as we
go. The current focus for performance measurements is Python 2.7, but
3.x is going to follow later.

pystone results
===============

The results are the top value from this kind of output, running pystone
1000 times and taking the minimal value. The idea is that the fastest
run is most meaningful, and eliminates usage spikes.

.. code:: bash

   echo "Uncompiled Python2"
   for i in {1..100}; do BENCH=1 python2 tests/benchmarks/pystone.py ; done | sort -rn | head -n 1
   python2 -m nuitka --lto=yes --pgo tests/benchmarks/pystone.py
   echo "Compiled Python2"
   for i in {1..100}; do BENCH=1 ./pystone.bin ; done | sort -n | head -rn 1

   echo "Uncompiled Python3"
   for i in {1..100}; do BENCH=1 python3 tests/benchmarks/pystone3.py ; done | sort -rn | head -n 1
   python3 -m nuitka --lto=yes --pgo tests/benchmarks/pystone3.py
   echo "Compiled Python3"
   for i in {1..100}; do BENCH=1 ./pystone3.bin ; done | sort -rn | head -n 1

+-------------------+-------------------+----------------------+---------------------+
| Python            | Uncompiled        | Compiled LTO         | Compiled PGO        |
+===================+===================+======================+=====================+
| Debian Python 2.7 | 137497.87 (1.000) | 460995.20 (3.353)    | 503681.91 (3.663)   |
+-------------------+-------------------+----------------------+---------------------+
| Nuitka Python 2.7 | 144074.78 (1.048) | 479271.51 (3.486)    | 511247.44 (3.718)   |
+-------------------+-------------------+----------------------+---------------------+

******************
 Where to go next
******************

Remember, this project needs constant work. Although the Python
compatibility is insanely high, and the test suite works near perfectly,
there is still more work needed, esp. to make it do more optimization.
Try it out, and when popular packages do not work, please make reports
on GitHub.

Follow me on Mastodon and Twitter
=================================

Nuitka announcements and interesting stuff is pointed to on both the
Mastodon and Twitter accounts, but obviously with not too many details,
usually pointing to the website, but sometimes I also ask questions
there.

`@KayHayen on Mastodon <https://fosstodon.org/@kayhayen>`_. `@KayHayen
on Twitter <https://twitter.com/KayHayen>`_.

Report issues or bugs
=====================

Should you encounter any issues, bugs, or ideas, please visit the
`Nuitka bug tracker <https://github.com/Nuitka/Nuitka/issues>`__ and
report them.

Best practices for reporting bugs:

-  Please always include the following information in your report, for
   the underlying Python version. You can easily copy&paste this into
   your report. It does contain more information than you think. Do not
   write something manually. You may always add, of course,

   .. code:: bash

      python -m nuitka --version

-  Try to make your example minimal. That is, try to remove code that
   does not contribute to the issue as much as possible. Ideally, come
   up with a small reproducing program that illustrates the issue, using
   ``print`` with different results when the program runs compiled or
   native.

-  If the problem occurs spuriously (i.e. not each time), try to set the
   environment variable ``PYTHONHASHSEED`` to ``0``, disabling hash
   randomization. If that makes the problem go away, try increasing in
   steps of 1 to a hash seed value that makes it happen every time,
   include it in your report.

-  Do not include the created code in your report. Given proper input,
   it's redundant, and it's not likely that I will look at it without
   the ability to change the Python or Nuitka source and re-run it.

-  Do not send screenshots of text, that is bad and lazy. Instead,
   capture text outputs from the console.

Word of Warning
===============

Consider using this software with caution. Even though many tests are
applied before releases, things are potentially breaking. Your feedback
and patches to Nuitka are very welcome.

*************
 Join Nuitka
*************

You are more than welcome to join Nuitka development and help to
complete the project in all minor and major ways.

The development of Nuitka occurs in git. We currently have these 3
branches:

-  ``main``

   This branch contains the stable release, to which only hotfixes for
   bugs will be done. It is supposed to work at all times and is
   supported.

-  ``develop``

   This branch contains the ongoing development. It may at times contain
   little regressions, but also new features. On this branch, the
   integration work is done, whereas new features might be developed on
   feature branches.

-  ``factory``

   This branch contains unfinished and incomplete work. It is very
   frequently subject to ``git rebase`` and the public staging ground,
   where my work for develop branch lives first. It is intended for
   testing only, and it's recommended to base any of your own
   development on. When updating it, you will very often get merge
   conflicts. Simply resolve those by doing ``git fetch && git reset
   --hard origin/factory`` and switch to the latest version.

.. note::

   The `Developer Manual
   <https://nuitka.net/doc/developer-manual.html>`__ explains the coding
   rules, branching model used, with feature branches and hotfix
   releases, the Nuitka design and much more. Consider reading it to
   become a contributor. This document is intended for Nuitka users.

***********
 Donations
***********

Should you feel that you cannot help Nuitka directly, but still want to
support, please consider `making a donation
<https://nuitka.net/pages/donations.html>`__ and help this way.

***************************
 Unsupported functionality
***************************

The ``co_code`` attribute of code objects
=========================================

The code objects are empty for native compiled functions. There is no
bytecode with Nuitka's compiled function objects, so there is no way to
provide it.

PDB
===

There is no tracing of compiled functions to attach a debugger to.

**************
 Optimization
**************

Constant Folding
================

The most important form of optimization is the constant folding. This is
when an operation can be fully predicted at compile time. Currently,
Nuitka does these for some built-ins (but not all yet, somebody to look
at this more closely will be very welcome!), and it does it e.g. for
binary/unary operations and comparisons.

Constants currently recognized:

.. code:: python

   5 + 6  # binary operations
   not 7  # unary operations
   5 < 6  # comparisons
   range(3)  # built-ins

Literals are the one obvious source of constants, but also most likely
other optimization steps like constant propagation or function inlining
will be. So this one should not be underestimated and a crucial step of
successful optimizations. Every option to produce a constant may impact
the generated code quality a lot.

.. admonition:: Status

   The folding of constants is considered implemented, but it might be
   incomplete in that not all possible cases are caught. Please report
   it as a bug when you find an operation in Nuitka that has only
   constants as input and is not folded.

Constant Propagation
====================

At the core of optimizations, there is an attempt to determine the
values of variables at run time and predictions of assignments. It
determines if their inputs are constants or of similar values. An
expression, e.g. a module variable access, an expensive operation, may
be constant across the module of the function scope and then there needs
to be none or no repeated module variable look-up.

Consider e.g. the module attribute ``__name__`` which likely is only
ever read, so its value could be predicted to a constant string known at
compile time. This can then be used as input to the constant folding.

.. code:: python

   if __name__ == "__main__":
       # Your test code might be here
       use_something_not_use_by_program()

.. admonition:: Status

   From modules attributes, only ``__name__`` is currently actually
   optimized. At least ``__doc__`` would also be possible to. In the
   future, this may improve as SSA is expanded to module variables.

Built-in Name Lookups
=====================

Also, built-in exception name references are optimized if they are used
as a module level read-only variables:

.. code:: python

   try:
       something()
   except ValueError:  # The ValueError is a slow global name lookup normally.
       pass

.. admonition:: Status

   This works for all built-in names. When an assignment is done to such
   a name, or it's even local, then, of course, it is not done.

Built-in Call Prediction
========================

For built-in calls like ``type``, ``len``, or ``range`` it is often
possible to predict the result at compile time, esp. for constant inputs
the resulting value often can be precomputed by Nuitka. It can simply
determine the result or the raised exception and replace the built-in
call with that value, allowing for more constant folding or code path
reduction.

.. code:: python

   type("string")  # predictable result, built-in type str.
   len([1, 2])  # predictable result
   range(3, 9, 2)  # predictable result
   range(3, 9, 0)  # predictable exception, range raises due to 0.

.. admonition:: Status

   The built-in call prediction is considered implemented. We can simply
   during compile time emulate the call and use its result or raised
   exception. But we may not cover all the built-ins there are yet.

Sometimes the result of a built-in should not be predicted when the
result is big. A ``range()`` call e.g. may give too big values to
include the result in the binary. Then it is not done.

.. code:: python

   range(100000)  # We do not want this one to be expanded

.. admonition:: Status

   This is considered mostly implemented. Please file bugs for built-ins
   that are pre-computed, but should not be computed by Nuitka at
   compile time with specific values.

Conditional Statement Prediction
================================

For conditional statements, some branches may not ever be taken, because
of the condition truth value being possible to predict. In these cases,
the branch is not taken, and the condition check is removed.

This can typically predict code like this:

.. code:: python

   if __name__ == "__main__":
       # Your test code might be here
       use_something_not_use_by_program()

or

.. code:: python

   if False:
       # Your deactivated code might be here
       use_something_not_use_by_program()

It will also benefit from constant propagations, or enable them because
once some branches have been removed, other things may become more
predictable, so this can trigger other optimization to become possible.

Every branch removed makes optimization more likely. With some code
branches removed, access patterns may be more friendly. Imagine e.g.
that a function is only called in a removed branch. It may be possible
to remove it entirely, and that may have other consequences too.

.. admonition:: Status

   This is considered implemented, but for the maximum benefit, more
   constants need to be determined at compile time.

Exception Propagation
=====================

For exceptions that are determined at compile time, there is an
expression that will simply do raise the exception. These can be
propagated upwards, collecting potentially "side effects", i.e. parts of
expressions that were executed before it occurred, and still have to be
executed.

Consider the following code:

.. code:: python

   print(side_effect_having() + (1 / 0))
   print(something_else())

The ``(1 / 0)`` can be predicted to raise a ``ZeroDivisionError``
exception, which will be propagated through the ``+`` operation. That
part is just Constant Propagation as normal.

The call ``side_effect_having()`` will have to be retained, though, but
the ``print`` does not and can be turned into an explicit raise. The
statement sequence can then be aborted, and as such the
``something_else`` call needs no code generation or consideration
anymore.

To that end, Nuitka works with a special node that raises an exception
and is wrapped with a so-called "side_effects" expression, yet can be
used in the code as an expression having a value.

.. admonition:: Status

   The propagation of exceptions is mostly implemented but needs
   handling in every kind of operation, and not all of them might do it
   already. As work progresses or examples arise, the coverage will be
   extended. You are welcome to generate bug reports with non-working
   examples.

Exception Scope Reduction
=========================

Consider the following code:

.. code:: python

   try:
       b = 8
       print(range(3, b, 0))
       print("Will not be executed")
   except ValueError as e:
       print(e)

The ``try`` block is bigger than it needs to be. The statement ``b = 8``
cannot cause a ``ValueError`` to be raised. As such, it can be moved to
outside the try without any risk.

.. code:: python

   b = 8
   try:
       print(range(3, b, 0))
       print("Will not be executed")
   except ValueError as e:
       print(e)

.. admonition:: Status

   This is considered done. For every kind of operation, we trace if it
   may raise an exception. We do however *not* properly track yet, what
   can do a ``ValueError`` and what cannot.

Exception Block Inlining
========================

With the exception propagation, it then becomes possible to transform
this code:

.. code:: python

   try:
       b = 8
       print(range(3, b, 0))
       print("Will not be executed!")
   except ValueError as e:
       print(e)

.. code:: python

   try:
       raise ValueError("range() step argument must not be zero")
   except ValueError as e:
       print(e)

Which then can be lowered in complexity by avoiding the raise and catch
of the exception, making it:

.. code:: python

   e = ValueError("range() step argument must not be zero")
   print(e)

.. admonition:: Status

   This is not implemented yet.

Empty Branch Removal
====================

For loops and conditional statements that contain only code without
effect, it should be possible to remove the whole construct:

.. code:: python

   for i in range(1000):
       pass

The loop could be removed, at maximum, it should be considered an
assignment of variable ``i`` to ``999`` and no more.

.. admonition:: Status

   This is not implemented yet, as it requires us to track iterators,
   and their side effects, as well as loop values, and exit conditions.
   Too much yet, but we will get there.

Another example:

.. code:: python

   if side_effect_free:
       pass

The condition check should be removed in this case, as its evaluation is
not needed. It may be difficult to predict that ``side_effect_free`` has
no side effects, but many times this might be possible.

.. admonition:: Status

   This is considered implemented. The conditional statement nature is
   removed if both branches are empty, only the condition is evaluated
   and checked for truth (in cases that could raise an exception).

Unpacking Prediction
====================

When the length of the right-hand side of an assignment to a sequence
can be predicted, the unpacking can be replaced with multiple
assignments.

.. code:: python

   a, b, c = 1, side_effect_free(), 3

.. code:: python

   a = 1
   b = side_effect_free()
   c = 3

This is of course only really safe if the left-hand side cannot raise an
exception while building the assignment targets.

We do this now, but only for constants because we currently have no
ability to predict whether an expression can raise an exception or not.

.. admonition:: Status

   This is partially implemented. We are working on unpacking
   enhancements, that will recognize where index access is available.
   This faster access will then avoid tuples and iteration, then this
   will be perfect.

Built-in Type Inference
=======================

When a construct like ``in xrange()`` or ``in range()`` is used, it is
possible to know what the iteration does and represent that so that
iterator users can use that instead.

I consider that:

.. code:: python

   for i in xrange(1000):
       something(i)

could translate ``xrange(1000)`` into an object of a special class that
does the integer looping more efficiently. In case ``i`` is only
assigned from there, this could be a nice case for a dedicated class.

.. admonition:: Status

   Future work, not even started.

Quicker Function Calls
======================

Functions are structured so that their parameter parsing and ``tp_call``
interface is separate from the actual function code. This way the call
can be optimized away. One problem is that the evaluation order can
differ.

.. code:: python

   def f(a, b, c):
       return a, b, c


   f(c=get1(), b=get2(), a=get3())

This will have to evaluate first ``get1()``, then ``get2()`` and only
then ``get3()`` and then make the function call with these values.

Therefore, it will be necessary to have a staging of the parameters
before making the actual call, to avoid a re-ordering of the calls to
``get1()``, ``get2()``, and ``get3()``.

.. admonition:: Status

   Not even started. A re-formulation that avoids the dictionary to call
   the function, and instead uses temporary variables appears to be
   relatively straightforward once we do that kind of parameter
   analysis.

Lowering of iterated Container Types
====================================

In some cases, accesses to ``list`` constants can become ``tuple``
constants instead.

Consider that:

.. code:: python

   for x in [a, b, c]:
       something(x)

This can be optimized into this:

.. code:: python

   for x in (a, b, c):
       something(x)

This allows for simpler, faster code to be generated, and fewer checks
needed because e.g. the ``tuple`` is clearly immutable, whereas the
``list`` needs a check to assert that. This is also possible for sets.

.. admonition:: Status

   Implemented, even works for non-constants. Needs other optimization
   to become generally useful, and will itself help other optimization
   to become possible. This allows us to e.g. only treat iteration over
   tuples, and not care about sets.

In theory, something similar is also possible for ``dict``. For the
latter, it will be non-trivial though to maintain the order of execution
without temporary values introduced. The same thing is done for pure
constants of these types, they change to ``tuple`` values when iterated.

Metadata calls at compile time
==============================

Nuitka does not include metadata in the distribution. It's rather large,
and the goal is to use it at compile time. Therefore, information about
entry points, version checks, etc. are all done at compile time rather
than at run time. Not only is that faster, it also recognized problems
sooner.

.. code:: python

   pkg_resources.require("lxml")
   importlib.metadata.version("lxml")
   ...

.. admonition:: Status

   This is considered complete. The coverage of the APIs is very good,
   but naturally this will always have to be code that uses compile time
   values, but that is nearly never an issue, and where it happens, we
   use "anti-bloat" patches to deal with these in 3rd party packages.

*************************
 Updates for this Manual
*************************

This document is written in REST. That is an ASCII format which is
readable by human, but easily used to generate PDF or HTML documents.

You will find the current version at:
https://nuitka.net/doc/user-manual.html
