.. image:: https://img.shields.io/pypi/pyversions/Nuitka.svg
:target: https://pypi.org/project/Nuitka

.. image:: https://badge.fury.io/py/Nuitka.svg
:target: https://pypi.org/project/Nuitka

.. image:: https://img.shields.io/badge/Contributor%20Covenant-v1.4%20adopted-ff69b4.svg
:target: CODE_OF_CONDUCT.md

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

This document is the recommended first read when you start using
Nuitka. On this page, you will learn more about Nuitka
fundamentals, such as license type, use cases, requirements, and
credits.

.. contents:: Table of Contents
:depth: 1
:local:
:class: page-toc

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 Python 2 (2.6, 2.7) and Python 3 (3.4 - 3.13)
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

To ensure smooth operation of Nuitka, make sure to follow system
requirements, that include the following components:

:depth: 1
:local:

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 [#]_. 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 older MSVC compilers don't do it yet. But as a workaround, with
Python 3.10 or older, the C++03 language standard is significantly
overlapping with C11, it is then used instead.

.. [#]

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 2 (2.6, 2.7) and Python 3 (3.4 — 3.13) 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, 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 --mode=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 --mode=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.

.. important::

Nuitka does not consider data files code, do not include DLLs, or
Python files as data files, and expect them to work, they will not,
unless you really know what you are doing.

In the following, non-code data files are all files, not matching on of
these criterions.

+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| Suffix | Rationale | Solution |
+============+========================================================================================+========================================================================================================+
| .py | Nuitka trims even the stdlib modules to be included. If it doesn't see Python code, | Use --include-module on them instead |
| | there is no dependencies analyzed, and as a result it will just not work. | |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .pyc | Same as .py. | Use --include-module on them from their source code instead. |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .pyo | Same as .pyc. | Use --include-module on them from their source code instead. |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .pyw | Same as .py. | For including multiple programs, use multiple --main arguments instead. |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .pyi | These are ignored, because they are code-like and not needed at run time. For the | Raise an issue if 3rd part software needs it. |
| | lazy package that actually would depend on them, we made a compile time solution | |
| | that removes the need. | |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .pyx | These are ignored, because they are Cython source code not used at run time | |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .dll | These are ignored, since they usually are not data files. For the cases where 3rd | Create Nuitka Package configuration for those, with dll section for the package that uses them. |
| | party packages do actually used them as data, e.g. .NET packages, we solve that in | For rare cases, data-files section with special configuration might be the correct thing to do. |
| | package configuration for it. | |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .dylib | These are ignored, since they macOS extension modules or DLLs. | Need to add configuration with dll section or depends that are missing |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .so | These are ignored, since they Linux, BSD, etc. extension modules or DLLs. | Need to add configuration with dll section or depends that are missing |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .exe | The are binaries to Windows. | You can add Nuitka Package configuration to include those as DLLs and mark them as executable: yes |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+
| .bin | The are binaries to non-Windows, otherwise same as .exe. | |
+------------+----------------------------------------------------------------------------------------+--------------------------------------------------------------------------------------------------------+

Also folders are ignored, these are site-packages, dist-packages
and vendor-packages which would otherwise include a full virtualenv,
which is never a good thing to happen. And the __pycache__ folder is
also always ignored. On non-MacOS the file .DS_Store is ignored too,
and py.typed folders have only meaning to IDEs, and are ignored like
.pyi files .

To copy a whole folder with all non-code files, you can use
--include-data-dir=/path/to/images=images which will place those in
the destination, and if you want to use the --noinclude-data-files
option to remove them. Code files are as detailed above DLLs,
executables, Python files, etc. and will be ignored. For those you can
use the --include-data-files=/binaries/*.exe=binary/ form to force
them, but that is not recommended and known to cause issues at run-time.

For package data, there is a better way, namely using
--include-package-data, which detects all non-code 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. Functionally it's
very similar to --include-data-dir but it has the benefit to locate
the correct folder for you.

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. Even better, raise PRs with
enhancements to the Nuitka package configuration. We want 3rd party
software to just work out of the box.

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, 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 |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {PROGRAM_DIR} | Containing directory of executable. | C:\SomeWhere |
+----------------+-----------------------------------------------------------+---------------------------------------+
| {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. avoid 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. avoid 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. avoid 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@v4

     - name: Setup Python
     uses: actions/setup-python@v5
     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.
        mode: onefile

     - name: Upload Artifacts
     uses: actions/upload-artifact@v4
     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

.. note::

This is best expression with nuitka-project: --mode=app which
encapsulates these options.

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 not opened by programs unless you say so.
Nuitka defaults to not show it, you can force it by using
--console=force though, then the program will open a new terminal
Window when its executed.

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: --mode=onefile

nuitka-project: --mode=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. These are example error messages, that are a sure sign
of too low memory, there is no end to them.

.. code::

gcc

fatal error: error writing to -: Invalid argument
Killed signal terminated program

MSVC

fatal error C1002: compiler is out of heap space in pass 2
fatal error C1001: Internal compiler error

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"))

.. note::

When the program is launched from the executable, the original
sys.argv[0] from the invocation command line will not be
preserved, it will be made an absolute path.

For advanced use cases where one needs access to the original
sys.argv[0], it may be found at __compiled__.original_argv0.
The field will read back as None if the program is not launched
from the onefile executable, thus not having gone through the onefile
bootstrap stage; the original sys.argv[0] would be preserved as
well in this case.

.. code:: python

Suppose the onefile binary is placed at /opt/abc/bin/foo, and it was

symlinked to /usr/local/bin/bar, and invoked as `bar ...`:

assert sys.argv[0] == "/usr/local/bin/bar"
assert compiled.original_argv0 == "bar"

If the onefile tempdir is overridden and the program is invoked

directly from the unpacked location, sys.argv[0] would not be touched.

Suppose the onefile tempdir is /home/xx/.cache/abc/0.1.2, and the

foo.bin executable inside is symlinked to /usr/local/bin/baz, and

invoked as `baz ...`:

assert sys.argv[0] == "baz"
assert compiled.original_argv0 is None

Windows Programs without console give no errors

For debugging purposes, 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. Also you can run the
program on a terminal prompt like CMD.exe to see its outputs.

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: --mode=app

nuitka-project-else:

nuitka-project: --mode=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
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 install it
automatically. This is the recommended way of using it on Windows, as
other versions may cause issues e.g. hanging.

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
environments 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.

There is also per cache control of these caches, here is a table of
environment variables that you can set before starting the compilation,
to make Nuitka store some of these caches in an entirely separate space.

+------------------+-----------------------------------+----------------------------------------+
| Cache name | Environment Variable | Data Put there |
+==================+===================================+========================================+
| downloads | NUITKA_CACHE_DIR_DOWNLOADS | Downloads made, e.g. dependency walker |
+------------------+-----------------------------------+----------------------------------------+
| ccache | NUITKA_CACHE_DIR_CCACHE | Object files created by gcc |
+------------------+-----------------------------------+----------------------------------------+
| clcache | NUITKA_CACHE_DIR_CLCACHE | Object files created by MSVC |
+------------------+-----------------------------------+----------------------------------------+
| bytecode | NUITKA_CACHE_DIR_BYTECODE | Bytecode of demoted modules |
+------------------+-----------------------------------+----------------------------------------+
| dll-dependencies | NUITKA_CACHE_DIR_DLL_DEPENDENCIES | DLL dependencies |
+------------------+-----------------------------------+----------------------------------------+

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 always recommended, working and best
supported, unless you want to target Windows 7.

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

When using MingGW64 as downloaded by Nuitka, you'll need the following
redist versions:

+----------------------------+-------------+---------------------+
| MingGW64 version | Redist Year | CPython |
+============================+=============+=====================+
| WinLibs automatic download | 2015 | 2.6, 2.7, 3.4- 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 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-c tests/benchmarks/pystone.py
echo "Compiled Python2"
for i in {1..100}; do BENCH=1 ./pystone.bin ; done | sort -n | head -rn 1

PYTHON3=python3.10

Using a 100 gives semi-reliable values already, 1000 for best accuracy.

RUNS=1000

$PYTHON3 -m nuitka --lto=yes --pgo-c --static-libpython=yes tests/benchmarks/pystone3.py
echo "Uncompiled $PYTHON3"
for i in $(seq 1 $RUNS); do BENCH=1 $PYTHON3 tests/benchmarks/pystone3.py ; done | sort -rn | head -n 1
echo "Compiled $PYTHON3"
for i in $(seq 1 $RUNS); 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) |
+-------------------+-------------------+----------------------+---------------------+

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.

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.

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