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, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10 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.

Usage

Requirements

C Compiler: You need a compiler with support for C11 or alternatively for C++03 1

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 2, 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.
Python: Version 2.6, 2.7 or 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10

For Python 3.3/3.4 and only those, 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 only. 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 (on Windows via registry) and you shouldn’t notice it as long as they are installed.

Moving binaries to other machines

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

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 do an overwrite without destroying the original source file.

It has to be CPython, Anaconda Python.

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

It cannot be from Windows app store

It is known that Windows app store Python definitely does not work, it’s checked against. And on macOS “pyenv” likely does not work.
Operating System: Linux, FreeBSD, NetBSD, macOS X, and Windows (32/64 bits).

Others may work as well. The portability is expected to be generally good, but the e.g. Scons usage may have to be adapted. Make sure to match Windows Python and C compiler architecture, or else you will get cryptic error messages.
Architectures: x86, x86_64 (amd64), and arm, likely 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.

1

Support for this C11 is a 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 very 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.

2

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 pre-Windows 10 support, and they might work for you, but support of these configurations is only available to commercial users.

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.

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:

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:

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 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.

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 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 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

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.

python hello.py

Build it using

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:

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 less 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:

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, because they e.g. depend on command line parameters. Nuitka also warns about these, and point to the option.

Note

The resulting filename will be program.exe on Windows, program.bin on other platforms.

Note

The resulting binary still depend 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:

python -m nuitka --module some_module.py

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

Note

It’s left as an exercise to the reader, to find out what happens if both are present.

Note

The option --follow-imports and other variants work as well, but the included modules will only become importable after you imported the some_module name.

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:

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 empty. You can be more specific if you want, and only include part of it. Data files located inside the package will not be embedded by this process, you need to copy them yourself with this approach.

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.

python -m nuitka --standalone program.py

Follow 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 runtime.

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

To copy some or all file in a directory, use the option --include-data-file=/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 shell style.

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.

python -m nuitka --onefile program.py

This will create a single binary, which on Linux will not even unpack itself, but instead loop back mount its contents as a filesystem and use that.

# 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 “Good Looks”.

Again, on Windows, for the temporary file directory, by default the user one is used, however this is overridable with a path specification given in --windows-onefile-tempdir-spec=%TEMP%\\onefile_%PID%_%TIME% which is the default and asserts that the temporary directories created cannot collide.

Currently these expanded tokens are available:

Token	What this Expands to	Example
%TEMP%	User temporary file directory	C:Users...AppDataLocalsTemp
%PID%	Process ID	2772
%TIME%	Time in seconds since the epoch.	1299852985
%PROGRAM%	Full program filename of executable.	C:SomeWhereYourOnefile.exe

Note

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.

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.

Lets 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.

# For setup.py if not you't use other build systems:
setup(
   ...,
   command_options={
      'nuitka': {
         # boolean option, e.g. if you cared for C commands
         '--show-scons': 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': 'anti-bloat',
         # 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(
   ...,
   command_options={
      'nuitka': {
         # boolean option, e.g. if you cared for C 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", 'anti-bloat'),
         # 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 what to change the target, you can add this to your setup.py.

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

Note

To temporarily disable the compilation, you could 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.environ.get("USE_NUITKA", "True")). This is up to you.

Or you could put it in your setup.cfg

[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:

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

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:

# These create binaries with icons:
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

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 splash screen, you need to specify it as an PNG file, and then make sure to disable the splash screen when your program is ready, e.g. has complete 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:

# 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
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.

Typical Problems

Memory issues and compiler bugs

Sometimes 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 influces 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 bits 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 bits Python will work.

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 you 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 a 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 to e.g. insert directories with source code relative to it, Nuitka will currently not be able to see those. However, if you set the PYTHONPATH to the resulting value, you will be able to compile it.

Missing data files in standalone

If your program fails to file data, it can cause all kinds of different behaviours, e.g. a package might complain it is not the right version, because a VERSION file check defaulted to 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 you program works without standalone, chances are data files might be cause.

Missing DLLs 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 given 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.

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. Right now, you will have to deal with huge compilation times for these.

For now, a major weapon in fighting dependency creap 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 --enable-plugin=anti-bloat --noinclude-pytest-mode=nofollow --noinclude-setuptools-mode=nofollow and check its help output. It can take for each module of your choice, e.g. forcing also that PyQt5 is considered uninstalled for standalone mode.

Onefile: Finding files

There is a difference between sys.argv[0] and __file__ of the main module for onefile more, that is caused by using a bootstrap to a temporary location. The first one will be the original executable path, where as 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.

# 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"))

Windows Programs without console give no errors

For debugging purposes, remove --windows-disable-console or use the options --windows-force-stdout-spec and --windows-force-stderr-spec with paths as documented for --windows-onefile-tempdir-spec above.

Tips

Nuitka Options in the code

There is support for conditional options, and options using pre-defined variables, this is an example:

# Compilation mode, support OS specific.
# nuitka-project-if: {OS} in ("Windows", "Linux", "Darwin", "FreeBSD"):
#    nuitka-project: --onefile
# nuitka-project-if: {OS} not in ("Windows", "Linux", "Darwin", "FreeBSD"):
#    nuitka-project: --standalone

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

The comments must be a start of line, and indentation 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 abitrary 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. (0, 6, 16)
{Commercial}	Version of Nuitka Commercial	e.g. (0, 9, 4)
{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

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 in 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.

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

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 -c conda-forge libpython-static

Standalone executables and dependencies

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

There is 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 redistribuation

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 rehook 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:

Visual C version	Redist Year	CPython
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	3.3, 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

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

It doesn’t set sys.frozen unlike other tools. For Nuitka, we have the module attribute __compiled__ to test if a specific module was compiled.

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 meanigful, and eliminates usage spikes.

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

echo "Uncompiled Python3"
for i in {1..100}; do BENCH=1 python3 tests/benchmarks/pystone3.py ; done | sort -n -r | head -n 1
python3 -m nuitka --lto=yes --pgo=yes tests/benchmarks/pystone3.py
echo "Compiled Python3"
for i in {1..100}; do BENCH=1 ./pystone3.bin ; done | sort -n -r | 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 is not completed yet. Although the CPython test suite works near perfect, there is still more work needed, esp. to make it do more optimization. Try it out.

Follow me on Twitter

Nuitka announcements and interesting stuff is pointed to on the Twitter account, but obviously with not too many details. @KayHayen.

Report issues or bugs

Should you encounter any issues, bugs, or ideas, please visit the Nuitka bug tracker 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.
```
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 that programs 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 recommended to base any of your own development on. When updating it, you very often will get merge conflicts. Simply resolve those by doing git reset --hard origin/factory and switch to the latest version.

Note

The Developer Manual 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 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:

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 very important step of successful optimizations. Every option to produce a constant may impact the generated code quality a lot.

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.

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

Status

From modules attributes, only __name__ is currently actually optimized. Also possible would be at least __doc__. 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:

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

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.

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

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.

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

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 conditions being possible to predict. In these cases, the branch not taken and the condition check is removed.

This can typically predict code like this:

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

or

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.

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:

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, but yet can be used in the code as an expression having a value.

Status

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

Exception Scope Reduction

Consider the following code:

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.

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

Status

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

Exception Block Inlining

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

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

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:

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

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:

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.

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:

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.

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.

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

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 if an expression can raise an exception or not.

Status

Not implemented yet. Will need us to see through the unpacking of what is an iteration over a tuple, we created ourselves. We are not there yet, but we will get there.

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:

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.

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.

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().

Status

Not even started. A re-formulation that avoids the dictionary to call the function, and instead uses temporary variables appears to be relatively straight forward 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:

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

Can be optimized into this:

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.

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 later, 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.

Updates for this Manual

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

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

And the current PDF under: https://nuitka.net/doc/README.pdf

Nuitka User Manual

Overview

Usage

Requirements

Command Line

Installation

License

Tutorial Setup and build on Windows

Setup

Install Python

Install Nuitka

Write some code and test

Create a folder for the Python code

Test your program

Build it using

Run it

Distribute

Use Cases

Use Case 1 - Program compilation with all modules embedded

Use Case 2 - Extension Module compilation

Use Case 3 - Package compilation

Use Case 4 - Program Distribution

Use Case 5 - Setuptools Wheels

Tweaks

Icons

Splash screen

Typical Problems

Memory issues and compiler bugs

Ask Nuitka to use less memory

Avoid 32 bit C compiler/assembler memory limits

Use LTO compilation or not

Switch the C compiler to clang

Add a larger swap file to your embedded Linux

Limit the amount of compilation jobs

Dynamic sys.path

Missing data files in standalone

Missing DLLs in standalone

Dependency creep in standalone

Onefile: Finding files

Windows Programs without console give no errors

Tips

Nuitka Options in the code

Python command line flags

Caching compilation results

Control where Caches live

Runners

Fastest C Compilers

Unexpected Slowdowns

Standalone executables and dependencies

Windows errors with resources

Windows standalone program redistribuation

Detecting Nuitka at run time

Performance

pystone results

Where to go next

Follow me on Twitter

Report issues or bugs

Word of Warning

Join Nuitka

Donations

Unsupported functionality

The co_code attribute of code objects

PDB

Optimization

Constant Folding

Constant Propagation

Built-in Name Lookups

Built-in Call Prediction

Conditional Statement Prediction

Exception Propagation

Exception Scope Reduction

Exception Block Inlining

Empty Branch Removal

Unpacking Prediction

Built-in Type Inference

Quicker Function Calls

Lowering of iterated Container Types

Updates for this Manual

Dynamic `sys.path`

The `co_code` attribute of code objects