02 July 2022

Nuitka Release 0.9

This is to inform you about the new stable release of Nuitka. It is the extremely compatible Python compiler, “download now”.

This release has a many optimization improvements, and scalability improvements, while also adding new features, with also some important bug fixes.

Bug Fixes

  • Fix, hard module name lookups leaked a reference to that object. Fixed in 0.8.1 already.

  • Python2: Fix, str.decode with errors as the only argument wasn’t working. Fixed in 0.8.1 already.

  • Fix, could corrupt created uncompiled class objects __init__ functions in case of descriptors being used.

  • Standalone: Added support for newer torch. Fixed in 0.8.1 already.

  • Standalone: Added support for newer torchvision. Fixed in 0.8.1 already.

  • Fix, could compile time crash during initial parsing phase on constant dictionary literals with non-hashable keys.

    { {}:1, }
  • Fix, hard imported sub-modules of hard imports were falsely resolved to their parent. Fixed in 0.8.3 already.

    importlib.resources.read_bytes() # gave importlib has no attribute...
  • Windows: Fix, outputs with --force-stdout-spec or --force-stderr-spec were created with the file system encoding on Python3, but they nee to be utf-8.

  • Fix, didn’t allow zero spaces in Nuitka project options, which is not expected.

  • Modules: Fix, the del __file__ in the top level module in module mode caused crashes at run time, when trying to restore the original __file__ value, after the loading CPython corrupted it.

  • Python2.6: Fixes for installations without pkg_resources.

  • Windows: Fix for very old Python 2.6 versions, these didn’t have a language assigned that could be used.

  • Security: Fix for CVE-2022-2054 where environment variables used for transfer of information between Nuitka restarting itself, could be used to execute arbitrary code at compile time.

  • Anaconda: Fix, the torch plugin was not working on Linux due to missing DLL dependencies.

  • Fix, static optimization of importlib.import_module with a package given, for an absolute import was optimized into the wrong import, package was not ignored as it should be.

  • Windows: Installed Python scan could crash on trying installation paths from registry that were manually removed in the mean time, but not through an uninstaller.

  • Standalone: Added missing implicit dependency for pyreadstat because parts of standard library it uses are no more automatically included.

  • Windows: Could still crash when no powershell is available with symlinks, handle this more gracefully.

  • Standalone: Added more missing Plotly dependencies, but more work will be needed to complete this.

  • Standalone: Add missing stdlib dependency on multiprocessing by concurrent.futures.process.

  • Standalone: Fix, implicit dependencies assigned to imageio on PIL plugins should actually be assigned to PIL.Image that actually loads them, so it works outside of imageio too.

New Features

  • UI: Added new option --user-package-configuration-file to allow users to provide extra Yaml configuration files for the Nuitka plugin mechanism to add hidden dependencies, anti-bloat, or data files, for packages. This will be useful for developing PRs to the standard file of Nuitka. Currently the schema is available, but it is not documented very well yet, so not really ready for end users just yet.

  • Standalone: Added new no-qt plugin as an easy way to prevent all of the Qt bindings from being included in a compilation.

  • Include module search path in compilation report.


  • Faster dictionary iteration with our own replacement for PyDict_Next that avoids the DLL call overhead (in case of non-static libpython) and does less unnecessary checks.

  • Added optimization for str.count and str.format methods as well, this should help in some cases with compile time optimization.

  • The node for dict.update with only an iterable argument, but no keyword arguments, was in fact unused due to wrongly generated code. Also the form with no arguments wasn’t yet handled properly.

  • Scalability: Use specialized nodes for pair values, i.e. the representation of x = y in e.g. dictionary creations. With constant keys, and values, these avoid full constant value nodes, and therefore save memory and compile time for a lot of code.

  • Anti-Bloat: Added more scalability work to avoid including modules that make compilation unnecessarily big.

  • Python3.9+: Faster calls in case of mixed code, i.e. compiled code calling uncompiled code.

  • Removing duplicates and non-existent entries from modules search path should improve performance when locating modules.

  • Optimize calls through variables as well. These are needed for the package resource nodes to properly resolve at compile time from their hard imports to the called function.

  • Hard imported names should also be considered very trusted themselves, so they are e.g. also optimized in calls.

  • Anti-Bloat: Avoid more useless imports in Pandas, Numba, Plotly, and other packages, improving the scalability some more.

  • Added dedicated nodes for pkg_resources.require, pkg_resources.get_distribution, importlib.metadata.version, and importlib_metadata.version, so we can use compile time optimization to resolve their argument values where possible.

  • Avoid annotating control flow escape for all release statements. Sometimes we can tell that __del__ will not execute outside code ever, so this then avoids marking values as escaped, and taking the time to do so.

  • Calls of methods through variables on str, dict, bytes that have dedicated nodes are now also optimized through variables.

  • Boolean tests through variables now also are optimized when the original assignment is a compile time constant that is not mutable. This is only basic, but will allow tests on TYPE_CHECKING coming from a from typing import TYPE_CHECKING statement to be optimized, avoiding this overhead.


  • Changed to torch plugin to Yaml based configuration, making it obsolete, it only remains there for a few releases, to not crash existing build scripts.

  • Moved older package specific hacks to the Yaml file. Some of these were from hotfixes where the Yaml file wasn’t yet used by default, but now there is no need for them anymore.

  • Removed most of the pkg-resources plugin work. This is now done during optimization phase and rather than being based on source code matches, it uses actual value tracing, so it immediately covers many more cases.

  • Continued spelling improvements, renaming identifiers used in the source that the cspell based extension doesn’t like. This aims at producing more readable and searchable code.

  • Generated attribute nodes no longer do local imports of the operation nodes they refer to. This also avoids compile time penalties during optimization that are not necessary.

  • Windows: Avoid useless bytecode of inline copy used by Python3 when installing for Python2, this spams just a lot of errors.


  • Removed MSI installers from the download page. The MSI installers are discontinued as Python has deprecated their support for them, as well as Windows 10 is making it harder for users to install them. Using the PyPI installation is recommended on Windows.

  • Merged our Yaml files into one and added schema description, for completion and checking in Visual Code while editing. Also check the schema in check-nuitka-with-yamllint which is now slightly misnamed. The schema is in no way final and will see improvements in future releases.

  • UI: Nicer progress bar layout that avoids flicker when optimizing modules.

  • UI: When linking, output the total number of object files used, to have that knowledge after the progress bar for C compilation is gone.

  • Quality: Auto-format the package configuration Yaml file for anti-bloat, implicit dependencies, etc.

  • GitHub: Point out the commit hook in the PR template.

  • UI: Nicer output in case of no commercial version is used.

  • Updated the MinGW64 winlibs download used on Windows to the latest version based on gcc 11, the gcc 12 is not yet ready.

  • Git: Make sure we are not affected by core.autocrlf setting, as it interferes with auto-format enforcing Unix newlines.

  • Removed the MSI downloads. Windows 10 has made them harder to install and Python itself is discontinuing support for them, while often it was only used by beginners, for which it was not intended.

  • Anaconda: Make it more clear how to install static libpython with precise command.

  • UI: Warn about using module from Debian packages. These can be made non-portable to other OSes. Read more on the info page for detailed information.

  • Quality: The auto-format now floats imports to the top for consistency. With few exceptions, it was already done like this. But it makes things easier for generated code.


  • The reflected test was adapted to preserve PYTHONPATH now that module presence influences optimization.


This release marks a point, that will allow us to open up the compatibility work for implicit dependencies and anti-bloat stuff even further. The Yaml format will need documentation and potentially more refinement, but will open up a future, where latest packages can be supported with just updating this configuration.

The scalability improvements really make a difference for many libraries and are a welcome improvement on both memory usage and compile time. They are achieved by an accord of static optimization of

One optimization aimed at optimizing tuple unpacking, was not finished in time for this release, but will be subject of a future release. It has driven many other improvements though.

Generally, also from the UI, this is a huge step forward. With links to the website for complex topics being started, and the progress bar flicker being removed, the tool has yet again become more user friendly.