####################
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 [#]_
Currently this means, you need to use one of these compilers:
- The MinGW64 C11 compiler on Windows, must be based on gcc 11.2 or
higher. It will be *automatically* downloaded if no usable C
compiler is found, which is the recommended way of installing it,
as Nuitka will also upgrade it for you.
- Visual Studio 2022 or higher on Windows [#]_, older versions will
work but only supported for commercial users. Configure to use the
English language pack for best results (Nuitka filters away
garbage outputs, but only for English language). It will be used
by default if installed.
- On all other platforms, the ``gcc`` compiler of at least version
5.1, and below that the ``g++`` compiler of at least version 4.4
as an alternative.
- The ``clang`` compiler on macOS X and most FreeBSD architectures.
- On Windows the ``clang-cl`` compiler on Windows can be used if
provided by the Visual Studio installer.
- Python: Version 2.6, 2.7 or 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9, 3.10
.. important::
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 (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 do an overwrite without destroying the original
source file.
.. admonition:: 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.
.. admonition:: 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.
.. [#]
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.
.. [#]
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.
.. 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.
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 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:
.. 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, 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:
.. code:: bash
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:
.. code:: bash
python -m nuitka --module some_package --include-package=some_package
.. note::
The inclusion of the package contents needs to be provided manually,
otherwise, the package is 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``.
.. code:: bash
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-files=<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-files=/etc/*.txt=etc/`` where you get to specify shell
patterns for the files, and a subdirectory where to put them, indicated
by the trailing slash.
To copy a whole folder with all files, you can use
``--include-data-dir=/path/to/images=images`` which will copy all files
including a potential subdirectory structure. You cannot filter here,
i.e. if you want only a partial copy, remove the files beforehand.
For package data, there is a better way, using
``--include-package-data`` which detects data files of packages
automatically and copies them over. It even accepts patterns in 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.
.. code:: bash
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.
.. 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 "Good Looks".
Again, on Windows, for the temporary file directory, by default the user
one is used, however this can be overridden 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\...\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 |
+-------------+---------------------------------------------------------------------------+----------------------------------+
| %CACHE_DIR% | Cache directory for the user. | C:\Users\SomeBody\AppData\Local |
+-------------+---------------------------------------------------------------------------+----------------------------------+
| %COMPANY% | Value given as ``--windows-company-name`` | YourCompanyName |
+-------------+---------------------------------------------------------------------------+----------------------------------+
| %PRODUCT% | Value given as ``--windows-product-name`` | YourProductName |
+-------------+---------------------------------------------------------------------------+----------------------------------+
| %VERSION% | Combination of ``--windows-file-version`` & ``--windows-product-version`` | 3.0.0.0-1.0.0.0 |
+-------------+---------------------------------------------------------------------------+----------------------------------+
| %HOME% | Home directory for the user. | /home/somebody |
+-------------+---------------------------------------------------------------------------+----------------------------------+
.. 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.
.. code:: python
# For setup.py if not you't use other build systems:
setup(
...,
command_options={
'nuitka': {
# boolean option, e.g. if you cared for C compilation commands
'--show-scons': True,
# options without value, e.g. enforce using Clang
'--clang': None,
# options with single values, e.g. enable a plugin of Nuitka
'--enable-plugin': "pyside2",
# options with several values, e.g. avoiding including modules
'--nofollow-import-to' : ["*.tests", "*.distutils"],
}
},
)
# For setup.py with other build systems:
# The tuple nature of the arguments is required by the dark nature of
# "setuptools" and plugins to it, that insist on full compatibility,
# e.g. "setuptools_rust"
setup(
...,
command_options={
'nuitka': {
# boolean option, e.g. if you cared for C compilation commands
'--show-scons': ("setup.py", True),
# options without value, e.g. enforce using Clang
'--clang': ("setup.py", None),
# options with single values, e.g. enable a plugin of Nuitka
'--enable-plugin': ("setup.py", "pyside2"),
# options with several values, e.g. avoiding including modules
'--nofollow-import-to' : ("setup.py", ["*.tests", "*.distutils"]),
}
},
)
If for some reason, you cannot or do not what 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 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``
.. 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"
[nuitka]
# These are not recommended, but they make it obvious to have effect.
# boolean option, e.g. if you cared for C compilation commands, leading
# dashes are omitted
show-scons = true
# options with single values, e.g. enable a plugin of Nuitka
enable-plugin = pyside2
# options with several values, e.g. avoiding including modules, accepts
# list argument.
nofollow-import-to = ["*.tests", "*.distutils"]
.. note::
For the ``nuitka`` requirement above absolute paths like
``C:\Users\...\Nuitka`` will also work on Linux, use an absolute path
with *two* leading slashes, e.g. ``//home/.../Nuitka``.
********
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:
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:
.. code:: python
# nuitka-project: --onefile
# nuitka-project: --onefile-windows-splash-screen-image={MAIN_DIRECTORY}/Splash-Screen.png
# Whatever this is obviously
print("Delaying startup by 10s...")
import time
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 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 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 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 uses ``os.listdir``, checks filenames, and
then opens a file and does ``exec`` on them. This approach is working
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, but this can actually
# be also a directory.
scan_path = scan_package.__path__
for item in pkgutil.iter_modules(scan_path):
# You may want to do it recursively, but we don't do this here in
# this example.
if item.ispkg:
continue
# The loader object knows how to do it.
module_loader = item.module_finder.find_module(item.name)
# Ignore bytecode only left overs. Deleted files can cause
# these things, so we just ignore it. Not every load has a
# filename, so we need to catch that error.
try:
if module_loader.get_filename().endswith(".pyc"):
continue
except AttributeError:
# Not a bytecode loader, but e.g. extension module, which is OK in case
# it was compiled with Nuitka.
pass
plugin_module = module_loader.load_module(item.name)
# At least for Python2, this is not set properly, but we use it for package
# data loading, so this manual patching up allows these to use proper methods
# for loading their stuff as well.
plugin_module.__package__ = scan_package.__name__
Missing data files in standalone
================================
If your program fails to file data, it can cause all kinds of different
behaviors, 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.
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. Feel free to
enhance it and make PRs towards Nuitka with it.
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.
.. 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"))
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:
.. code:: python
# 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 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. (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 where things might be non-standard.
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
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 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:
+------------------+-------------+-------------------------------+
| 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.
Providing extra Options to Nuitka C compilation
===============================================
Nuitka will apply values from the environment variables ``CCFLAGS``,
``LDFLAGS`` during the compilation on top of what it determines to be
necessary. Beware of course, that is this is only useful if you know
what you are doing, so should this pose an issues, raise them only with
perfect information.
*************
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 -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 needs constant work. Although the Python
compatibility is insanely high, and test suite works near perfectly,
there is still more work needed, esp. to make it do more optimization.
Try it out, and when popular packages do not work, please make reports
on GitHub.
Follow me on Twitter
====================
Nuitka announcements and interesting stuff is pointed to on the Twitter
account, but obviously with not too many details. `@KayHayen
<https://twitter.com/KayHayen>`_.
Report issues or bugs
=====================
Should you encounter any issues, bugs, or ideas, please visit the
`Nuitka bug tracker <https://github.com/kayhayen/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.
.. 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 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
<https://nuitka.net/doc/developer-manual.html>`__ explains the coding
rules, branching model used, with feature branches and hotfix
releases, the Nuitka design and much more. Consider reading it to
become a contributor. This document is intended for Nuitka users.
***********
Donations
***********
Should you feel that you cannot help Nuitka directly, but still want to
support, please consider `making a donation
<https://nuitka.net/pages/donations.html>`__ and help this way.
***************************
Unsupported functionality
***************************
The ``co_code`` attribute of code objects
=========================================
The code objects are empty for native compiled functions. There is no
bytecode with Nuitka's compiled function objects, so there is no way to
provide it.
PDB
===
There is no tracing of compiled functions to attach a debugger to.
**************
Optimization
**************
Constant Folding
================
The most important form of optimization is the constant folding. This is
when an operation can be fully predicted at compile time. Currently,
Nuitka does these for some built-ins (but not all yet, somebody to look
at this more closely will be very welcome!), and it does it e.g. for
binary/unary operations and comparisons.
Constants currently recognized:
.. code:: python
5 + 6 # binary operations
not 7 # unary operations
5 < 6 # comparisons
range(3) # built-ins
Literals are the one obvious source of constants, but also most likely
other optimization steps like constant propagation or function inlining
will be. So this one should not be underestimated and a very important
step of successful optimizations. Every option to produce a constant may
impact the generated code quality a lot.
.. admonition:: Status
The folding of constants is considered implemented, but it might be
incomplete in that not all possible cases are caught. Please report
it as a bug when you find an operation in Nuitka that has only
constants as input and is not folded.
Constant Propagation
====================
At the core of optimizations, there is an attempt to determine the
values of variables at run time and predictions of assignments. It
determines if their inputs are constants or of similar values. An
expression, e.g. a module variable access, an expensive operation, may
be constant across the module of the function scope and then there needs
to be none or no repeated module variable look-up.
Consider e.g. the module attribute ``__name__`` which likely is only
ever read, so its value could be predicted to a constant string known at
compile time. This can then be used as input to the constant folding.
.. code:: python
if __name__ == "__main__":
# Your test code might be here
use_something_not_use_by_program()
.. admonition:: Status
From modules attributes, only ``__name__`` is currently actually
optimized. 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:
.. code:: python
try:
something()
except ValueError: # The ValueError is a slow global name lookup normally.
pass
.. admonition:: Status
This works for all built-in names. When an assignment is done to such
a name, or it's even local, then, of course, it is not done.
Built-in Call Prediction
========================
For built-in calls like ``type``, ``len``, or ``range`` it is often
possible to predict the result at compile time, esp. for constant inputs
the resulting value often can be precomputed by Nuitka. It can simply
determine the result or the raised exception and replace the built-in
call with that value, allowing for more constant folding or code path
reduction.
.. code:: python
type("string") # predictable result, 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.
.. admonition:: Status
The built-in call prediction is considered implemented. We can simply
during compile time emulate the call and use its result or raised
exception. But we may not cover all the built-ins there are yet.
Sometimes the result of a built-in should not be predicted when the
result is big. A ``range()`` call e.g. may give too big values to
include the result in the binary. Then it is not done.
.. code:: python
range(100000) # We do not want this one to be expanded
.. admonition:: Status
This is considered mostly implemented. Please file bugs for built-ins
that are pre-computed, but should not be computed by Nuitka at
compile time with specific values.
Conditional Statement Prediction
================================
For conditional statements, some branches may not ever be taken, because
of the 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:
.. code:: python
if __name__ == "__main__":
# Your test code might be here
use_something_not_use_by_program()
or
.. code:: python
if False:
# Your deactivated code might be here
use_something_not_use_by_program()
It will also benefit from constant propagations, or enable them because
once some branches have been removed, other things may become more
predictable, so this can trigger other optimization to become possible.
Every branch removed makes optimization more likely. With some code
branches removed, access patterns may be more friendly. Imagine e.g.
that a function is only called in a removed branch. It may be possible
to remove it entirely, and that may have other consequences too.
.. admonition:: Status
This is considered implemented, but for the maximum benefit, more
constants need to be determined at compile time.
Exception Propagation
=====================
For exceptions that are determined at compile time, there is an
expression that will simply do raise the exception. These can be
propagated upwards, collecting potentially "side effects", i.e. parts of
expressions that were executed before it occurred, and still have to be
executed.
Consider the following code:
.. code:: python
print(side_effect_having() + (1 / 0))
print(something_else())
The ``(1 / 0)`` can be predicted to raise a ``ZeroDivisionError``
exception, which will be propagated through the ``+`` operation. That
part is just Constant Propagation as normal.
The call ``side_effect_having()`` will have to be retained though, but
the ``print`` does not and can be turned into an explicit raise. The
statement sequence can then be aborted and as such the
``something_else`` call needs no code generation or consideration
anymore.
To that end, Nuitka works with a special node that raises an exception
and is wrapped with a so-called "side_effects" expression, but yet can
be used in the code as an expression having a value.
.. admonition:: Status
The propagation of exceptions is mostly implemented but needs
handling in every kind of 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:
.. code:: python
try:
b = 8
print(range(3, b, 0))
print("Will not be executed")
except ValueError as e:
print(e)
The ``try`` block is bigger than it needs to be. The statement ``b = 8``
cannot cause a ``ValueError`` to be raised. As such it can be moved to
outside the try without any risk.
.. code:: python
b = 8
try:
print(range(3, b, 0))
print("Will not be executed")
except ValueError as e:
print(e)
.. admonition:: Status
This is considered done. For every kind of operation, we trace if it
may raise an exception. We do however *not* 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:
.. code:: python
try:
b = 8
print(range(3, b, 0))
print("Will not be executed!")
except ValueError as e:
print(e)
.. code:: python
try:
raise ValueError("range() step argument must not be zero")
except ValueError as e:
print(e)
Which then can be lowered in complexity by avoiding the raise and catch
of the exception, making it:
.. code:: python
e = ValueError("range() step argument must not be zero")
print(e)
.. admonition:: Status
This is not implemented yet.
Empty Branch Removal
====================
For loops and conditional statements that contain only code without
effect, it should be possible to remove the whole construct:
.. code:: python
for i in range(1000):
pass
The loop could be removed, at maximum, it should be considered an
assignment of variable ``i`` to ``999`` and no more.
.. admonition:: Status
This is not implemented yet, as it requires us to track iterators,
and their side effects, as well as loop values, and exit conditions.
Too much yet, but we will get there.
Another example:
.. code:: python
if side_effect_free:
pass
The condition check should be removed in this case, as its evaluation is
not needed. It may be difficult to predict that ``side_effect_free`` has
no side effects, but many times this might be possible.
.. admonition:: Status
This is considered implemented. The conditional statement nature is
removed if both branches are empty, only the condition is evaluated
and checked for truth (in cases that could raise an exception).
Unpacking Prediction
====================
When the length of the right-hand side of an assignment to a sequence
can be predicted, the unpacking can be replaced with multiple
assignments.
.. code:: python
a, b, c = 1, side_effect_free(), 3
.. code:: python
a = 1
b = side_effect_free()
c = 3
This is of course only really safe if the left-hand side cannot raise an
exception while building the assignment targets.
We do this now, but only for constants, because we currently have no
ability to predict if an expression can raise an exception or not.
.. admonition:: 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:
.. code:: python
for i in xrange(1000):
something(i)
could translate ``xrange(1000)`` into an object of a special class that
does the integer looping more efficiently. In case ``i`` is only
assigned from there, this could be a nice case for a dedicated class.
.. admonition:: Status
Future work, not even started.
Quicker Function Calls
======================
Functions are structured so that their parameter parsing and ``tp_call``
interface is separate from the actual function code. This way the call
can be optimized away. One problem is that the evaluation order can
differ.
.. code:: python
def f(a, b, c):
return a, b, c
f(c=get1(), b=get2(), a=get3())
This will have to evaluate first ``get1()``, then ``get2()`` and only
then ``get3()`` and then make the function call with these values.
Therefore it will be necessary to have a staging of the parameters
before making the actual call, to avoid a re-ordering of the calls to
``get1()``, ``get2()``, and ``get3()``.
.. admonition:: Status
Not even started. A re-formulation that avoids the dictionary to call
the function, and instead uses temporary variables appears to be
relatively 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:
.. code:: python
for x in [a, b, c]:
something(x)
Can be optimized into this:
.. code:: python
for x in (a, b, c):
something(x)
This allows for simpler, faster code to be generated, and fewer checks
needed, because e.g. the ``tuple`` is clearly immutable, whereas the
``list`` needs a check to assert that. This is also possible for sets.
.. admonition:: Status
Implemented, even works for non-constants. Needs other optimization
to become generally useful, and will itself help other optimization
to become possible. This allows us to e.g. only treat iteration over
tuples, and not care about sets.
In theory, something similar is also possible for ``dict``. For the
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