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Python compiler with full language support and CPython compatibility

Latest Version:

Nuitka User Manual

.. image:: images/Nuitka-Logo-Symbol.png

.. contents::

.. raw:: pdf

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Nuitka is the Python compiler. It is a good replacement for the Python
interpreter and compiles **every** construct that CPython 2.6, 2.7, 3.2 and 3.3
offer. It translates the Python into a C++ program that then uses "libpython" to
execute in the same way as CPython does, in a very compatible way.

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.



- C++ Compiler: You need a compiler with support for C++03

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

  * GNU g++ compiler of at least version 4.4

  * The clang compiler on MacOS X or FreeBSD, based on LLVM version 3.2

  * The MinGW compiler on Windows

  * Visual Studion 2008 and 2010 on Windows

- Python: Version 2.6, 2.7 or 3.2, 3.3 (support for upcoming 3.4 exists

  You need CPython to execute Nuitka, because itis tightly bound to the
  reference implementation of Python, called "CPython".

  .. note::

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

- 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 Scons usage may have to be adapted.

- Architectures: x86, x86_64 (amd64), and arm.

  Other architectures may also work, these are just the only ones
  tested. Feedback is welcome.

Command Line

No environment variable changes are needed, you can call the ``nuitka`` and
``nuitka-python`` 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.

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

.. code-block:: bash

    nuitka --help

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

.. code-block:: bash

    nuitka-python --help

These options with different defaults are ``--exe`` and ``--execute``, so it is
somewhat more similar to what plain ``python`` will do.


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

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.

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-block:: bash

    nuitka-python --recurse-all

.. note::

   The is more fine grained control than ``--recurse-all`` available. Consider
   the output of ``nuitka-python --help``.

In case you have a plugin directory, i.e. one which is not found by recursing
after normal import statements (recommended way), you can always require that a
given directory shall also be included in the executable:

.. code-block:: bash

    nuitka-python --recurse-all --recurse-directory=plugin_dir

.. note::

   If you don't do any dynamic imports, simply setting your ``PYTHONPATH`` at
   compilation time will be sufficient for all your needs normally.

   Use ``--recurse-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 binary still depends on CPython and used C extension modules
   being installed.

   If you want to be able to copy it to another machine, use ``--standalone``
   and copy the created ``program.dist`` directory and execute the
   ``program.exe`` 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-block:: bash


The resulting file "" can then be used instead of
"". It's left as an exercise to the reader, what happens if both
are present.

.. note::

   The option ``--recurse-all`` and other variants work as well.

Use Case 3 - Package compilation

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

.. code-block:: bash

    nuitka some_package --recurse-directory=some_package

.. note::

   The recursion into the package directory needs to be provided manually,
   otherwise the package is empty. Data files located inside the package will
   not be embedded yet.

Use Case 4 - Cross compilation to Windows

Nuitka can cross compile to Windows from other platforms, specifically Linux,
and these are the instructions on how to do it.

1. Make sure to have the latest wine installed.

   .. code-block:: bash

      apt-get install wine-unstable

   .. note::

      Make sure to actually use the "i386" architecture. From multiarch enabled
      debian systems, that may mean to say "wine-unstable:i386", otherwise it
      won't work.

2. Make sure to use the latest "mxe" environment as the cross compiler.

   .. code-block:: bash

      git clone
      cd mxe
      make gcc
      mkdir -p /opt
      cd /opt
      ln -s $OLDPWD mingw

   Nuitka will use "/opt/mingw" to locate the cross compiler.

3. Install the *same* Python version as you have under Linux.

   .. code-block:: bash

      wine msiexec /i python-2.7.5.msi

   .. note::

      You don't have to install documentation, TCL/Tk files, or Python tests to
      preserve disk space.

.. code-block:: bash

    nuitka-python --windows-target

To test the binary, use "wine program.exe", the "nuitka-python" does it
automatically for you.

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, to make it do more
optimization. Try it out.

Subscribe to its mailing lists

Please visit the `mailing list page
<>`_ in order to subscribe the
relatively low volume mailing list. All Nuitka issues can be discussed there.

Report issues or bugs

Should you encounter any issues, bugs, or ideas, please visit the `Nuitka bug
tracker <>`_ and report them.

Contact me via email with your questions

You are welcome to `contact me via email <>`_ with
your questions.

Word of Warning

Consider using this software with caution. Your feedback and patches to Nuitka
are very welcome.

Especially report it please, if you find that anything doesn't work, because the
project is now at the stage that this should not happen.

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 2 branches:

- `master <;a=shortlog;h=refs/heads/master>`_:

  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 <;a=shortlog;h=refs/heads/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 <;a=shortlog;h=refs/heads/factory>`_:

  This branch contains potentially unfinished and incomplete work. It is very
  frequently subject ``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.

.. note::

   I accept patch files, git formatted patch queues (use ``git format-patch
   origin`` command), or if you prefer git pull on the social code platforms.

   I will do the integration work. If you base your work on "master" or
   "develop" at any given time, I will do any re-basing required and keep your
   authorship intact.

.. 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.


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


Constant Folding

The most important form of optimization is the constant folding. This is when an
operation can be predicted. Currently Nuitka does these for some built-ins (but
not all yet), and it does it for binary/unary operations and comparisons.

Constants currently recognized:

.. code-block:: python

    5 + 6     # 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. Please report it as a bug when you find an operation in Nuitka that
has only constants are input and is not folded.

Constant Propagation

At the core of optimizations there is an attempt to determine 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

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-block:: python

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

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

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

.. code-block:: python

   except ValueError: # The ValueError is a slow global name lookup normally.

Builtin Call Prediction

For builtin 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 builtin call with it allowing for more constant
folding or code path folding.

.. code-block:: 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 hates that 0.

The builtin 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-block:: python

   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 predictable but are not computed by Nuitka at compile time.

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-block:: python

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


.. code-block:: python

   if False:
      # Your deactivated code might be here

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 needs 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, collecting
potentially "side effects", i.e. parts of expressions that must still be

Consider the following code:

.. code-block:: 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 to ``side_effect_having`` will have to be retained though, but the
print statement, 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 has
so called "side_effects" children, yet can be used in generated code as an

Status: The propagation of exceptions is implemented on a very basic level. It
works, but exceptions will not propagate through all different expression and
statement types. As work progresses or examples arise, the coverage will be

Exception Scope Reduction

Consider the following code:

.. code-block:: python

        b = 8
        print range( 3, b, 0 )
        print "Will not be executed"
    except ValueError, 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-block:: python

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

Status: Not yet done yet. The infrastructure is in place, but until exception
block inlining works perfectly, there is not much of a point.

Exception Block Inlining

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

.. code-block:: python

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

.. code-block:: python

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

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

.. code-block:: python

   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:

.. code-block:: python

   for i in range( 1000 ):

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

Another example:

.. code-block:: python

   if side_effect_free:

The condition 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 not implemented yet.

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-block:: python

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

.. code-block:: 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.

Status: Not really implemented, and should use ``mayHaveSideEffect()`` to be
actually good at things.

Builtin 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-block:: python

    for i in xrange(1000):

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.

.. code-block:: python

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

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

This will evaluate first get1(), then get2() and then get3() and then make the

In C++ whatever way the signature is written, its order is fixed.

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

To solve this, we may have to create wrapper functions that allow different
order of parameters to C++.

Status: Not even started.


Contributors to Nuitka

Thanks go to these individuals for their much valued contributions to
Nuitka. Contributors have the license to use Nuitka for their own code even if
Closed Source.

The order is sorted by time.

- Li Xuan Ji: Contributed patches for general portability issue and enhancements
  to the environment variable settings.

- Nicolas Dumazet: Found and fixed reference counting issues, ``import``
  packages work, improved some of the English and generally made good code
  contributions all over the place, solved code generation TODOs, did tree
  building cleanups, core stuff.

- Khalid Abu Bakr: Submitted patches for his work to support MinGW and Windows,
  debugged the issues, and helped me to get cross compile with MinGW from Linux
  to Windows. This was quite a difficult stuff.

- Liu Zhenhai: Submitted patches for Windows support, making the inline Scons
  copy actually work on Windows as well. Also reported import related bugs, and
  generally helped me make the Windows port more usable through his testing and

- Christopher Tott: Submitted patches for Windows, and general as well as
  structural cleanups.

- Pete Hunt: Submitted patches for MacOS X support.

- "ownssh": Submitted patches for built-ins module guarding, and made massive
  efforts to make high quality bug reports. Also the initial "standalone" mode
  implementation was created by him.

- Juan Carlos Paco: Submitted cleanup patches, creator of the `Nuitka GUI
  <>`_, creator of the `Ninja IDE
  plugin <>`_ for Nuitka.

- "dr. Equivalent": Submitted the Nuitka Logo.

- Johan Holmberg: Submitted patch for Python3 support on MacOS X.

- Umbra: Submitted patches to make the Windows port more usable, adding user
  provided application icons, as well as MSVC support for large constants and
  console applications.

Projects used by Nuitka

* The `CPython project <>`_

  Thanks for giving us CPython, which is the base of Nuitka. We are nothing
  without it.

* The `GCC project <>`_

  Thanks for not only the best compiler suite, but also thanks for supporting
  C++11 which helped to get Nuitka off the ground. Your compiler was the first
  usable for Nuitka and with little effort.

* The `Scons project <>`_

  Thanks for tackling the difficult points and providing a Python environment to
  make the build results. This is such a perfect fit to Nuitka and a dependency
  that will likely remain.

* The `valgrind project <>`_

  Luckily we can use Valgrind to determine if something is an actual improvement
  without the noise. And it's also helpful to determine what's actually
  happening when comparing.

* The `NeuroDebian project <>`_

  Thanks for hosting the build infrastructure that the Debian and sponsor
  Yaroslav Halchenko uses to provide packages for all Ubuntu versions.

* The `openSUSE Buildservice <>`_

  Thanks for hosting this excellent service that allows us to provide RPMs for a
  large variety of platforms and make them available immediately nearly at
  release time.

* The `MinGW project <>`_

  Thanks for porting the gcc to Windows. This allowed portability of Nuitka with
  relatively little effort. Unfortunately this is currently limited to compiling
  CPython with 32 bits, and 64 bits requires MSVC compiler.

* The `Wine project <>`_

  Thanks for enabling us to run the cross compiled binaries without have to
  maintain a windows installation at all. Unfortunately this is currently
  limited to compiling CPython with 32 bits, for 64 bits there is no solution

* The Builtbot project <>_

  Thanks for creating an easy to deploy and use continous integration framework
  that also runs on Windows and written and configured in Python. This allows to
  run the Nuitka tests long before release time.

Updates for this Manual

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You will find the current source under:;a=blob_plain;f=README.txt

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