15 October 2015

Nuitka Release 0.5.15

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

This release enables SSA based optimization, the huge leap, not so much in terms of actual performance increase, but for now making the things possible that will allow it.

This has been in the making literally for years. Over and over, there was just “one more thing” needed. But now it’s there.

The release includes much stuff, and there is a perspective on the open tasks in the summary, but first out to the many details.

Bug Fixes

  • Standalone: Added implicit import for reportlab package configuration dynamic import. Fixed in already.

  • Standalone: Fix, compilation of the ctypes module could happen for some import patterns, and then prevented the distribution to contain all necessary libraries. Now it is made sure to not include compiled and frozen form both. Fixed in already.

  • Fix, compilation for conditional statements where the boolean check on the condition cannot raise, could fail compilation. Fixed in already.

  • Fix, the __import__ built-in was making static optimization assuming compile time constants to be strings, which in the error case they are not, which was crashing the compiler.

    __import__(("some.module",))  # tuples don't work

    This error became only apparent, because now in some cases, Nuitka forward propagates values.

  • Windows: Fix, when installing Python2 only for the user, the detection of it via registry failed as it was only searching system key. This was a github pull request. Fixed in already.

  • Some modules have extremely complex expressions requiring too deep recursion to work on all platforms. These modules are now included entirely as bytecode fallback.

  • The standard library may contain broken code due to installation mistakes. We have to ignore their SyntaxError.

  • Fix, pickling compiled methods was failing with the wrong kind of error, because they should not implement __reduce__, but only __deepcopy__.

  • Fix, when running under wine, the check for scons binary was fooled by existence of /usr/bin/scons.

New Features

  • Added experimental support for Python3.5, coroutines don’t work yet, but it works perfectly as a 3.4 replacement.

  • Added experimental Nuitka plug-in framework, and use it for the packaging of Qt plugins in standalone mode. The API is not yet stable nor polished.

  • New option --debugger that makes --run execute directly in gdb and gives a stack trace on crash.

  • New option --profile executes compiled binary and outputs measured performance with vmprof. This is work in progress and not functional yet.

  • Started work on --internal-graph to render the SSA state into diagrams. This is work in progress and not too functional yet.

  • Plug-in framework added. Not yet ready for users. Working PyQt4 and PyQt5 plug-in support. Experimental Windows multiprocessing support. Experimental PyLint warnings disable support. More to come.

  • Added support for Anaconda accelerated mode on macOS by modifying the rpath to the Python DLL.

  • Added experimental support for multiprocessing on Windows, which needs monkey patching of the module to support compiled methods.


  • The SSA analysis is now enabled by default, eliminating variables that are not shared, and can be forward propagated. This is currently limited mostly to compile time constants, but things won’t remain that way.

  • Code generation for many constructs now takes into account if a specific operation can raise or not. If e.g. an attribute look-up is known to not raise, then that is now decided by the node the looked is done to, and then more often can determine this, or even directly the value.

  • Calls to C-API that we know cannot raise, no longer check, but merely assert the result.

  • For attribute look-up and other operations that might be known to not raise, we now only assert that it succeeds.

  • Built-in loop-ups cannot fail, merely assert that.

  • Creation of built-in exceptions never raises, merely assert that too.

  • More Python operation slots now have their own computations and some of these gained overloads for more compile time constant optimization.

  • When taking an iterator cannot raise, this is now detected more often.

  • The try/finally construct is now represented by duplicating the final block into all kinds of handlers (break, continue, return, or except) and optimized separately. This allows for SSA to trace values more correctly.

  • The hash built-in now has dedicated node and code generation too. This is mostly intended to represent the side effects of dictionary look-up, but gives more compact and faster code too.

  • Type type built-in cannot raise and has no side effect.

  • Speed improvement for in-place float operations for += and *=, as these will be common cases.


  • Made the construct based testing executable with Python3.

  • Removed warnings using the new PyLint warnings plug-in for the reflected test. Nuitka now uses the PyLint annotations to not warn. Also do not go into PyQt for reflected test, not needed. Many Python3 improvements for cases where there are differences to report.

  • The optimization tests no longer use 2to3 anymore, made the tests portable to all versions.

  • Checked more in-place operations for speed.


  • Many improvements to the coverage taking. We can hope to see public data from this, some improvements were triggered from this already, but full runs of the test suite with coverage data collection are yet to be done.


The release includes many important new directions. Coverage analysis will be important to remain certain of test coverage of Nuitka itself. This is mostly done, but needs more work to complete.

Then the graphing surely will help us to debug and understand code examples. So instead of tracing, and reading stuff, we should visualize things, to more clearly see, how things evolve under optimization iteration, and where exactly one thing goes wrong. This will be improved as it proves necessary to do just that. So far, this has been rare. Expect this to become end user capable with time. If only to allow you to understand why Nuitka won’t optimize code of yours, and what change of Nuitka it will need to improve.

The comparative performance benchmarking is clearly the most important thing to have for users. It deserves to be the top priority. Thanks to the PyPy tool vmprof, we may already be there on the data taking side, but the presenting and correlation part, is still open and a fair bit of work. It will be most important to empower users to make competent performance bug reports, now that Nuitka enters the phase, where these things matter.

As this is a lot of ground to cover. More than ever. We can make this compiler, but only if you help, it will arrive in your life time.