Contents
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. You can use all library or and all extension modules freely. It translates the Python into a C level program that then uses "libpython" to execute in the same way as CPython does, in a very, 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 or higher on Windows
Python: Version 2.6, 2.7 or 3.2, 3.3 (support for upcoming 3.4 exists partially)
You need the standard Python implementation, called CPython, to execute Nuitka, because it is closely tied to using it.
Note
The created binaries can be made executable independent of the Python installation, with
--standaloneoption.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.
Architectures: x86, x86_64 (amd64), and arm.
Other architectures may also work, as Nuitka is generally not using much hardware specifics. These are just the ones tested and known good. Feedback is welcome. Generally the architectures that Debian supports should be considered good.
No environment variable changes are needed, you can call 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.
Nuitka has a --help option to output what it can do:
nuitka --helpThe nuitka-run command is the same as nuitka, but with different
default. It tries to compile and directly execute a Python script:
nuitka-run --helpThese 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.
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.
If you want to compile a whole program recursively, and not only the single file that is the main program, do it like this:
nuitka --recurse-all program.pyNote
The is more fine grained control than --recurse-all available. Consider
the output of nuitka --help.
In case you have a plugin directory, i.e. one which cannot be found by recursing
after normal import statements via the PYTHONPATH (which would be
recommended way), you can always require that a given directory shall also be
included in the executable:
nuitka --recurse-all --recurse-directory=plugin_dir program.pyNote
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.
If you want to compile a single extension module, all you have to do is this:
nuitka --module some_module.pyThe resulting file "some_module.so" can then be used instead of "some_module.py". 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.
If you need to compile a whole package and embedded all modules, that is also feasible, use Nuitka like this:
nuitka --module some_package --recurse-directory=some_packageNote
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.
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.
Please visit the mailing list page in order to subscribe the relatively low volume mailing list. All Nuitka issues can be discussed there.
Should you encounter any issues, bugs, or ideas, please visit the Nuitka bug tracker and report them.
You are welcome to contact me via email with your questions.
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.
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:
-
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.
-
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.
-
This branch contains potentially unfinished and incomplete work. It is very frequently subject
git rebaseand 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.
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 it.
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:
5 + 6 # operations
5 < 6 # comparisons
range(3) # built-insLiterals 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.
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 look-up.
Consider e.g. the module attribute __name__ which likely is only ever read,
so its value could be predicted to a constant string known at compile time. This
can then be used as input to the constant folding.
if __name__ == "__main__":
# Your test code might be here
use_something_not_use_by_program()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:
try:
something()
except ValueError: # The ValueError is a slow global name lookup normally.
passFor 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.
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.
range( 100000 ) # We do not want this one to be expandedStatus: This is considered mostly implemented. Please file bugs for built-ins that are predictable but are not computed by Nuitka at compile time.
For conditional statements, some branches may not ever be taken, because of the conditions being possible to predict. In these cases, the branch not taken and the condition check is removed.
This can typically predict code like this:
if __name__ == "__main__":
# Your test code might be here
use_something_not_use_by_program()or
if False:
# Your deactivated code might be hereIt 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.
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 executed.
Consider the following code:
print side_effect_having() + (1 / 0)
print something_else()The (1 / 0) can be predicted to raise a ZeroDivisionError exception,
which will be propagated through the + operation. That part is just Constant
Propagation as normal.
The call 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 expression.
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 extended.
Consider the following code:
try:
b = 8
print range(3, b, 0)
print "Will not be executed"
except ValueError, e:
print eThe try block is bigger than it needs to be. The statement b = 8 cannot
cause a ValueError to be raised. As such it can be moved to outside the try
without any risk.
b = 8
try:
print range(3, b, 0)
print "Will not be executed"
except ValueError, e:
print eStatus: Not yet done yet. The infrastructure is in place, but until exception block inlining works perfectly, there is not much of a point.
With the exception propagation it is then possible to transform this code:
try:
b = 8
print range(3, b, 0)
print "Will not be executed"
except ValueError, e:
print etry:
raise ValueError, "range() step argument must not be zero"
except ValueError, e:
print eWhich then can be reduced by avoiding the raise and catch of the exception, making it:
e = ValueError( "range() step argument must not be zero" )
print eStatus: This is not implemented yet.
For loops and conditional statements that contain only code without effect, it should be possible to remove the whole construct:
for i in range(1000):
passThe loop could be removed, at maximum it should be considered an assignment of
variable i to 999 and no more.
Another example:
if side_effect_free:
passThe 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.
When the length of the right hand side of an assignment to a sequence can be predicted, the unpacking can be replaced with multiple assignments.
a, b, c = 1, side_effect_free(), 3a = 1
b = side_effect_free()
c = 3This 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.
When a construct like in xrange() or in range() is used, it is possible
to know what the iteration does and represent that, so that iterator users can
use that instead.
I consider that:
for i in xrange(1000):
something(i)could translate xrange(1000) into an object of a special class that does the
integer looping more efficiently. In case i is only assigned from there,
this could be a nice case for a dedicated class.
Status: Future work, not even started.
Functions are structured so that their parameter parsing and tp_call
interface is separate from the actual function code. This way the call can be
optimized away. One problem is that the evaluation order can differ.
def f(a, b, c):
return a, b, c
f( c = get1(), b = get2(), a = get3() )This will evaluate first get1(), then get2() and then get3() and then make the call.
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 get3().
To solve this, we may have to create wrapper functions that allow different order of parameters to C++.
Status: Not even started.
In some cases, accesses to list constants can become tuple constants
instead.
Consider that:
for x in [ 1, 2, 7 ]:
something( x )Can be optimized into this:
for x in ( 1, 2, 7 ):
something( x )This allows for simpler code to be generated, and less checks needed, because
e.g. the tuple is clearly immutable, whereas the list needs a check to
assert that.
Something similar is possible for set and in theory also 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.
Status: Implemented, needs other optimization to become generally useful, will help others to become possible.
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,
importpackages 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 information.
- 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.
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.
-
Thanks for hosting the build infrastructure that the Debian and sponsor Yaroslav Halchenko uses to provide packages for all Ubuntu versions.
-
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 Buildbot 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.
This document is written in REST. That is an ASCII format which is readable as ASCII, but used to generate PDF or HTML documents.
You will find the current source under: http://nuitka.net/gitweb/?p=Nuitka.git;a=blob_plain;f=README.rst
And the current PDF under: http://nuitka.net/doc/README.pdf