Multi-intepreter execution environment

cpy2py allows multiple interpreters to act as one application. In parallel to the main interpreter, other interpreters are run to execute parts of the application.

To connect two interpreters using cpy2py, it must be installed in both of them. For example, to use CPython and PyPy in a single application, make sure the package is available to them:

python -m pip install cpy2py --user
pypy -m pip install cpy2py --user

A twinterpreter is simply another interpreter running as a subprocess -with some glue and magic sprinkled on it. You can control and create them using a :pycpy2py.TwinMaster.

You should only ever worry about two methods: :pyTwinMaster.start launches the twinterpreter. :pyTwinMaster.execute executes an arbitrary callable in the twinterpreter.

from cpy2py import TwinMaster
from my_module import my_function
twinterpreter = TwinMaster('pypy')
twinterpreter.start()

if __name__ == "__main__":
    twinterpreter.execute(my_function, 1, 2, 3, 'ka-pow!', doctor="who?")

The real power of :pycpy2py are Twins - objects living in one twinterpreter and being represented by proxies in any other interpeter. Using twins, you can seamlessly split your application across multiple twinterpreters.

You create twins by inheriting from :pycpy2py.TwinObject instead of :pyobject and setting a __twin_id__. That's it.

from cpy2py import TwinObject
class SuperComputer(TwinObject):
    __twin_id__ = 'pypy'  # makes class native to pypy twinterpeter

    def megaloop(self, x, y):
        return sum(a+b for a in range(x) for b in range(y))

class CWrapper(TwinObject):
    __twin_id__ = 'python'  # makes class native to python twinterpeter

    def callme(self, who, what="buy milk"):
        return some_clib.c_fcn_cll_cplx_xmpl(who, what)

If you don't set __twin_id__ on a child of :pycpy2py.TwinObject, the class will always be native to the main interpreter. Handy for all the stuff that's needed everywhere but really doesn't belong anywhere.

note

At the moment, you have to explicitly start a class's native twinterpreter before instantiating the class. Only the main interpreter is always available, of course.

Instead of full-fletched objects, you can also define functions as twins. These are automatically called in their native twinterpreter.

from cpy2py import twinfunction

@twinfunction('pypy')
def superlooper(count=1000, add=3, start=0):
    for _ in range(count):
        start += add
    return add

print(superlooper(int(1E6), 1))

note

A :pycpy2py.twinfunction is a regular function wrapping a callable. Unlike a :pycpy2py.TwinObject, it will not pass attribute assignments.

The core of :pycpy2py supports some :pylogging facilities. All such loggers are children of the __cpy2py__ logger. By default, no active handlers are attached and propagation is disabled. If needed, you reconfigure them like any other :pylogging logger to suit your needs. Note that if python is run with the -O flag, several logging calls are skipped entirely to improve performance.

For small scale debugging, one can set the environment variable CPY2PY_DEBUG. If it is defined and not empty, logging output is written to stderr. In addition, if it names a valid :pylogging level, that logging level is used.

Note that loggers are meant for development and only address the internal state. Your application should not depend on this information. Unless :pycpy2py misbehaves (or you suspect it to), ignore its logging.

CPy2Py is stable at its core, but still has some features missing. What's there is more than sufficient to significantly enhance your applications.

• Seamlessly integrates into python code.
  • All internals are wrapped away behind the plain python interfaces. No eval, exec or code strings required.
  • Lightweight hooks optimize objects and functions for use with :pycpy2py.
  • If needed, any pickle'able callable can be dispatched to another interpreter.
• Objects natively integrate with twinterpreters.
  • Objects can live in a specific interpreter, with proxies replacing them in others. Classes and instances transparently interact with :pycpy2py in the background.
  • Both class and instance attributes work as expected. Methods, classmethods, staticmethods and descriptors are fully supported.
  • Inheritance is fully supported, including multiple inheritance. Affiliation to interpreters can be changed freely.
• A wide range of interpeters is supported.
  • Pure python, no dependencies means perfect portability.
  • Any interpreter compatible with python 2.6 to 3.7 is supported.
  • Virtual Environments work out of the box.
  • Tested with cpython and pypy, on Linux and Mac OSX.

• Importing functions and classes from __main__ may fail if the module can only be imported via its path.
• By default, calls across interpreters are blocking and not threadsafe. If recursion switches between twinterpreters, :pycpy2py.TwinMaster must use the 'async' kernel.
• Module level settings are not synchronized. For example, configuration of :pylogging is not applied to twinterpreters. Use :py~cpy2py.twinterpreter.group_state.TwinGroupState for initialisation, write modules aware of twinterpreters, or use immutable module-level initializers.
• A :pyweakref to objects only takes local references into account, not cross-interpreter references.

Dispatching to another twinterpreter adds about 200 - 300 us of overhead. This is mainly due to serialization for the IPC between the interpreters. Using the asynchronous kernel, there is an additional overhead for creating threads.

In general, twinterpreters get faster the shorter they have to wait between requests. pypy twinterpreters benefit from a high number of requests, allowing their JIT to warm up. Python3 connections are the fastest, provided that both twinterpreters support pickle protocol 4.

A notable fraction of time is spent on debugging output via :pylogging. Even if no output is produced, :pycpy2py is optimized to a point where the logging call is noticeable. If needed, any per-call logging can be disabled by running python in optimized mode. See the python documentation on the -O option and PYTHONOPTIMIZE environment variable.

You can benchmark the overhead yourself using the :pycpy2py_benchmark tools.