Develop Reference

Python: Using modules which support different Python versions in one project

I have 2 python modules where one only supports Python 2.x and the other one 3.x.
Unfortunately I need both for a project.
My workaround for now is to have them run on their own as separate programs and building up their communication via the socket module.
I will be ending up with 2 executables, what I would like to avoid.
The "connection" between both modules has to be as fast as possible.
So my question is if there is a way to somehow combine both to one executable at the end and if there is a better solution for a fast communication as the client-server construction I have now.

There really is no good way to avoid that workaround.
Conceptually, there's no reason that you couldn't embed two interpreters into the same process. But practically, the CPython interpreter depends on some static/global state. While 3.7 is much better about that than, say, 3.0 or 2.6 was, that state still hasn't nearly been eliminated.1 And, the way C linkage works, there's no way to get around that without changing the interpreter.
Also, embedding CPython isn't hard, but it's not trivial, in the way that running an interpreter as a subprocess is trivial—and it may be harder than coming up with an efficient way to pass or share state between subprocesses.
Of course there are other interpreters besides CPython. But the other major implementation with both 2.7 and 3.x versions isn't easily embeddable (PyPy), and the two that are easily embeddable don't have 3.x versions, and also can only be embedded in another VM, and can't run C extension modules (Jython and IronPython). It is possible to do something like using JEP to embed CPython 3.7 via JNI in a JVM while also using Jython 2.7 natively in that same JVM, but I doubt that approach will work for you.
Meanwhile, I mentioned that passing or sharing data between processes generally isn't that hard.
If you don't have that much data, you can usually just pass it pickled over a pipe.
If you do have a ton of data, it usually is, or could be, stored in memory in some structured form—numpy arrays, big hunks of ASCII or UTF-8 text, arrays of ctypes structs, etc.—that you can overlay on an mmap or shared memory segment.
Or, of course, you can come up with your own protocol and communicate with it over a (UNIX or IP) socket. But you don't necessarily have to jump right to that option.
Notice that multiprocessing supports both of the first two—although to take advantage of it with independent interpreters, you have to dig into its source and pull out the bits you need. And there are also third-party libraries that can help. (For example, if you need to pickle things that don't pickle natively, the answer is often as simple as "replace pickle with dill".)
1. Running multiple subinterpreters in various restricted ways does sort of work with things like mod_wsgi, and PEP 554 aims to get things to the state where you can easily and cleanly run multiple 3.7 subinterpreters in the same process, but still nothing like completely independent embeddings of CPython—the subinterpreters share a GIL, a cycle collector, an atexit handler, etc.

What parts of the standard library can run multi cored?

I am learning multi-threaded Python (CPython). I'm aware of the GIL and how it limits threading to a single core (in most circumstances).
I know that I/O functionality can be run multi-cored, however I have been unable to find a list of what parts of the standard library can be run across multiple cores. I believe that urllib can be run multi cored, allowing downloading on a thread on a separate core (but have been unable to find confirmation of this in the docs).
What I am trying to find out is, which parts of the standard library will run multi-core, as this doesn't seem to be specified in the documentation.
Taken from the docs:
However, some extension modules, either standard or third-party, are designed so as to release the GIL when doing computationally-intensive tasks such as compression or hashing. Also, the GIL is always released when doing I/O.

With the multiprocessing package you can write truly parallel programs where separate processes run on different cores. There is no limitation to which libraries (standard or not) each sub-process can use.
The tricky part about multi-process programming is when the processes need to exchange information (e.g., pass each other values, wait for each other to finish with a certain task). The multiprocessing package contains several tools for that.

Python and Threads with PyPy?

I have a kivy application in python which uses some threads.
As python is not able to run these threads on different Cores due to the Global Interpreter Lock, I would have liked to try to use PyPy for it and see if I can make the threads run faster of different cores since PyPy is different and offers stackless (what ever that is? :).
Does somebody have some information to share on how to make a simple python program, which launches some threads by the module threading, running with the pypy interpreter such that it uses this stackless feature?

Pypy won't resolve Python problems of running a single-thread each time, since it also makes use of the GIL - http://doc.pypy.org/en/latest/faq.html#does-pypy-have-a-gil-why
Besides that, Kivy is a complex project embedding Python itself - although I don't know it very well, I doubt it is possible to switch the Python used in it for Pypy.
Depending on what you are doing, you may want to use the multiprocessing module instead of threading - it is a drop-in replacement that will make transparent inter-process calls to Python functions, and can therefore take advantage of multiple-cores.
https://docs.python.org/3/library/multiprocessing.html
This is standard in cPython and can likely be used from within Kivy, if (and only if) all code in the subprocess just take care of number-crunching, and so on, and all user interaction and display updates are made on the main process.

How to set the max thread a python script could use when calling from shell

Have script a.py, it will run some task with multiple-thread, please noticed that I have no control over the a.py.
I'm looking for a way to limit the number of thread it could use, as I found that using more threads than my CPU core will slow down the script.
It could be something like:
python --nthread=2 a.py
or Modifying something in my OS is also acceptable .
I am using ubuntu 16.04
As requested:
the a.py is just a module in scikit-learn the MLPRegressor .
I also asked this question here.

A more general way, not specific to python:
taskset -c 1-3 python yourProgram.py
In this case the threads 1-3 (3 in total) will be used. Any parallelization invoked by your program will share those resources.
For a solution that fits your exact problem you should better identify which part of the code parellelizes. For instance, if it is due to numpy routines, you could limit it by using:
OMP_NUM_THREADS=4 python yourProgram.py
Again, the first solution is general and handled by the os, whereas the second is python (numpy) specific.

Read the threading doc, it said:
CPython implementation detail: In CPython, due to the Global Interpreter Lock, only one thread can execute Python code at once (even though certain performance-oriented libraries might overcome this limitation). If you want your application to make better use of the computational resources of multi-core machines, you are advised to use multiprocessing. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.
If you will like to take advantage of the multi-core processing better update the code to use multiprocessing module.
In case that you prefer anyway continue using threading, you have one option passing the number of threads to the application as an argument like:
python a.py --nthread=2
Then you can update the script code to limit the threads.

Are Python threads buggy?

A reliable coder friend told me that Python's current multi-threading implementation is seriously buggy - enough to avoid using altogether. What can said about this rumor?

Python threads are good for concurrent I/O programming. Threads are swapped out of the CPU as soon as they block waiting for input from file, network, etc. This allows other Python threads to use the CPU while others wait. This would allow you to write a multi-threaded web server or web crawler, for example.
However, Python threads are serialized by the GIL when they enter interpreter core. This means that if two threads are crunching numbers, only one can run at any given moment. It also means that you can't take advantage of multi-core or multi-processor architectures.
There are solutions like running multiple Python interpreters concurrently, using a C based threading library. This is not for the faint of heart and the benefits might not be worth the trouble. Let's hope for an all Python solution in a future release.

The standard implementation of Python (generally known as CPython as it is written in C) uses OS threads, but since there is the Global Interpreter Lock, only one thread at a time is allowed to run Python code. But within those limitations, the threading libraries are robust and widely used.
If you want to be able to use multiple CPU cores, there are a few options. One is to use multiple python interpreters concurrently, as mentioned by others. Another option is to use a different implementation of Python that does not use a GIL. The two main options are Jython and IronPython.
Jython is written in Java, and is now fairly mature, though some incompatibilities remain. For example, the web framework Django does not run perfectly yet, but is getting closer all the time. Jython is great for thread safety, comes out better in benchmarks and has a cheeky message for those wanting the GIL.
IronPython uses the .NET framework and is written in C#. Compatibility is reaching the stage where Django can run on IronPython (at least as a demo) and there are guides to using threads in IronPython.

The GIL (Global Interpreter Lock) might be a problem, but the API is quite OK. Try out the excellent processing module, which implements the Threading API for separate processes. I am using that right now (albeit on OS X, have yet to do some testing on Windows) and am really impressed. The Queue class is really saving my bacon in terms of managing complexity!
EDIT: it seemes the processing module is being included in the standard library as of version 2.6 (import multiprocessing). Joy!

As far as I know there are no real bugs, but the performance when threading in cPython is really bad (compared to most other threading implementations, but usually good enough if all most of the threads do is block) due to the GIL (Global Interpreter Lock), so really it is implementation specific rather than language specific. Jython, for example, does not suffer from this due to using the Java thread model.
See this post on why it is not really feasible to remove the GIL from the cPython implementation, and this for some practical elaboration and workarounds.
Do a quick google for "Python GIL" for more information.

If you want to code in python and get great threading support, you might want to check out IronPython or Jython. Since the python code in IronPython and Jython run on the .NET CLR and Java VM respectively, they enjoy the great threading support built into those libraries. In addition to that, IronPython doesn't have the GIL, an issue that prevents CPython threads from taking full advantage of multi-core architectures.

I've used it in several applications and have never had nor heard of threading being anything other than 100% reliable, as long as you know its limits. You can't spawn 1000 threads at the same time and expect your program to run properly on Windows, however you can easily write a worker pool and just feed it 1000 operations, and keep everything nice and under control.