I have a C++ program that uses the C api to use a Python library of mine.
Both the Python library AND the C++ code are multithreaded.
In particular, one thread of the C++ program instantiates a Python object that inherits from threading.Thread. I need all my C++ threads to be able to call methods on that object.
From my very first tries (I naively just instantiate the object from the main thread, then wait some time, then call the method) I noticed that the execution of the Python thread associated with the object just created stops as soon as the execution comes back to the C++ program.
If the execution stays with Python (for example, if I call PyRun_SimpleString("time.sleep(5)");) the execution of the Python thread continues in background and everything works fine until the wait ends and the execution goes back to C++.
I am evidently doing something wrong. What should I do to make both my C++ and Python multithreaded and capable of working with each other nicely? I have no previous experience in the field so please don't assume anything!
A correct order of steps to perform what you are trying to do is:
In the main thread:
Initialize Python using Py_Initialize*.
Initialize Python threading support using PyEval_InitThreads().
Start the C++ thread.
At this point, the main thread still holds the GIL.
In a C++ thread:
Acquire the GIL using PyGILState_Ensure().
Create a new Python thread object and start it.
Release the GIL using PyGILState_Release().
Sleep, do something useful or exit the thread.
Because the main thread holds the GIL, this thread will be waiting to acquire the GIL. If the main thread calls the Python API it may release the GIL from time to time allowing the Python thread to execute for a little while.
Back in the main thread:
Release the GIL, enabling threads to run using PyEval_SaveThread()
Before attempting to use other Python calls, reacquire the GIL using PyEval_RestoreThread()
I suspect that you are missing the last step - releasing the GIL in the main thread, allowing the Python thread to execute.
I have a small but complete example that does exactly that at this link.
You probably do not unlock the Global Interpreter Lock when you callback from python's threading.Thread.
Well, if you are using bare python's C API you have some documentation here, about how to release/acquire the GIL. But while using C++, I must warn you that it might broke down upon any exceptions throwing in your C++ code. See here.
In general any of your C++ function that runs for too long should unlock GIL and lock, whenever it use the C Python API again.
Related
My understanding is that the typical GIL manipulations involve, e.g., blocking I/O operations. Hence one would want to release the lock before the I/O operation and reacquire it once it has completed.
I'm currently facing a different scenario with a C extension: I am creating X windows that are exposed to Python via the Canvas class. When the method show() is called on an instance, a new UI thread is started using PyThreads (with a call to PyThread_start_new_thread). This new thread is responsible for drawing on the X window, using the Python code specified in the on_draw method of a subclass of Canvas. A pure C event loop is started in the main thread that simply checks for events on the X window and, for the time being, only captures the WM_DELETE_EVENT.
So I have potentially many threads (one for each X window) that want to execute Python code and the main thread that does not execute any Python code at all.
How do I release/acquire the GIL in order to allow the UI threads to get into the interpreter orderly?
The rule is easy: you need to hold the GIL to access Python machinery (any API starting with Py<...> and any PyObject).
So, you can release it whenever you don't need any of that.
Anything further than this is the fundamental problem of locking granularity: potential benefits vs locking overhead. There was an experiment for Py 1.4 to replace the GIL with more granular locks that failed exactly because the overhead proved prohibitive.
That's why it's typically released for code chunks involving call(s) to extental facilities that can take arbitrary time (especially if they involve waiting for external events) -- if you don't release the lock, Python will be just idling during this time.
Heeding this rule, you will get to your goal automatically: whenever a thread can't proceed further (whether it's I/O, signal from another thread, or even so much as a time.sleep() to avoid a busy loop), it will release the lock and allow other threads to proceed in its stead. The GIL assigning mechanism strives to be fair (see issue8299 for exploration on how fair it is), releasing the programmer from bothering about any bias stemming solely from the engine.
I think the problem stems from the fact that, in my opinion, the official documentation is a bit ambiguous on the meaning of Non-Python created threads. Quoting from it:
When threads are created using the dedicated Python APIs (such as the threading module), a thread state is automatically associated to them and the code showed above is therefore correct. However, when threads are created from C (for example by a third-party library with its own thread management), they don’t hold the GIL, nor is there a thread state structure for them.
I have highlighted in bold the parts that I find off-putting. As I have stated in the OP, I am calling PyThread_start_new_thread. Whilst this creates a new thread from C, this function is not part of a third-party library, but of the dedicated Python (C) APIs. Based on this assumption, I ruled out that I actually needed to use the PyGILState_Ensure/PyGILState_Release paradigm.
As far as I can tell from what I've seen with my experiments, a thread created from C with (just) PyThread_start_new_thread should be considered as a non-Python created thread.
I'm trying to settle an argument with a coworker. Say that I have a Python 2.6 app that uses psycopg2 to communicate with a Postgres database. The app is multithreaded. When a thread makes a database call using psycopg2, does it release the GIL so other threads could also make a database call?
From a quick glance at the Psycopg release notes, there are many references to releasing the GIL. So apparently it tries to release the GIL when appropriate. You didn't ask about a specific scenario, so I don't think a more specific answer is possible.
As you said, a rule exists that only the thread that has acquired the GIL may operate on Python objects or call Python/C API functions. In order to emulate concurrency of execution, the Python interpreter regularly tries to switch threads. The lock is also released around potentially blocking I/O operations like reading or writing a file, so that other Python threads can run in the meantime.
Most extension code (psycopg2's code included) manipulating the GIL has the following simple structure:
Save the thread state in a local variable.
Release the global interpreter lock.
... Do some blocking I/O operation ...
Reacquire the global interpreter lock.
Restore the thread state from the local variable.
This means that when a blocking I/O operation (waiting for network response from Postgres, for example) occurs, the GIL is released and other threads can continue their execution. When the blocking I/O operation completes, the thread attempts to acquire the GIL, and continues execution (handling results etc) when it finally does acquire it.
Have a look at psycopg2's implementation here.
How can i keep using the console while executing a process from a boost::python module? I figured i have to use threading but I think I'm missing something.
import pk #my boost::python module from c++
import threading
t = threading.Thread(target=pk.showExample, args=())
t.start()
This executes showExample, which runs a Window rendering 3D content. Now i would like to keep on coding in the python console while this window is running. The example above works to show the Window but fails to keep the console interactive. Any Ideas how to do it? Thanks for any suggestions.
Greetings
Chris
Edit: I also tried to make Threads in the showExample() C++ code, didn't work as well. I probably have to make the console a thread, but I have not a clue how and can't find any helpful examples.
Edit2: to make the example more simple I implemented these c++ methods:
void Example::simpleWindow()
{
int running = GL_TRUE;
glfwInit();
glfwOpenWindow(800,600, 8,8,8,8,24,8, GLFW_WINDOW);
glewExperimental = GL_TRUE;
glewInit();
glEnable(GL_DEPTH_TEST);
glEnable(GL_CULL_FACE);
glCullFace(GL_BACK);
while(running)
{
glClearColor(0.0f, 0.0f, 0.0f, 0.0f);
glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT | GL_STENCIL_BUFFER_BIT);
glfwSwapBuffers();
running = !glfwGetKey(GLFW_KEY_ESC) && gkfwGetWindowParam(GLFW_OPENED);
}
}
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
t.join();
}
There may be some useless lines of code (it was just copy paste of a part from the real method i want to use.) Both methods are static. If I start interactive console in a thread and start the pk.makeWindowThread() in python, i can't give input anymore. Doesn't work if I put the call of pk.makeWindowThread() in a python thread as well. (I am trying to print something in console while showing the window.
When trying to execute a process while keeping the console interactive, then consider using the subprocess or multiprocessing modules. When doing this within Boost.Python, it is probably more appropriate to execute a process in C++ using execv() family of functions.
When trying to spawn a thread while keeping the console interactive, then one must consider the Global Interpreter Lock (GIL). In short, the GIL is a mutex around the interpreter, preventing parallel operations to be performed on Python objects. Thus, at any point in time, a max of one thread, the one that has acquired the GIL, is allowed to perform operations on Python objects.
For multithreaded Python programs with no C or C++ threads, the CPython interpreter functions as a cooperative scheduler, enabling concurrency. Threads will yield control when Python knows the thread is about to perform a blocking call. For example, a thread will release the GIL within time.sleep(). Additionally, the interpreter will force a thread to yield control after certain criteria have been met. For example, after a thread has executed a certain amount of bytecode operations, the interpreter will force it to yield control, allowing other threads to execute.
C or C++ threads are sometimes referred to as alien threads in the Python documentation. The Python interpreter has no ability to force an alien thread to yield control by releasing the GIL. Therefore, alien threads are responsible for managing the GIL to permit concurrent or parallel execution with Python threads. With this in mind, lets examine some of the C++ code:
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
t.join();
}
This will spawn a thread, and thread::join() will block, waiting for the t thread to complete execution. If this function is exposed to Python via Boost.Python, then the calling thread will block. As only one Python thread is allowed to be executed at any point in time, the calling thread will own the GIL. Once the calling thread blocks on t.join(), all other Python threads will remain blocked, as the interpreter cannot force the thread to yield control. To enable other Python threads to run, the GIL should be released pre-join, and acquired post-join.
void Example::makeWindowThread()
{
boost::thread t(simpleWindow);
release GIL // allow other python threads to run.
t.join();
acquire GIL // execution is going to occur within the interpreter.
}
However, this will still cause the console to block waiting for the thread to complete execution. Instead, consider spawning the thread and detaching from it via thread::detach(). As the calling thread will no longer block, managing the GIL within Example::makeWindowThread is no longer necessary.
void Example::makeWindowThread()
{
boost::thread(simpleWindow).detach();
}
For more details/examples of managing the GIL, please consider reading this answer for a basic implementation overview, and this answer for a much deeper dive into considerations one must take.
You have two options:
start python with the -i flag, that will cause to drop it to the interactive interperter instead of exiting from the main thread
start an interactive session manually:
import code
code.interact()
The second option is particularily useful if you want to run the interactive session in it's own thread, as some libraries (like PyQt/PySide) don't like it when they arn't started from the main thread:
from code import interact
from threading import Thread
Thread(target=interact, kwargs={'local': globals()}).start()
... # start some mainloop which will block the main thread
Passing local=globals() to interact is necessary so that you have access to the scope of the module, otherwise the interpreter session would only have access to the content of the thread's scope.
I have a "I just want to understand it" question..
first, I'm using python 2.6.5 on Ubuntu.
So.. threads in python (via thread module) are only "threads", and is just tells the GIL to run code blocks from each "thread" in a certain period of time and so and so.. and there aren't actually real threads here..
So the question is - if I have a blocking socket in one thread, and now I'm sending data and block the thread for like 5 seconds. I expected to block all the program because it is one C command (sock.send) that is blocking the thread. But I was surprised to see that the main thread continue to run.
So the question is - how can GIL is able to continue and run the rest of the code after it reaches a blocking command like send? Isn't it have to use real thread in here?
Thanks.
Python uses "real" threads, i.e. threads of the underlying platform. On Linux, it will use the pthread library (if you are interested, here is the implementation).
What is special about Python's threads is the GIL: A thread can only modify Python data structures if it holds this global lock. Thus, many Python operations cannot make use of multiple processor cores. A thread with a blocking socket won't hold the GIL though, so it does not affect other threads.
The GIL is often misunderstood, making people believe threads are almost useless in Python. The only thing the GIL prevents is concurrent execution of "pure" Python code on multiple processor cores. If you use threads to make a GUI responsive or to run other code during blocking I/O, the GIL won't affect you. If you use threads to run code in some C extension like NumPy/SciPy concurrently on multiple processor cores, the GIL won't affect you either.
Python wiki page on GIL mentions that
Note that potentially blocking or long-running operations, such as I/O, image processing, and NumPy number crunching, happen outside the GIL.
GIL (the Global Interpreter Lock) is just a lock, it does not run anything by itself. Rather, the Python interpreter captures and releases that lock as necessary. As a rule, the lock is held when running Python code, but released for calls to lower-level functions (such as sock.send). As Python threads are real OS-level threads, threads will not run Python code in parallel, but if one thread invokes a long-running C function, the GIL is released and another Python code thread can run until the first one finishes.
I am trying to write a C++ class that calls Python methods of a class that does some I/O operations (file, stdout) at once. The problem I have ran into is that my class is called from different threads: sometimes main thread, sometimes different others. Obviously I tried to apply the approach for Python calls in multi-threaded native applications. Basically everything starts from PyEval_AcquireLock and PyEval_ReleaseLock or just global locks. According to the documentation here when a thread is already locked a deadlock ensues. When my class is called from the main thread or other one that blocks Python execution I have a deadlock.
Python> Cfunc1() - C++ func that creates threads internally which lead to calls in "my class",
It stuck on PyEval_AcquireLock, obviously the Python is already locked, i.e. waiting for C++ Cfunc1 call to complete... It completes fine if I omit those locks. Also it completes fine when Python interpreter is ready for the next user command, i.e. when thread is calling funcs in the background - not inside of a native call
I am looking for a workaround. I need to distinguish whether or not the global lock is allowed, i.e. Python is not locked and ready to receive the next command... I tried PyGIL_Ensure, unfortunately I see hang.
Any known API or solution for this ?
(Python 2.4)
Unless you have wrapped your C++ code quite peculiarly, when any Python thread calls into your C++ code, the GIL is held. You may release it in your C++ code (if you want to do some consuming task that doesn't require any Python interaction), and then will have to acquire it again when you want to do any Python interaction -- see the docs: if you're just using the good old C API, there are macros for that, and the recommended idiom is
Py_BEGIN_ALLOW_THREADS
...Do some blocking I/O operation...
Py_END_ALLOW_THREADS
the docs explain:
The Py_BEGIN_ALLOW_THREADS macro opens
a new block and declares a hidden
local variable; the
Py_END_ALLOW_THREADS macro closes the
block. Another advantage of using
these two macros is that when Python
is compiled without thread support,
they are defined empty, thus saving
the thread state and GIL
manipulations.
So you just don't have to acquire the GIL (and shouldn't) until after you've explicitly released it (ideally with that macro) and need to interact with Python in any way again. (Where the docs say "some blocking I/O operation", it could actually be any long-running operation with no Python interaction whatsoever).