I am trying to run a method which has an infinite loop in it to create a video display. This method is called within another loop that handles hardware input and as such cannot loop as fast as the video, causing lag if I use the outer loop to run the video. Is there a way to start the video loop and then start the hardware loop and run them separately? Currently if I call the video loop it just sits at that loop until it returns.
Yes, you can use Python's own threading module, or a cooperative microthreading module like gevent.
Note that Python's threading mechanism carries this disclaimer for CPython (the default Python implementation on most boxes):
Due to the Global Interpreter Lock, in CPython 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 of 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.
Depending on how the underlying modules you are calling operate, you may find that while using threading, one thread won't give up control very often, if at all. In that case, using cooperative microthreading might be your only option.
Yes, you can use Python's own multiprocessing module.
Note that Multiprocessing does not have to fight the GIL and can work simultaneously for everything you give it to do.
On the other hand there is a warning with the multiprocessing module, when you spawn a process it is a completely separate python interpreter. So its not just a OS controlled thread. It is in itself an entirely different process. This can add overhead to programs but the advantage of completely dodging the GIL makes this only a mild issue.
Related
CPython has a Global Interpreter Lock (GIL).
So, multiple threads cannot concurrently run Python bytecodes.
What then is the use and relevance of the threading package in CPython ?
During I/O the GIL is released to other threads can run.
Also some extensions (like numpy) can release the GIL when doing calculations.
So an important purpose is to improve performance on not CPU-bound programs. From the Python documentation for the threading module:
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 or concurrent.futures.ProcessPoolExecutor. However, threading is still an appropriate model if you want to run multiple I/O-bound tasks simultaneously.
Another benefit of threading is to do long-running calculations in a GUI program without having to chop up your calculations in small enough pieces to make them fit in timeout functions.
Also keep in mind that while CPython has a GIL now, that might not always be the case in the future.
When python runs some code, the code is compiled in "atomic" commands (= small instructions). Every few hundred atomic instructions python will switch to the next thread and execute the the instructions for that thread. This allows running code pseudo-parallel.
Lets assume you have this code:
def f1():
while True:
# wait for incomming connections and serve a website to them
def f2():
while True:
# get new tweets and process them
And you want to execute f1() and f2() at the same time. In this case, you can simpy use threading and dont need to worry about breaking the loops every now and then to execute the other function. This is also way easier than asynchronous programming.
Simple said: It makes writing scripts which needs to do multiple things easier.
Also, like #roland-smith said, Python releases the GIL during I/O and some other low-level c-code.
I've been trying to wrap my head around how threads work in Python, and it's hard to find good information on how they operate. I may just be missing a link or something, but it seems like the official documentation isn't very thorough on the subject, and I haven't been able to find a good write-up.
From what I can tell, only one thread can be running at once, and the active thread switches every 10 instructions or so?
Where is there a good explanation, or can you provide one? It would also be very nice to be aware of common problems that you run into while using threads with Python.
Yes, because of the Global Interpreter Lock (GIL) there can only run one thread at a time. Here are some links with some insights about this:
http://www.artima.com/weblogs/viewpost.jsp?thread=214235
http://smoothspan.wordpress.com/2007/09/14/guido-is-right-to-leave-the-gil-in-python-not-for-multicore-but-for-utility-computing/
From the last link an interesting quote:
Let me explain what all that means.
Threads run inside the same virtual
machine, and hence run on the same
physical machine. Processes can run
on the same physical machine or in
another physical machine. If you
architect your application around
threads, you’ve done nothing to access
multiple machines. So, you can scale
to as many cores are on the single
machine (which will be quite a few
over time), but to really reach web
scales, you’ll need to solve the
multiple machine problem anyway.
If you want to use multi core, pyprocessing defines an process based API to do real parallelization. The PEP also includes some interesting benchmarks.
Python's a fairly easy language to thread in, but there are caveats. The biggest thing you need to know about is the Global Interpreter Lock. This allows only one thread to access the interpreter. This means two things: 1) you rarely ever find yourself using a lock statement in python and 2) if you want to take advantage of multi-processor systems, you have to use separate processes. EDIT: I should also point out that you can put some of the code in C/C++ if you want to get around the GIL as well.
Thus, you need to re-consider why you want to use threads. If you want to parallelize your app to take advantage of dual-core architecture, you need to consider breaking your app up into multiple processes.
If you want to improve responsiveness, you should CONSIDER using threads. There are other alternatives though, namely microthreading. There are also some frameworks that you should look into:
stackless python
greenlets
gevent
monocle
Below is a basic threading sample. It will spawn 20 threads; each thread will output its thread number. Run it and observe the order in which they print.
import threading
class Foo (threading.Thread):
def __init__(self,x):
self.__x = x
threading.Thread.__init__(self)
def run (self):
print str(self.__x)
for x in xrange(20):
Foo(x).start()
As you have hinted at Python threads are implemented through time-slicing. This is how they get the "parallel" effect.
In my example my Foo class extends thread, I then implement the run method, which is where the code that you would like to run in a thread goes. To start the thread you call start() on the thread object, which will automatically invoke the run method...
Of course, this is just the very basics. You will eventually want to learn about semaphores, mutexes, and locks for thread synchronization and message passing.
Note: wherever I mention thread i mean specifically threads in python until explicitly stated.
Threads work a little differently in python if you are coming from C/C++ background. In python, Only one thread can be in running state at a given time.This means Threads in python cannot truly leverage the power of multiple processing cores since by design it's not possible for threads to run parallelly on multiple cores.
As the memory management in python is not thread-safe each thread require an exclusive access to data structures in python interpreter.This exclusive access is acquired by a mechanism called GIL ( global interpretr lock ).
Why does python use GIL?
In order to prevent multiple threads from accessing interpreter state simultaneously and corrupting the interpreter state.
The idea is whenever a thread is being executed (even if it's the main thread), a GIL is acquired and after some predefined interval of time the
GIL is released by the current thread and reacquired by some other thread( if any).
Why not simply remove GIL?
It is not that its impossible to remove GIL, its just that in prcoess of doing so we end up putting mutiple locks inside interpreter in order to serialize access, which makes even a single threaded application less performant.
so the cost of removing GIL is paid off by reduced performance of a single threaded application, which is never desired.
So when does thread switching occurs in python?
Thread switch occurs when GIL is released.So when is GIL Released?
There are two scenarios to take into consideration.
If a Thread is doing CPU Bound operations(Ex image processing).
In Older versions of python , Thread switching used to occur after a fixed no of python instructions.It was by default set to 100.It turned out that its not a very good policy to decide when switching should occur since the time spent executing a single instruction can
very wildly from millisecond to even a second.Therefore releasing GIL after every 100 instructions regardless of the time they take to execute is a poor policy.
In new versions instead of using instruction count as a metric to switch thread , a configurable time interval is used.
The default switch interval is 5 milliseconds.you can get the current switch interval using sys.getswitchinterval().
This can be altered using sys.setswitchinterval()
If a Thread is doing some IO Bound Operations(Ex filesystem access or
network IO)
GIL is release whenever the thread is waiting for some for IO operation to get completed.
Which thread to switch to next?
The interpreter doesn’t have its own scheduler.which thread becomes scheduled at the end of the interval is the operating system’s decision. .
Use threads in python if the individual workers are doing I/O bound operations. If you are trying to scale across multiple cores on a machine either find a good IPC framework for python or pick a different language.
One easy solution to the GIL is the multiprocessing module. It can be used as a drop in replacement to the threading module but uses multiple Interpreter processes instead of threads. Because of this there is a little more overhead than plain threading for simple things but it gives you the advantage of real parallelization if you need it.
It also easily scales to multiple physical machines.
If you need truly large scale parallelization than I would look further but if you just want to scale to all the cores of one computer or a few different ones without all the work that would go into implementing a more comprehensive framework, than this is for you.
Try to remember that the GIL is set to poll around every so often in order to do show the appearance of multiple tasks. This setting can be fine tuned, but I offer the suggestion that there should be work that the threads are doing or lots of context switches are going to cause problems.
I would go so far as to suggest multiple parents on processors and try to keep like jobs on the same core(s).
I have been trying to write a simple python application to implement a worker queue
every webpage I found about threading has some random guy commenting on it, you shouldn't use python threading because this or that, can someone help me out? what is up with Python threading, can I use it or not? if yes which lib? the standard one is good enough?
Python's threads are perfectly viable and useful for many tasks. Since they're implemented with native OS threads, they allow executing blocking system calls and keep "running" simultaneously - by calling the blocking syscall in a separate thread. This is very useful for programs that have to do multiple things at the same time (i.e. GUIs and other event loops) and can even improve performance for IO bound tasks (such as web-scraping).
However, due to the Global Interpreter Lock, which precludes the Python interpreter of actually running more than a single thread simultaneously, if you expect to distribute CPU-intensive code over several CPU cores with threads and improve performance this way, you're out of luck. You can do it with the multiprocessing module, however, which provides an interface similar to threading and distributes work using processes rather than threads.
I should also add that C extensions are not required to be bound by the GIL and many do release it, so C extensions can employ multiple cores by using threads.
So, it all depends on what exactly you need to do.
You shouldn't need to use
threading. 95% of code does not need
threads.
Yes, Python threading is
perfectly valid, it's implemented
through the operating system's native
threads.
Use the standard library
threading module, it's excellent.
GIL should provide you some information on that topic.
In my script, I have a function foo which basically uses pynotify to notify user about something repeatedly after a time interval say 15 minutes.
def foo:
while True:
"""Does something"""
time.sleep(900)
My main script has to interact with user & does all other things so I just cant call the foo() function. directly.
Whats the better way of doing it and why?
Using fork or threads?
I won't tell you which one to use, but here are some of the advantages of each:
Threads can start more quickly than processes, and threads use fewer operating system resources than processes, including memory, file handles, etc. Threads also give you the option of communicating through shared variables (although many would say this is more of a disadvantage than an advantage - See below).
Processes each have their own separate memory and variables, which means that processes generally communicate by sending messages to each other. This is much easier to do correctly than having threads communicate via shared memory. Processes can also run truly concurrently, so that if you have multiple CPU cores, you can keep all of them busy using processes. In Python*, the global interpreter lock prevents threads from making much use of more than a single core.
* - That is, CPython, which the implementation of Python that you get if you go to http://python.org and download Python. Other Python implementations (such as Jython) do not necessarily prohibit Python from running threads on multiple CPUs simultaneously. Thanks to #EOL for the clarification.
For these kinds of problems, neither threads nor forked processes seem the right approach. If all you want to do is to once every 15 minutes notify the user of something, why not use an event loop like GLib's or Twisted's reactor ? This allows you to schedule operations that should run once in a while, and get on with the rest of your program.
Using multiple processes lets you exploit multiple CPU cores at the same time, while, in CPython, using threads doesn't (threads take turns using a single CPU core) -- so, if you have CPU intensive work and absolutely want to use threads, you should consider Jython or IronPython; with CPython, this consideration is often enough to sway the choice towards the multiprocessing module and away from the threading one (they offer pretty similar interfaces, because multiprocessing was designed to be easily put in place in lieu of threading).
Net of this crucial consideration, threads might often be a better choice (performance-wise) on Windows (where making a new process is a heavy task), but less often on Unix variants (Linux, BSD versions, OpenSolaris, MacOSX, ...), since making a new process is faster there (but if you're using IronPython or Jython, you should check, on the platforms you care about, that this still applies in the virtual machines in question -- CLR with either .NET or Mono for IronPython, your JVM of choice for Jython).
Processes are much simpler. Just turn them loose and let the OS handle it.
Also, processes are often much more efficient. Processes do not share a common pool of I/O resources; they are completely independent.
Python's subprocess.Popen handles everything.
If by fork you mean os.fork then I would avoid using that. It is not cross platform and too low level - you would need to implement communication between the processes yourself.
If you want to use a separate process then use either the subprocess module or if you are on Python 2.6 or later the new multiprocessing module. This has a very similar API to the threading module, so you could start off using threads and then easily switch to processes, or vice-versa.
For what you want to do I think I would use threads, unless """does something""" is CPU intensive and you want to take advantage of multiple cores, which I doubt in this particular case.
I've been trying to wrap my head around how threads work in Python, and it's hard to find good information on how they operate. I may just be missing a link or something, but it seems like the official documentation isn't very thorough on the subject, and I haven't been able to find a good write-up.
From what I can tell, only one thread can be running at once, and the active thread switches every 10 instructions or so?
Where is there a good explanation, or can you provide one? It would also be very nice to be aware of common problems that you run into while using threads with Python.
Yes, because of the Global Interpreter Lock (GIL) there can only run one thread at a time. Here are some links with some insights about this:
http://www.artima.com/weblogs/viewpost.jsp?thread=214235
http://smoothspan.wordpress.com/2007/09/14/guido-is-right-to-leave-the-gil-in-python-not-for-multicore-but-for-utility-computing/
From the last link an interesting quote:
Let me explain what all that means.
Threads run inside the same virtual
machine, and hence run on the same
physical machine. Processes can run
on the same physical machine or in
another physical machine. If you
architect your application around
threads, you’ve done nothing to access
multiple machines. So, you can scale
to as many cores are on the single
machine (which will be quite a few
over time), but to really reach web
scales, you’ll need to solve the
multiple machine problem anyway.
If you want to use multi core, pyprocessing defines an process based API to do real parallelization. The PEP also includes some interesting benchmarks.
Python's a fairly easy language to thread in, but there are caveats. The biggest thing you need to know about is the Global Interpreter Lock. This allows only one thread to access the interpreter. This means two things: 1) you rarely ever find yourself using a lock statement in python and 2) if you want to take advantage of multi-processor systems, you have to use separate processes. EDIT: I should also point out that you can put some of the code in C/C++ if you want to get around the GIL as well.
Thus, you need to re-consider why you want to use threads. If you want to parallelize your app to take advantage of dual-core architecture, you need to consider breaking your app up into multiple processes.
If you want to improve responsiveness, you should CONSIDER using threads. There are other alternatives though, namely microthreading. There are also some frameworks that you should look into:
stackless python
greenlets
gevent
monocle
Below is a basic threading sample. It will spawn 20 threads; each thread will output its thread number. Run it and observe the order in which they print.
import threading
class Foo (threading.Thread):
def __init__(self,x):
self.__x = x
threading.Thread.__init__(self)
def run (self):
print str(self.__x)
for x in xrange(20):
Foo(x).start()
As you have hinted at Python threads are implemented through time-slicing. This is how they get the "parallel" effect.
In my example my Foo class extends thread, I then implement the run method, which is where the code that you would like to run in a thread goes. To start the thread you call start() on the thread object, which will automatically invoke the run method...
Of course, this is just the very basics. You will eventually want to learn about semaphores, mutexes, and locks for thread synchronization and message passing.
Note: wherever I mention thread i mean specifically threads in python until explicitly stated.
Threads work a little differently in python if you are coming from C/C++ background. In python, Only one thread can be in running state at a given time.This means Threads in python cannot truly leverage the power of multiple processing cores since by design it's not possible for threads to run parallelly on multiple cores.
As the memory management in python is not thread-safe each thread require an exclusive access to data structures in python interpreter.This exclusive access is acquired by a mechanism called GIL ( global interpretr lock ).
Why does python use GIL?
In order to prevent multiple threads from accessing interpreter state simultaneously and corrupting the interpreter state.
The idea is whenever a thread is being executed (even if it's the main thread), a GIL is acquired and after some predefined interval of time the
GIL is released by the current thread and reacquired by some other thread( if any).
Why not simply remove GIL?
It is not that its impossible to remove GIL, its just that in prcoess of doing so we end up putting mutiple locks inside interpreter in order to serialize access, which makes even a single threaded application less performant.
so the cost of removing GIL is paid off by reduced performance of a single threaded application, which is never desired.
So when does thread switching occurs in python?
Thread switch occurs when GIL is released.So when is GIL Released?
There are two scenarios to take into consideration.
If a Thread is doing CPU Bound operations(Ex image processing).
In Older versions of python , Thread switching used to occur after a fixed no of python instructions.It was by default set to 100.It turned out that its not a very good policy to decide when switching should occur since the time spent executing a single instruction can
very wildly from millisecond to even a second.Therefore releasing GIL after every 100 instructions regardless of the time they take to execute is a poor policy.
In new versions instead of using instruction count as a metric to switch thread , a configurable time interval is used.
The default switch interval is 5 milliseconds.you can get the current switch interval using sys.getswitchinterval().
This can be altered using sys.setswitchinterval()
If a Thread is doing some IO Bound Operations(Ex filesystem access or
network IO)
GIL is release whenever the thread is waiting for some for IO operation to get completed.
Which thread to switch to next?
The interpreter doesn’t have its own scheduler.which thread becomes scheduled at the end of the interval is the operating system’s decision. .
Use threads in python if the individual workers are doing I/O bound operations. If you are trying to scale across multiple cores on a machine either find a good IPC framework for python or pick a different language.
One easy solution to the GIL is the multiprocessing module. It can be used as a drop in replacement to the threading module but uses multiple Interpreter processes instead of threads. Because of this there is a little more overhead than plain threading for simple things but it gives you the advantage of real parallelization if you need it.
It also easily scales to multiple physical machines.
If you need truly large scale parallelization than I would look further but if you just want to scale to all the cores of one computer or a few different ones without all the work that would go into implementing a more comprehensive framework, than this is for you.
Try to remember that the GIL is set to poll around every so often in order to do show the appearance of multiple tasks. This setting can be fine tuned, but I offer the suggestion that there should be work that the threads are doing or lots of context switches are going to cause problems.
I would go so far as to suggest multiple parents on processors and try to keep like jobs on the same core(s).