multiprocessing

问题 So, since several days I do a lot of research about multiprocessing and multithreading on python and i'm very confused about many thing. So many times I see someone talking about GIL something that doesn't allow Python code to execute on several cpu cores, but when I code a program who create many threads I can see several cpu cores are active. 1st question: What's is really GIL? does it work? I think about something like when a process create too many thread the OS distributed task on multi

问题 So, since several days I do a lot of research about multiprocessing and multithreading on python and i'm very confused about many thing. So many times I see someone talking about GIL something that doesn't allow Python code to execute on several cpu cores, but when I code a program who create many threads I can see several cpu cores are active. 1st question: What's is really GIL? does it work? I think about something like when a process create too many thread the OS distributed task on multi

问题 Here is a simple example of using keyword arguments in a function call. Nothing special. def foo(arg1,arg2, **args): print arg1, arg2 print (args) print args['x'] args ={'x':2, 'y':3} foo(1,2,**args) Which prints, as expected: 1 2 {'y': 3, 'x': 2} 2 I am trying to pass the same style keyword arguments to a multiprocessing task, but the use of **, in the args list is a syntax error. I know that my function, stretch() will take two positional arguments and n keyword arguments. pool =

问题 I am trying to implement multiprocessing, but I am having difficulties accessing information from the object scans that I'm passing through the pool.map() function Before multiprocessing (this works perfectly): for sc in scans: my_file = scans[sc].resources['DICOM'].files[0] After multiprocessing (does not work, error shown below): def process(x): my_file = x.resources['DICOM'].files[0] def another_method(): ... pool = Pool(os.cpu_count()) pool.map(process, [scans[sc] for sc in scans])

问题 I am trying to implement multiprocessing, but I am having difficulties accessing information from the object scans that I'm passing through the pool.map() function Before multiprocessing (this works perfectly): for sc in scans: my_file = scans[sc].resources['DICOM'].files[0] After multiprocessing (does not work, error shown below): def process(x): my_file = x.resources['DICOM'].files[0] def another_method(): ... pool = Pool(os.cpu_count()) pool.map(process, [scans[sc] for sc in scans])

问题 Can someone explain why this code blocks and cannot complete? I've followed a couple of examples for multiprocessing and I've writting some very similar code that does not get blocked. But, obviously, I cannot see what is the difference between that working code and that below. Everything sets up fine, I think. It gets all the way to .get(), but none of the processes ever finish. The problem is that python3 blocks indefinitely in waiter.acquire(), which you can tell by interrupting it and

问题 I am finding it incredibly difficult to follow rays guidelines to running a docker image on a ray cluster in order to execute a python script. I am finding a lack of simple working examples. So I have the simplest docker file: FROM rayproject/ray WORKDIR /usr/src/app COPY . . CMD ["step_1.py"] ENTRYPOINT ["python3"] I use this to create can image and push this to docker hub. ("myimage" is just an example) docker build -t myimage . docker push myimage "step_1.py" just prints hello every second

问题 To improve my code which has one heavy loop I need a speed up. How can I implement multiprocessing for a code like this? (a is typical of size 2 and l up to 10) for x1 in range(a**l): for x2 in range(a**l): for x3 in range(a**l): output[x1,x2,x3] = HeavyComputationThatIsThreadSafe1(x1,x2,x3) 回答1: If the HeavyComputationThatIsThreadSafe1 function only uses arrays and not python objects, I would using a concurrent futures (or the python2 backport) ThreadPoolExecutor along with Numba (or cython)

问题 How to make sure that all the pool.apply_async() calls are executed and their results are accumulated through callback before a premature call to pool.close() and pool.join() ? numofProcesses = multiprocessing.cpu_count() pool = multiprocessing.Pool(processes=numofProcesses) jobs=[] for arg1, arg2 in arg1_arg2_tuples: jobs.append(pool.apply_async(function1, args=(arg1, arg2, arg3,), callback=accumulate_apply_async_result)) pool.close() pool.join() 回答1: You need to wait on the appended

问题 How to make sure that all the pool.apply_async() calls are executed and their results are accumulated through callback before a premature call to pool.close() and pool.join() ? numofProcesses = multiprocessing.cpu_count() pool = multiprocessing.Pool(processes=numofProcesses) jobs=[] for arg1, arg2 in arg1_arg2_tuples: jobs.append(pool.apply_async(function1, args=(arg1, arg2, arg3,), callback=accumulate_apply_async_result)) pool.close() pool.join() 回答1: You need to wait on the appended