If you run it, you will get a result similar to this: Let’s take a look: from multiprocessing import Process def proc(i): print(f'I am Process ’) if _name_ = ‘_main_’: # Create manager m = Manager() # Create multiprocessing queue q = m.Queue() # Create a group of parallel writers and start them for i in range(num_writers): Process(target=writer, args=(i,q,)).start() # Create multiprocessing pool p = Pool(num_readers, init_worker) # Create a group of parallel readers and start them # Number of readers is matching the number of writers # However, the number of simultaneously running # readers is constrained to the pool size readers = for i in range(10): readers.append(p.apply_async(reader, (i,q,))) # Wait for the asynchrounous reader threads to finish try: except: print(‘Interrupted’) p.terminate() p.join() Starting a process(es) requires 2 things: the target function called and the Process call itself. Processobjects represent activity that is run in a separate process. Below, I provide a small tutorial on how to use these data structures and objects.įor our large array of parallel threads on the left we are going to use multithreading.Process(). Yet, there are small nuances and gaps in documentation which took me some time to understand (especially when using multiprocessing on Windows). Combination of queue ( multiprocessing.Queue) for passing down the work from builder threads to pusher threads and thread pool ( multiprocessing.Pool) looked like a best candidate. At that point I decided to limit the simultaneous uploads to the small number of parallel threads, while still utilizing large number of threads to facilitate image builds. In my testing, I was only able to run 2–3 simultaneous docker push commands until all the new ones I add got stalled. However, after some initial testing I discovered that pushing multiple images to registry got stalled likely due to an overload of simultaneous uploads. ![]() Docker SDK for Python provided an excellent handle on that, and together with `multiprocessing` library allowed to parallelize the task very effectively. I was recently confronted with a problem: I needed to build a large number (order of 100) of Docker containers and then push them to the registry. How to process your data in parallel with a predefined number of threads
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