The problem is in this line: with pattern.findall(row) as f: You are using the with statement. It requires an object with __enter__ and __exit__ methods. But pattern.findall returns a list, with tries to store the __exit__ method, but it can’t find it, and raises an error. Just use f = pattern.findall(row) instead.
The previously accepted solution has race conditions and it does not work with map and async functions. The correct way to handle Ctrl+C/SIGINT with multiprocessing.Pool is to: Make the process ignore SIGINT before a process Pool is created. This way created child processes inherit SIGINT handler. Restore the original SIGINT handler in the parent process … Read more
Perhaps things become clearer if we look at what would happen if instead we simply wrote Parallel(n_jobs=8)(getHog(i) for i in allImages) which, in this context, could be expressed more naturally as: Create a Parallel instance with n_jobs=8 create a generator for the list [getHog(i) for i in allImages] pass that generator to the Parallel instance … Read more
Multiprocessing copies connection objects between processes because it forks processes, and therefore copies all the file descriptors of the parent process. That being said, a connection to the SQL server is just a file, you can see it in linux under /proc//fd/…. any open file will be shared between forked processes. You can find more … Read more
The problem is that the counter variable is not shared between your processes: each separate process is creating it’s own local instance and incrementing that. See this section of the documentation for some techniques you can employ to share state between your processes. In your case you might want to share a Value instance between … Read more
If you are on Linux (or any POSIX-compliant system), you can define this array as a global variable. multiprocessing is using fork() on Linux when it starts a new child process. A newly spawned child process automatically shares the memory with its parent as long as it does not change it (copy-on-write mechanism). Since you … Read more
It might be most sensible to use multiprocessing.Pool which produces a pool of worker processes based on the max number of cores available on your system, and then basically feeds tasks in as the cores become available. The example from the standard docs (http://docs.python.org/2/library/multiprocessing.html#using-a-pool-of-workers) shows that you can also manually set the number of cores: … Read more
Because this is still a very high result on google and no one else has mentioned it yet, I thought I would mention the new possibility of ‘true’ shared memory which was introduced in python version 3.8.0: https://docs.python.org/3/library/multiprocessing.shared_memory.html I have here included a small contrived example (tested on linux) where numpy arrays are used, which … Read more
The multiprocessing.pool.ThreadPool behaves the same as the multiprocessing.Pool with the only difference that uses threads instead of processes to run the workers logic. The reason you see hi outside of main() being printed multiple times with the multiprocessing.Pool is due to the fact that the pool will spawn 5 independent processes. Each process will initialize … Read more
The subprocess module lets you run and control other programs. Anything you can start with the command line on the computer, can be run and controlled with this module. Use this to integrate external programs into your Python code. The multiprocessing module lets you divide tasks written in python over multiple processes to help improve … Read more