Python's "multiprocessing" module feels like threads, but actually launches processes. Many people, when they start to work with Python, are excited to hear that the language supports threading. And, ...

Python provides two ways to work around this issue: threading and multiprocessing. Each approach allows you to break a long-running job into parallel batches, which you can work on side-by-side.

Learn how to use Python’s async functions, threads, and multiprocessing capabilities to juggle tasks and improve the responsiveness of your applications.

Fortunately for us, the Python developers worked hard to create a multiprocessing module which has an interface that is almost identical to the threading module.

I do this all the time. Post the results for each row to a multiprocessing.Queue, and spawn a single process that gets from the queue and writes to the file. It'll post some code when I get to work.

Threads can provide concurrency, even if they're not truly parallel. In my last article, I took a short tour through the ways you can add concurrency to your programs. In this article, I focus on one ...

Ruby and Python's standard implementations make use of a Global Interpreter Lock. Justin James explains the major advantages and downsides of the GIL mechanism.

Llama 2 API with multiprocessing The video tutorial below provides valuable insights into creating an API for the Llama 2 language model, with a focus on supporting multiprocessing with PyTorch.

The resource leak detector targets open file descriptors, as well as leaks on connections, sessions, open sockets, and multiprocessing thread pools.