What are the best practices for using the Python multiprocessing library?

What are the best practices for using the Python multiprocessing library?

When using the Python multiprocessing library, it's important to follow best practices like avoiding shared state, using queues for communication, and ensuring proper cleanup of processes.

Can you explain why avoiding shared state is important?

Avoiding shared state is crucial because it can lead to race conditions and bugs that are hard to debug. Each process should operate independently.

What are some common pitfalls to watch out for?

Common pitfalls include not managing process lifecycles properly, failing to handle exceptions in child processes, and using too many processes which can lead to overhead.

How can I effectively use queues for communication?

You can use multiprocessing.Queue to send data between processes. It's thread-safe and allows for easy communication without shared state.