How to Run Processes in Parallel using Python

Discover how to efficiently run multiple inference sessions simultaneously in Python with multithreading and multiprocessing techniques.
---
This video is based on the question https://stackoverflow.com/q/77487252/ asked by the user 'Johnzy' ( https://stackoverflow.com/u/12030711/ ) and on the answer https://stackoverflow.com/a/77488685/ provided by the user 'Mike McKerns' ( https://stackoverflow.com/u/2379433/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, comments, revision history etc. For example, the original title of the Question was: how to run this process in parallel

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/licensing
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/by-sa/4.0/ ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Run Processes in Parallel using Python

When working with large datasets or complex model inferences in Python, you may encounter the need to run multiple processes in parallel. This can significantly speed up the inference time as it allows your code to leverage multiple CPU cores. In this guide, we’ll explore how to run inference sessions in parallel using Python's multiprocess library and how to successfully implement multithreading for tasks that may be CPU-bound.

The Problem of Serial Processing

In your initial code, you are utilizing the multiprocessing library, which runs processes in parallel. However, for models like those created in ONNX, the inference processing might be performed more efficiently if these sessions run simultaneously using threads.

Key Considerations:

Multithreading can be more lightweight due to shared memory usage and is suitable for I/O-bound tasks.

Multiprocessing creates separate memory spaces for each process, which is outfitted for CPU-bound operations, but may introduce overhead in terms of memory usage.

The Solution: Leveraging Multiprocess and Threading

1. Using the Right Library

To run inference sessions in parallel, a good place to start is by switching from multiprocessing to multiprocess. This library extends the existing multiprocessing capabilities and makes it easier to handle objects across multiple processes. Here’s how to get started with it:

[[See Video to Reveal this Text or Code Snippet]]

2. Implementing a Pool for Parallel Execution

You can run tasks in parallel using a Pool. This will encapsulate multiple Process instances and allows you to distribute tasks more neatly. Here’s a simple pattern to follow:

[[See Video to Reveal this Text or Code Snippet]]

This way, you can efficiently manage multiple inference sessions, making the process swift and efficient.

3. Example Code

Here's a more comprehensive example based on the code you’ve provided:

[[See Video to Reveal this Text or Code Snippet]]

4. Conclusion

With the proper utilization of multiprocess and Pool, running multiple inference sessions in parallel becomes a breeze. This approach aligns well with high-performance requirements necessary for dealing with large datasets or intensive computations.

Whether you're completing simultaneous tasks or optimizing your code for performance, implementing these techniques will elevate the efficiency of your Python workflow.

By understanding and applying the concepts of parallel processing using multiprocess, you’re equipped to handle various computational challenges with ease.

Видео How to Run Processes in Parallel using Python канала vlogize