Thread by @akshay_pachaar

Multiprocessing in Python clearly explained:

Ever felt like your Python code could run faster❓

Multiprocessing might be the solution you're looking for!

Today, I'll simplify it for you in this step-by-step guide.

Let's go! 🚀

Let's start with an example where we run a simple function twice sequentially (without multiprocessing).

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Let's visually understand what happened in the code above & how multi processing can help here.

• Sequential execution: task 2 starts only when task 1 is finished.

• Parallel execution: both tasks are performed at the same time in parallel, on separate CPU cores

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Now that we understand the difference between sequential & parallel execution!

Let’s add multiprocessing to the mix and see the difference in execution time! ⏰

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But why stop there? Let’s run our function multiple times using a for loop to see the real power of multiprocessing!

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To make it even simpler, we can use a ProcessPool!

The recommended way to write multi-processing code in Python.

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OK, last but not least let's do one more interesting thing before we wrap it up!

Let's modify task() to take sleep_time as an argument & observe how execution order changes.

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Multiprocessing is ideal for CPU-bound tasks (intensive calculations, data processing), as each process operates in its own memory space.

Where as multithreading suits I/O-bound tasks (network requests, file I/O), where threads share memory within the same process.

Interested in:

- Python 🐍
- ML/AI Engineering ⚙️

Find me → @akshay_pachaar ✔️

Enjoyed today's tutorial❓
Check out my book for more: bit.ly/InstantPython