I want to warn people against multiprocessing in python though.
If you're thinking about parallelizing your Python process, chances are your Python code is CPU-bound. That's when you should stop and think, is Python really the right tool for this job?
From experience, translating a Python program into C++ or Rust often gives a speed-up of around 100x, without introducing threads. Go probably has a similar level of speed-up. So while you can throw a lot of time fighting Python to get it to consume 16x the compute resources for a 10x speed-up, you could often instead spend a similar amount of time rewriting the program for a 100x speed-up with the same compute resources. And then you could parallelize your Go/Rust/C++ program for another 10x, if necessary.
Of course, this is highly dependent on what you're actually doing. Maybe your Python code isn't the bottleneck, maybe your code spends 99% of its time in datastructure operations implemented in C and you need to parallelize it. Or maybe your use-case is one where you could use pypy and get the required speed-up. I just recognize from my own experience the temptation of parallelizing some Python code because it's slow, only to find that the parallelized version isn't that much faster (my computer is just hotter and louder), and then giving in and rewriting the code in C++.
The first thing you should do is profile the code (py-spy is my preferred option) and see if there are any obvious hotspots. Then I'd actually look at the code, and understand what the structure is. For example, are you making lots of unnecessary copies of data? Are you recomputing something expensive you can store (functools.cache is one line and can make things much faster at the cost of memory)?
Once you've done that, then you should be familiar enough the code to know which bits are worth using multiprocessing on (i.e. the large embarrassingly parallel bits), which if they are a significant part of your code should scale near linearly.
The other thing to check is which libraries are you using (and what are your dependencies using). numpy now includes openblas (though mkl may be faster for your usecase), but sometimes you can achieve large speedups though choosing a different library, or ensuring speedups are being built.
If you're thinking about parallelizing your Python process, chances are your Python code is CPU-bound. That's when you should stop and think, is Python really the right tool for this job?
From experience, translating a Python program into C++ or Rust often gives a speed-up of around 100x, without introducing threads. Go probably has a similar level of speed-up. So while you can throw a lot of time fighting Python to get it to consume 16x the compute resources for a 10x speed-up, you could often instead spend a similar amount of time rewriting the program for a 100x speed-up with the same compute resources. And then you could parallelize your Go/Rust/C++ program for another 10x, if necessary.
Of course, this is highly dependent on what you're actually doing. Maybe your Python code isn't the bottleneck, maybe your code spends 99% of its time in datastructure operations implemented in C and you need to parallelize it. Or maybe your use-case is one where you could use pypy and get the required speed-up. I just recognize from my own experience the temptation of parallelizing some Python code because it's slow, only to find that the parallelized version isn't that much faster (my computer is just hotter and louder), and then giving in and rewriting the code in C++.