There are many options to run Python tasks in parallel. This provides a brief description for some of the options.

• threading and multiprocessing are part of the standard library. threading is used for thread-based parallelism, while multiprocessing is used for process-based parallelism. If your tasks involve Python objects that lock the Global Interpreter Lock (GIL), then threading does not provide much parallelism, and you should opt for multiprocessing. On the other hand, if your tasks involve NumPy objects that release the GIL, then threading is a better choice. In general, threading has less overheads compared to multiprocessing.

• concurrent.futures is also part of the standard library. It provides a high-level interface for asynchronous parallelism (using Future objects). You can easily choose between a “thread” pool or a “process” pool by using ThreadPoolExecutor or ProcessPoolExecutor. If you are running user-level codes, I believe concurrent.futures is usually the best option. But if you are writing more low-level codes, I believe you still want to choose between threading or multiprocessing. Note that you can still use threads from multiprocessing by using multiprocessing.pool.ThreadPool.

• dask is a powerful library that helps with the common pains dealing with large data and parallel computing (e.g. delayed, lazy loading, array chunking, distributed computing). There are two kinds of schedulers: the single-machine scheduler (default) and the more advanced dask.distributed. The advanced dask.distributed provides asynchronous parallelism similar to concurrent.futures and can be used on a cluster. But if you are just running codes on a single machine, the default scheduler should suffice (it requires zero setup). You can set the scheduler to “threads”, “processes” or “single-threaded”. The Best Practices pages (1, 2, 3) contain some very useful examples.

• joblib is a lightweight library that also provides lazy evaluation and parallel computing. For process-based parallelism, it uses the alternative serialization library cloudpickle instead of pickle, which allows you to serialize more things.

I want to mention that Keras and Tensorflow also have built-in parallelism. So, if you are dealing with ML stuff, you should be able to use what is offered by Keras/Tensorflow (e.g. keras.Model.fit). However, I think the documentation is kind of difficult to go through.

Simple code snippets:

def fun(x):
return x * x

# No parallelism
result = map(fun, range(10))

# Using concurrent.futures
import concurrent.futures
with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
result = executor.map(fun, range(10))

# Using multiprocessing
import multiprocessing
with multiprocessing.Pool(processes=4) as pool:
result = pool.imap(fun, range(10))