Python provides two ways to work around this issue: threading and multiprocessing. Each approach allows you to break a long-running job into parallel batches, which you can work on side-by-side.

The best parallel processing libraries for Python. Ray: Parallelizes and distributes AI and machine learning workloads across CPUs, machines, and GPUs.; Dask: Parallelizes Python data science ...