The standard Python workaround to the GIL is multiprocessing. Multiprocessing is basically a library which spins up a distributed system running locally - it forks your process, and runs workers in the forks. The parent process then communicates with the child processes via unix pipes, TCP, or some such method, allowing multiple cores to be used.
Multiprocessing is the way to do parallelism. Deviating from that should be an exception -- for example, shared memory maps could be used to transfer select data objects instantaneously between the processes instead of serializing/deserializing over a pipe, and only those while still retaining separate process images. Threads were practically invented as a compensation for systems with heavy process image overhead.
I think Python is very Unix in this regard. And that's not a bad thing per se. Unix and Linux can do multiprocessing very efficiently.