Joseph Turian wrote a post about regarding pickling generator on his blog. In his post, he says:
However, generators become problematic when you want to persist your experiment’s state in order to later restart training at the same place. Unfortunately, you can’t pickle generators in Python. And it can be a bit of a PITA to workaround this, in order to save the training state.
This caught my attention, because I was involved in the decision, he cites, to not allow generators to be pickled in CPython. Although Joseph’s examples are a bit convoluted, it is pretty clear why his generators cannot be pickled automatically—i.e., Python cannot pickle the operating system’s state, like file descriptors.
Let’s ignore that problem for a moment and look what we would need to do to pickle a generator. Since a generator is essentially a souped-up function, we would need to save its bytecode, which is not guarantee to be backward-compatible between Python’s versions, and its frame, which holds the state of the generator such as local variables, closures and the instruction pointer. And this latter is rather cumbersome to accomplish, since it basically requires to make the whole interpreter picklable. So, any support for pickling generators would require a large number of changes to CPython’s core.
Now if an object unsupported by pickle (e.g., a file handle, a socket, a database connection, etc) occurs in the local variables of a generator, then that generator could not be pickled automatically, regardless of any pickle support for generators we might implement. So in that case, you would still need to provide custom
__setstate__ methods. This problem renders any pickling support for generators rather limited.
Anyway, if you need for a such feature, then look into Stackless Python which does all the above. And since Stackless’s interpreter is picklable, you also get process migration for free. This means you can interrupt a tasklet (the name for Stackless’s green threads), pickle it, send the pickle to a another machine, unpickle it, resume the tasklet, and voilà you’ve just migrated a process. This is freaking cool feature!
But in my humble opinion, the best solution to this problem to the rewrite the generators as simple iterators (i.e., one with a
__next__ method). Iterators are easy and efficient space-wise to pickle because their state is explicit. You would still need to handle objects representing some external state explicitly however; you cannot get around this.