WebFeb 2, 2024 · I am also somewhat new to this, but I believe your error is because your block (in_, out, Activation) function is defined as a nested function inside init () and according … WebJul 9, 2024 · Solution 1 ⭐ Don't use a generator expression when you want to pickle data. Use a list comprehension instead, or call list() on the generator to capture all generated elements for pickling. For ex...
Fit generator with yield generator. Cannot Pickle
WebNov 28, 2024 · Upstream issue: Make `torch.Generator` picklable · Issue #43672 · pytorch/pytorch · GitHub. acxz (acxz) July 5, 2024, 7:34pm 4. If you post the entire definition of the class, maybe we can help out. My guess is that you might have a self.torch_seed function call. Try to separate that call from the object you are pickling i.e. your Policy class. the parkland ratchada - wongsawang
TypeError: can
WebOct 14, 2024 · from multiprocessing import Process from queue import Queue import logging def main(): x = DataGenerator() try: x.run() except Exception as e: … Webuse_multiprocessing=True won't work with generator input if your generator takes unpickable arguments (e.g. other generators) as arguments.. You could either just set use_multiprocessing=False (at expense of some performance probably) or make sure that you don't have unpickable arguments (from the code you've provided it's hard to say … WebApr 10, 2024 · Using a generator is helpful for memory management by efficiently processing data in smaller chunks, which can prevent overloading the RAM. Additionally, utilizing multiprocessing can reduce time complexity by allowing for parallel processing of tasks. So I will try to find a way to solve this problem. – Anna Yerkanyan. the parklands at the meadows