I’ve created a Conditional DCGAN to work on the MNIST data. It involves applying some transformations and reversing them before feeding the data to the Conditional DGAN for training. Therefore I’ve created a custom dataset from custom transformations on the MNIST.
However, the training time is too long. One epoch took about 3 hours. I’ve modified the Data loader to use num_workers=6 and pin_memory=True, and still see the training is painstakingly slow. How can I speed this up?
Using num_workers=16 throws error:
assert self._parent_pid == os.getpid(), 'can only join a child process'

AssertionError: can only join a child process
Is there any other way to increase training speed?
Full code can be found at: https://colab.research.google.com/drive/1gUr54oAwONwqCRWcWc2lMKSQ8ZOJxw0N 5