You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Product typeBook

Published inJan 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781838826994

Edition2nd Edition

Languages

Python

Tools

Keras TensorFlow

Concepts

Deep Reinforcement Learning

Author (1)

Maxim Lapan

Multiprocessing in Python

Python includes the multiprocessing (most of the time abbreviated to just mp) module to support process-level parallelism and the required communication primitives. In our example, we will use the two main classes from this module:

mp.Queue: A concurrent multi-producer, multi-consumer FIFO (first in, first out) queue with transparent serialization and deserialization of objects placed in the queue
mp.Process: A piece of code that is run in the child process and methods to control it from the parent process

PyTorch provides its own thin wrapper around the multiprocessing module, which adds the proper handling of tensors and variables on CUDA devices and shared memory. It provides exactly the same functionality as the multiprocessing module from the standard library, so all you need to do is use import torch.multiprocessing instead of import multiprocessing.

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Deep Reinforcement Learning Hands-On. - Second Edition

Published in: Jan 2020Publisher: PacktISBN-13: 9781838826994

Author (1)

Maxim Lapan

Maxim has been working as a software developer for more than 20 years and was involved in various areas: distributed scientific computing, distributed systems and big data processing. Since 2014 he is actively using machine and deep learning to solve practical industrial tasks, such as NLP problems, RL for web crawling and web pages analysis. He has been living in Germany with his family.
Read more about Maxim Lapan

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You're reading from Deep Reinforcement Learning Hands-On. - Second Edition

Multiprocessing in Python

Unlock this book and the full library FREE for 7 days

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