Distributed Machine Learning with Python

Distributed Machine Learning with Python

eBook Details:

  • Paperback: 284 pages
  • Publisher: WOW! eBook (April 29, 2022)
  • Language:¬†English
  • ISBN-10: 1801815690
  • ISBN-13: 978-1801815697

eBook Description:

Distributed Machine Learning with Python: Build and deploy an efficient data processing pipeline for machine learning model training in an elastic, in-parallel model training or multi-tenant cluster and cloud

Reducing time cost in machine learning leads to a shorter waiting time for model training and a faster model updating cycle. Distributed machine learning enables machine learning practitioners to shorten model training and inference time by orders of magnitude. With the help of this practical guide, you’ll be able to put your Python development knowledge to work to get up and running with the implementation of distributed machine learning, including multi-node machine learning systems, in no time.

You’ll begin by exploring how distributed systems work in the machine learning area and how distributed machine learning is applied to state-of-the-art deep learning models. As you advance, you’ll see how to use distributed systems to enhance machine learning model training and serving speed. You’ll also get to grips with applying data parallel and model parallel approaches before optimizing the in-parallel model training and serving pipeline in local clusters or cloud environments.

  • Deploy distributed model training and serving pipelines
  • Get to grips with the advanced features in TensorFlow and PyTorch
  • Mitigate system bottlenecks during in-parallel model training and serving
  • Discover the latest techniques on top of classical parallelism paradigm
  • Explore advanced features in Megatron-LM and Mesh-TensorFlow
  • Use state-of-the-art hardware such as NVLink, NVSwitch, and GPUs

By the end of this Distributed Machine Learning with Python book, you’ll have gained the knowledge and skills needed to build and deploy an efficient data processing pipeline for machine learning model training and inference in a distributed manner.

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