Distributed Machine Learning with Python
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Distributed Machine Learning with Python
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Overview of this book
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. By the end of this 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.
Table of Contents (17 chapters)
Preface
Section 1 – Data Parallelism
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Chapter 1: Splitting Input Data
Chapter 2: Parameter Server and All-Reduce
Chapter 3: Building a Data Parallel Training and Serving Pipeline
Chapter 4: Bottlenecks and Solutions
Section 2 – Model Parallelism
Chapter 5: Splitting the Model
Chapter 6: Pipeline Input and Layer Split
Chapter 7: Implementing Model Parallel Training and Serving Workflows
Chapter 8: Achieving Higher Throughput and Lower Latency
Section 3 – Advanced Parallelism Paradigms
Chapter 9: A Hybrid of Data and Model Parallelism
Chapter 10: Federated Learning and Edge Devices
Chapter 11: Elastic Model Training and Serving
Chapter 12: Advanced Techniques for Further Speed-Ups
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