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

Key Features

• Accelerate model training and interference with order-of-magnitude time reduction
• Learn state-of-the-art parallel schemes for both model training and serving
• A detailed study of bottlenecks at distributed model training and serving stages

Book Description

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 ML 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 ML, including multi-node ML 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.

What you will learn

• Deploy distributed model training and serving pipelines with deep learning platforms
• Effectively analyze and mitigate system bottlenecks during in-parallel model training and inference
• Discover state-of-the-art techniques built on top of the classical parallelism paradigm
• Explore advanced features with the latest versions of Megatron-LM and Mesh-TensorFlow
• Get acquainted with state-of-the-art hardware accelerators used for distributed ML model training and inference
• Use state-of-the-art hardware for in-parallel training, such as NVLink, NVSwitch, TPUs, and GPUs

Who This Book Is For

This book is for data scientists, machine learning engineers, and ML practitioners in both academia and industry. A fundamental understanding of machine learning concepts and working knowledge of Python programming is assumed. Prior experience implementing ML/DL models with TensorFlow or PyTorch will be beneficial. You'll find this book useful if you are interested in using distributed systems to boost machine learning model training and serving speed.

Table of Contents

1. Splitting Input Data
2. Parameter Server and AllReduce
3. Building Data Parallel Training and Serving Pipeline
4. Bottlenecks and Solutions
5. Splitting the Model
6. Pipeline Input and Layer Split
7. Implementing Model Parallel Training and Serving Workflows
8. Higher Throughput and Lower Latency
9. A Hybrid of Data and Model Parallelism
10. Federated Learning and Edge Devices
11. Elastic Model Training and Serving
12. Advanced Techniques for Further Speed-Ups