Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems (Paperback)
暫譯: 使用 Python 的分散式機器學習：透過分散式系統加速模型訓練與服務 (平裝本)

Wang, Guanhua

Distributed Machine Learning with Python: Accelerating model training and serving with distributed systems (Paperback)

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商品描述

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 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.

What you will learn

- 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

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.

商品描述(中文翻譯)

建立並部署一個高效的數據處理管道，用於在彈性、並行模型訓練或多租戶集群和雲端中進行機器學習模型訓練

主要特點

- 透過數量級的時間縮減來加速模型訓練和推斷
- 學習最先進的並行方案，適用於模型訓練和服務
- 詳細研究分散式模型訓練和服務階段的瓶頸

書籍描述

在機器學習中減少時間成本可以縮短模型訓練的等待時間，並加快模型更新的週期。分散式機器學習使機器學習從業者能夠將模型訓練和推斷時間縮短數量級。藉助這本實用指南，您將能夠運用您的 Python 開發知識，迅速實現分散式機器學習的實作，包括多節點機器學習系統。您將首先探索分散式系統在機器學習領域的運作方式，以及分散式機器學習如何應用於最先進的深度學習模型。隨著進展，您將看到如何使用分散式系統來提升機器學習模型訓練和服務的速度。您還將掌握應用數據並行和模型並行方法，然後優化在本地集群或雲端環境中的並行模型訓練和服務管道。在本書結束時，您將獲得建立和部署高效的數據處理管道所需的知識和技能，以便以分散的方式進行機器學習模型訓練和推斷。

您將學到的內容

- 部署分散式模型訓練和服務管道
- 熟悉 TensorFlow 和 PyTorch 的進階功能
- 減輕在並行模型訓練和服務過程中的系統瓶頸
- 探索經典並行主義範式之上的最新技術
- 探索 Megatron-LM 和 Mesh-TensorFlow 的進階功能
- 使用最先進的硬體，如 NVLink、NVSwitch 和 GPU

本書適合誰

本書適合數據科學家、機器學習工程師以及學術界和業界的機器學習從業者。假設讀者對機器學習概念有基本了解，並具備 Python 編程的工作知識。先前使用 TensorFlow 或 PyTorch 實作 ML/DL 模型的經驗將是有益的。如果您有興趣使用分散式系統來提升機器學習模型訓練和服務的速度，您會發現這本書非常有用。

作者簡介

Guanhua Wang is a final-year Computer Science PhD student in the RISELab at UC Berkeley, advised by Professor Ion Stoica. His research lies primarily in the Machine Learning Systems area including fast collective communication, efficient in-parallel model training and real-time model serving. His research gained lots of attention from both academia and industry. He was invited to give talks to top-tier universities (MIT, Stanford, CMU, Princeton) and big tech companies (Facebook/Meta, Microsoft). He received his master’s degree from HKUST and bachelor’s degree from Southeast University in China. He also did some cool research on wireless networks. He likes playing soccer and runs half-marathon multiple times in the Bay Area of California.

作者簡介(中文翻譯)

王冠華（Guanhua Wang）是加州大學伯克利分校（UC Berkeley）RISELab的計算機科學博士生，指導教授為Ion Stoica教授。他的研究主要集中在機器學習系統（Machine Learning Systems）領域，包括快速的集體通信（fast collective communication）、高效的並行模型訓練（efficient in-parallel model training）和實時模型服務（real-time model serving）。他的研究受到了學術界和業界的廣泛關注。他曾受邀在頂尖大學（麻省理工學院MIT、史丹佛大學Stanford、卡內基梅隆大學CMU、普林斯頓大學Princeton）和大型科技公司（Facebook/Meta、微軟Microsoft）進行演講。他在香港科技大學（HKUST）獲得碩士學位，並在中國東南大學獲得學士學位。他還在無線網絡方面進行了一些有趣的研究。他喜歡踢足球，並在加州灣區多次參加半程馬拉松。

目錄大綱

1. Splitting Input Data
2. Parameter Server and All-Reduce
3. Building a 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. Achieving 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

目錄大綱(中文翻譯)

1. Splitting Input Data

2. Parameter Server and All-Reduce

3. Building a 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. Achieving 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

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