Book Image

Distributed Machine Learning with Python

By : Guanhua Wang
Book Image

Distributed Machine Learning with Python

By: Guanhua Wang

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)
Section 1 – Data Parallelism
Section 2 – Model Parallelism
Section 3 – Advanced Parallelism Paradigms

The data parallel training pipeline in a nutshell

In this section, we will mainly focus on using the All-Reduce-based data parallel architecture. Here, we will wrap up the whole data parallel training pipeline. The whole training workflow is shown in the following diagram:

Figure 3.1 – Parameter server architecture with a single server node

As we can see, the training pipeline of each worker consists of six steps:

  1. Input Pre-Processing: Given the raw training input data, we need to pre-process it. Common input pre-processing techniques include image crop, image flip, input data normalization, and many more.
  2. Input Data Partition: Split the whole input dataset into multiple chunks and assign each chunk to one accelerator for the model training process.
  3. Data Loading: Load the data partition into the accelerators we use to train the model.
  4. Training: Train the model locally with its training input data.
  5. Model Synchronization: After...