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

Model training in a heterogeneous environment

This is not a very general case. The motivation for heterogeneous DNN model training is that we may have some legacy hardware accelerators. For example, a company may have used NVIDIA K80 GPUs 10 years ago. Now the company purchases new GPUs such as NVIDIA V100. However, the older K80 GPUs are still usable and the company wants to use all the legacy hardware.

One key challenge of doing heterogeneous DNN model training is load balancing among different hardware.

Let's assume the computation power of each K80 is half of the V100. To achieve good load balancing, if we are doing data parallel training, we should assign N as the mini-batch size on K80 and 2*N as the mini-batch size on V100. If we are doing model-parallel training, we should assign 1/3 layers on K80 and 2/3 layers on V100.

Note that the preceding example for heterogeneous DNN training is simplified. In reality, it is much harder to achieve decent load balancing...