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

Elasticity in model inference

After the model is fully trained, we can use it for parallel model inference. However, traditional model inference also needs to predefine how many workers/GPUs to use for a serving job.

Here, we discuss a simple solution of elastic model serving. It works as follows:

  • If the number of concurrent inference inputs is higher, we use more GPUs for this model-serving job.
  • If the number of concurrent inference inputs is lower, we shrink down the number of GPUs we use.

For example, right now we have received four concurrent model-serving queries, as shown in the following figure:

Figure 11.12 – Elastic model serving with more queries

As shown in the preceding figure, if we have more queries, we can use more GPUs to do concurrent model serving in order to reduce the model-serving latency.

On the contrary, if we have fewer queries, for example, we only have one query, as shown in the following figure, we...