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

Hyperparameter tuning in model parallelism

In this section, we will discuss some of the important hyperparameters required during the model parallel training process, such as balancing the workload among GPUs and enabling/disabling pipeline parallelism.

Balancing the workload among GPUs

In most of the cases, we split the model layer-wise. Since we use homogenous GPUs, we should try to balance the workload among all the GPUs we have.

GPU workload is not always linearly proportional to the number of layers held inside the GPU. One way to balance the workload among GPUs is to look at its computation core utilization. This computation utility value can be found in nvidia-smi. For example, the following screenshot shows that GPU0 has a greater workload than GPU1 – Volatile GPU-Util on GPU0 is 42%, whereas on GPU1, it is 20%:

Figure 7.12 – GPUs are underutilized

Thus, we need to move some of the layers originally assigned on GPU0 to GPU1...