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)
1
Section 1 – Data Parallelism
6
Section 2 – Model Parallelism
11
Section 3 – Advanced Parallelism Paradigms

Single-node training error – out of memory

Giant NLP models, such as BERT, are often hard to train using a single GPU (that is, single-node). The main reason is due to the limited on-device memory size.

Here, we will first fine-tune the BERT model using a single GPU. The dataset we will use is SQuAD 2.0. It often throws an Out-of-Memory (OOM) error due to the giant model size and huge intermediate results size.

Second, we will use a state-of-the-art GPU and try our best to pack the relatively small BERT-base model inside a single GPU. Then, we will carefully adjust the batch size to avoid an OOM error.

Fine-tuning BERT on a single GPU

The first thing we need to do is to install the transformers library on our machine. Here, we use the transformers library provided by Hugging Face. The following command is how we install it on an Ubuntu machine using PyTorch:

$ pip install transformers

Please make sure you are installing the correct transformers version (...