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

Running edge devices with TinyML

After the model is trained using the federated learning approach that we have discussed so far, we want to deploy the trained model and conduct efficient model inference/serving. This leads to the concept of TinyML.

The deploy hardware of edge devices usually has a lot of constraints. Let's look at these constraints and how we can tackle them:

  • Limited battery power: This means that our deployment should be very efficient and cannot consume a lot of battery power.
  • Unstable connection to the server: This means that we need to guarantee that the model is still usable if the device cannot connect to the server.
  • High latency for communication: This means that if some emergency happens, the model deployed on the device can handle it without coordinating with the central server.
  • Data locality: This means that we need to keep each device's local data private and never allow the local data to communicate with other devices.
  • ...