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

Case study: TensorFlow Lite

Given the four challenges defined in TinyML, the TensorFlow team implemented a specific platform for TinyML called TensorFlow Lite.

Now let's talk about how TensorFlow Lite handles each of the TinyML challenges one by one here.

First, to reduce the total power consumption, TensorFlow Lite can run the model without maintaining the following metadata:

  • Layer dependency
  • Computation graph
  • Holding intermediate results

Second, to avoid the unstable connection issue, TensorFlow Lite removes all the unnecessary communication between the server and devices. Once the model is deployed on the device, normally no specific communication is needed between the central server and the deployed devices.

Third, to reduce the high latency for communication, TensorFlow Lite enables faster (real-time) model inference by doing the following:

  • Reducing the code footprint
  • Directly feeding the data into the model as the data does not...