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 Federated

Here, we will discuss TensorFlow Federated (TFF) as a case study.

TFF is based on TensorFlow and enables TensorFlow to conduct federated learning.

In order to use it, you need to first install it as follows:

# installation
#first step
pip3 install tensorflow
# GPU support
pip3 install tensorflow-gpu
#second step
pip3 install tensorflow_federated

After installing, you can make a function call by importing the libraries you installed as follows:

# To use TensorFlow Federated
# First Step
import tensorflow as tf
# Second Step
import tensorflow_federated as tff

After that, you can start writing code for federated learning using TensorFlow.

In a nutshell, TFF mainly has two-layer APIs as follows:

  • Federated Learning APIs
  • Federated Core APIs

These two layers are shown as the top two layers in the following figure:

Figure 10.7 – Two-layer (top two) structure of TFF

As shown in the preceding...