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You're reading from  Mastering Azure Machine Learning

Product typeBook
Published inApr 2020
Reading LevelBeginner
PublisherPackt
ISBN-139781789807554
Edition1st Edition
Languages
C#
Tools
Azure
Concepts
Machine Learning
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Authors (2):
Christoph Körner
Christoph Körner
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Christoph Körner

Christoph Körner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.
Read more about Christoph Körner

Kaijisse Waaijer
Kaijisse Waaijer
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Kaijisse Waaijer

Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
Read more about Kaijisse Waaijer

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Summary

Distributed ML is a great approach to scaling out your training infrastructure in order to gain speed in your training process. It is applied in many real-world scenarios and is very easy to use with Horovod and Azure Machine Learning.

Parallel execution is similar to hyperparameter search, while distributed execution is similar to Bayesian optimization, which we discussed in detail in the previous chapter. Distributed executions need methods to perform communication (such as one-to-one, one- to-many, many-to-one, and many-to-many) and synchronization (such as barrier synchronization) efficiently. These so-called collective algorithms are provided by communication backends (MPI, Gloo, and NCCL) and allow efficient GPU-to-GPU communication.

DL frameworks build higher-level abstractions on top of communication backends to perform model-parallel and data-parallel training. In data-parallel training, we partition the input data to compute multiple independent parts of the...

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Mastering Azure Machine Learning
Published in: Apr 2020Publisher: PacktISBN-13: 9781789807554
© 2020 Packt Publishing Limited All Rights Reserved

Authors (2)

author image
Christoph Körner

Christoph Körner previously worked as a cloud solution architect for Microsoft, specializing in Azure-based big data and machine learning solutions, where he was responsible for designing end-to-end machine learning and data science platforms. He currently works for a large cloud provider on highly scalable distributed in-memory database services. Christoph has authored four books: Deep Learning in the Browser for Bleeding Edge Press, as well as Mastering Azure Machine Learning (first edition), Learning Responsive Data Visualization, and Data Visualization with D3 and AngularJS for Packt Publishing.
Read more about Christoph Körner

author image
Kaijisse Waaijer

Kaijisse Waaijer is an experienced technologist specializing in data platforms, machine learning, and the Internet of Things. Kaijisse currently works for Microsoft EMEA as a data platform consultant specializing in data science, machine learning, and big data. She works constantly with customers across multiple industries as their trusted tech advisor, helping them optimize their organizational data to create better outcomes and business insights that drive value using Microsoft technologies. Her true passion lies within the trading systems automation and applying deep learning and neural networks to achieve advanced levels of prediction and automation.
Read more about Kaijisse Waaijer

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