Fundamentals of Deep Learning for Multi-GPUs

Programme Overview

This workshop teaches you techniques for training deep neural networks on multi-GPU technology to shorten the training time required for data-intensive applications. Working with deep learning tools, frameworks, and workflows to perform neural network training, you’ll learn concepts for implementing Horovod multi-GPUs to reduce the complexity of writing efficient distributed software.

Course Content

At the conclusion of the workshop, you’ll have an understanding of:

Various approaches to multi-GPU training
Algorithmic and engineering challenges to the large-scale training of a neural network
The linear neuron model and the loss function and optimization logic for gradient descent
Concepts for transforming single-GPU implementation to Horovod multi-GPU implementation to reduce the complexity of writing efficient distributed software
Techniques that improve overall performance of the entire pipeline

Fundamentals of Deep Learning for Multi-GPUs

Programme Overview

Course Content

Key Information

Prerequisites

Libraries, Tools & Framework

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