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

Key Information


Experience with stochastic gradient descent

Libraries, Tools & Framework


Share this

Share on linkedin
Share on facebook
Share on twitter
Share on whatsapp
Share on telegram