Data Parallelism: How to Train Deep Learning Models on Multiple GPUs (DPHTDLM)


Course Overview

This workshop teaches you techniques for data-parallel deep learning training on multiple GPUs 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 how to decrease model training time by distributing data to multiple GPUs, while retaining the accuracy of training on a single GPU.

Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.


Experience with deep learning training using Python

Course Objectives

By participating in this workshop, you’ll:

  • Understand how data parallel deep learning training is performed using multiple GPUs
  • Achieve maximum throughput when training, for the best use of multiple GPUs
  • Distribute training to multiple GPUs using Pytorch Distributed Data Parallel
  • Understand and utilize algorithmic considerations specific to multi-GPU training performance and accuracy

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Prices & Delivery methods

Online Training

1 day

  • 500.— €
Classroom Training

1 day

  • Austria: 500.— €
  • Germany: 500.— €

Currently there are no training dates scheduled for this course.