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.

Prerequisites

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

Duration
1 day

Price
  • on request
Classroom Training

Duration
1 day

Price
  • on request

Click on town name or "Online Training" to book Schedule

Instructor-led Online Training:   This is an Instructor-Led Online (ILO) course. These sessions are conducted via WebEx in a VoIP environment and require an Internet Connection and headset with microphone connected to your computer or laptop. If you have any questions about our online courses, feel free to contact us via phone or Email anytime.

United States

Online Training 07:30 Pacific Daylight Time (PDT) This course is being delivered by a partner Enroll