Deep Learning Models on Multiple GPUs

Price
$500.00 USD

Duration
1 Day

Delivery Methods
Virtual Instructor Led
Private Group

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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 GP

Course Objectives

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

Who Should Attend?

Experienced Python Developers

Top-rated instructors: Our crew of subject matter experts have an average instructor rating of 4.8 out of 5 across thousands of reviews.
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Interactive classroom participation: Our virtual training includes live lectures, demonstrations and virtual labs that allow you to participate in discussions with your instructor and fellow classmates to get real-time feedback.
Post Class Resources: Review your class content, catch up on any material you may have missed or perfect your new skills with access to resources after your course is complete.
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Tailored Training Solutions: Our subject matter experts can customize the class to specifically address the unique goals of your team.

Course Prerequisites

Experience with deep learning training using Python

Agenda

Introduction
Stochastic Gradient Descent and the Effects of Batch Size
Training on Multiple GPUs with PyTorch Distributed Data Parallel (DDP)
Maintaining Model Accuracy when Scaling to Multiple GPUs
Workshop Assessment
Final Review

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