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Fundamentals of Deep Learning for Multi-GPUs
Fundamentals of Deep Learning for Multi-GPUs
Instructor Led Workshop
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 and to maintain accuracy when training a model across many GPUs.
Objectives
At the conclusion of the workshop, you’ll have an understanding of Stochastic Gradient Descent (SGD) - a crucial tool in parallelised training; batch size and its effect on training time and accuracy; transforming a single-GPU implementation to a Horovod multi-GPU implementation and techniques for maintaining high accuracy when training across multiple GPUs.
Agenda
Time | Task |
---|---|
10.00 - 10.15 | Introductions |
10.15 - 12.00 | Principles of Stochastic Gradient Descent (SGD) |
12.00 - 13.00 | Lunch Break |
13.20 - 14.20 | Introduction to Distributed Training |
14.20 - 14.30 | Coffee Break |
14.30 - 15.00 | Virtualisation of GPUs with Run:AI software |
15.00 - 16.15 | Algorithmic Challenges of Distributed SGD |
16.30 - 16.30 | Q&A |
Upon completion of the assessment, participants will receive an NVIDIA DLI certificate to recognise their subject matter competency and support professional career growth.
Workshop Details
Duration
allow 8 hours (full day)
Prerequisites
experience with gradient descent model training
Technologies used
TensorFlow, Keras, Horovod
Assessment Type
Code-based
Hardware Requirements
Desktop or laptop computer capable of running the latest version of Chrome or Firefox. Each participant will be provided with dedicated access to a fully configured, GPU-accelerated server in the cloud
Cost
£75 per person
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