This course is part of the "LRZ AI Training Series", a series of courses aiming at the needs and expectations of data analytics, big data & AI users at LRZ.
The course is organised as an on-site even at LRZ in Garching near Munich. There will be no possibility to join online remotely via video conference. Participants are expected to bring their own laptops running the latest version of Chrome or Firefox. There are no PCs installed in the course room!
Contents:
Modern deep learning challenges leverage increasingly larger datasets and more complex models. As a result, significant computational power is required to train models effectively and efficiently. Learning to distribute data across multiple GPUs during deep learning model training makes possible an incredible wealth of new applications utilizing deep learning.
Additionally, the effective use of systems with multiple GPUs reduces training time, allowing for faster application development and much faster iteration cycles. Teams who are able to perform training using multiple GPUs will have an edge, building models trained on more data in shorter periods of time and with greater engineer productivity.
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.
The course is co-organised by LRZ and NVIDIA Deep Learning Institute (DLI). All instructors are NVIDIA certified University Ambassadors.
Learning 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
