11 December 2020
[ONLINE]
CET timezone
Only for academia

Annotation

The computational requirements of deep neural networks used to enable AI applications like self-driving cars are enormous. A single training cycle can take weeks on a single GPU or even years for larger datasets like those used in self-driving car research. Using multiple GPUs for deep learning can significantly shorten the time required to train lots of data, making solving complex problems with deep learning feasible.

We will teach you how to use multiple GPUs to train neural networks. You'll learn:

  • Approaches to multi-GPUs training

  • Algorithmic and engineering challenges to large-scale training

  • Key techniques used to overcome the challenges mentioned above

This course is only offered to academia (see details below in section Capacity and Fees).

Level

beginner

Language

English

Purpose of the course (benefits for the attendees)

Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow. Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

About the tutor(s)

Georg Zitzlsberger is a research specialist for Machine and Deep Learning. He received his certification from Nvidia as a University Ambassador of the Nvidia Deep Learning Institute (DLI) program. This certification allows him to offer Nvidia DLI courses to academic users of IT4Innovations' HPC services.

NVIDIA Deep Learning Institute

The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

Acknowledgements

This course  is sponsored by NVIDIA as part of the NVIDIA Deep Learning Institute (DLI) University Ambassador program.

This event was partially supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project "e-Infrastruktura CZ – LM2018140“ and partially by the PRACE-6IP project - the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823767.

Starts
Ends
CET
[ONLINE]

Practicalities

This training will be an online event. Technical details about joining will be sent to the accepted registrants before the event.

Prerequisites

Basics in Python will be helpful. Since Python 2.7 is used, the following tutorial can be used to learn the syntax: docs.python.org/2.7/tutorial/index.html

The recommended browser for the course is a recent version of Chrome. Please ensure your laptop will run smoothly by going to websocketstest.com. Make sure that WebSockets work for you by seeing under Environment, WebSockets is supported and Data Receive, Send and Echo Test all check Yes under WebSockets (Port 80). If there are issues with WebSockets, try updating your browser.

Before the workshop please create an account under http://courses.nvidia.com/join using the same email address as for event registration.

Capacity and Fees

Capacity limited.

The workshop is free of charge for all academic participants.

Note, that the training is exclusively for verifiable students, staff, and researchers from any academic institution. For interested parties from industry, please contact us at training@it4i.cz.