[ONLINE] Fundamentals of Deep Learning for Multi-GPUs @ IT4Innovations

CET
[ONLINE]

[ONLINE]

Description

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.

Links
    • 09:45 10:00
      Presentation 15m
    • 10:00 10:20
      Intro
    • 10:20 12:00
      Stochastic Gradient Descent
    • 12:00 13:00
      Lunch Break 1h
    • 13:00 14:20
      Introduction to Distributed Training
    • 14:20 14:30
      Coffee Break 10m
    • 14:30 15:45
      Algorithmic Challenges of Distributed SGD
    • 15:45 16:00
      Q & A