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[ONLINE] Fundamentals of Deep Learning for Multi-GPUs (EuroCC)
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Europe/Prague
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[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:
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Approaches to multi-GPUs training
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Algorithmic and engineering challenges to large-scale training
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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
intermediate
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 2.x.
About the tutor
Georg Zitzlsberger is a research specialist for Machine and Deep Learning at IT4Innovations. He has for over three years been certified by NVIDIA as a University Ambassador of the NVIDIA Deep Learning Institute (DLI) programme. This certification allows him to offer NVIDIA DLI courses to academic users of IT4Innovations' HPC services. In addition, in collaboration with Bayncore, he is a trainer for Intel HPC and AI workshops and conferences carried out across Europe. He has been contributing to these events, which are held for audiences from industry and academia, for five years.
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 event was supported by the EuroCC project. This project has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951732. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and Germany, Bulgaria, Austria, Croatia, Cyprus, the Czech Republic, Denmark, Estonia, Finland, Greece, Hungary, Ireland, Italy, Lithuania, Latvia, Poland, Portugal, Romania, Slovenia, Spain, Sweden, the United Kingdom, France, the Netherlands, Belgium, Luxembourg, Slovakia, Norway, Switzerland, Turkey, Republic of North Macedonia, Iceland, Montenegro. This project has received funding from the Ministry of Education, Youth and Sports of the Czech Republic (ID:MC2101).
This course is supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90140).
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