Due to COVID-19, the event will be POSTPONED! A new date will be announced as soon as the situation with COVID-19 is stabilised.
Overview
NVIDIA Deep Learning Institute (DLI) offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.
Learn how to train and deploy a neural network to solve real-world problems, how to effectively parallelize training of deep neural networks on Multi-GPUs, and how to accelerate your applications with OpenACC.
The workshop combines lectures about fundamentals of Deep Learning for Computer Vision and for Multi-GPUs with a lecture about Accelerated Computing with OpenACC.
The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.
This workshop is co-organized by Nvidia, VSC (Austria), LRZ (Germany) and IT4Innovations (Czech republic). All instructors are NVIDIA certified University Ambassadors.
Agenda
1st day, June 3, 2020: Fundamentals of Deep Learning for Computer Vision
Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.
During this day, you’ll learn the basics of deep learning by training and deploying neural networks. You’ll learn how to:
- Implement common deep learning workflows, such as image classification and object detection
- Experiment with data, training parameters, network structure, and other strategies to increase performance and capability
- Deploy your neural networks to start solving real-world problems
Upon completion, you’ll be able to start solving problems on your own with deep learning.
2nd day, June 4, 2020: Fundamentals of Accelerated Computing with OpenACC
On the 2nd day you learn the basics of OpenACC, a high-level programming language for programming on GPUs. Discover how to accelerate the performance of your applications beyond the limits of CPU-only programming with simple pragmas. You’ll learn:
- How to profile and optimize your CPU-only applications to identify hot spots for acceleration
- How to use OpenACC directives to GPU accelerate your codebase
- How to optimize data movement between the CPU and GPU accelerator
Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.
3rd day, June 5, 2020: Fundamentals of Deep Learning for Multi-GPUs
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.
On the 3rd day 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
Upon completion, you'll be able to effectively parallelize training of deep neural networks using TensorFlow.
Important information
After you are accepted, please create an account under courses.nvidia.com/join.
Course Fee
The workshop is free of charge for all academic participants and coffee breaks will be provided (lunch is not included). Please note, that the workshop is exclusively for verifiable students, staff, and researchers from any academic institution (for industrial participants, please contact NVIDIA for industrial specific training). On the first day of the workshop, please bring your student/academia id.
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.
 
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