This course will be provided as ONLINE course (using Newrow).
NVIDIA Deep Learning Institute (DLI) offershands-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 generate effective descriptions of content within images and how to accelerate your applications with OpenACC.
The workshop combines lectures on Fundamentals of Deep Learning and Fundamentals of Deep Learning for Multi-GPUs with a lecture on 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.
The workshop is organized in cooperation Nvidia.
On the last day, you will learn more about details of Nvidia's GPU architecture and how to use containers for DL on the systems at HLRS.
- Preleminary -
In this workshop, you’ll learn how deep learning works through hands-on exercises in computer vision and natural language processing. You’ll train deep learning models from scratch, learning tools and tricks to achieve highly accurate results. You’ll also learn to leverage freely available, state-of-the-art pre-trained models to save time and get your deep learning application up and running quickly.
An understanding of fundamental programming concepts in Python 3, such as functions, loops, dictionaries, and arrays; familiarity with Pandas data structures; and an understanding of how to compute a regression line.
Suggested materials to satisfy prerequisites: Python Beginner’s Guide.
This workshop teaches you techniques for training deep neural networks on multi-GPU technology 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 concepts for implementing Horovod multi-GPUs to reduce the complexity of writing efficient distributed software and to maintain accuracy when training a model across many GPUs.
Experience with Deep Learning using Python 3 and, in particular, gradient descent model training.
Learn the basics of OpenACC, a high-level programming language for programming on GPUs. This course is for anyone with some C/C++ experience who is interested in accelerating the performance of their applications beyond the limits of CPU-only programming.
Basic experience with C/C++. Suggested Resources to satisfy prerequisites The learn-c.org interactive tutorial, https://www.learn-c.org/.
In this workshop details of (Nvidia's) GPU architecture and fundamentals of GPU programming will be introduced. Further, in the second part, an introduction on how to use unprivileged container solutions, e.g., Singularity, in HPC environments and methods to use existing Docker containers from Nvidia's GPU Cloud in HPC environments
If you want to test containers in HLRS HPC systems, you need an account.
The course language is English.
Jonny Hancox, Gunter Roth and Dai Yang from Nvidia.
Please register only for the days that you will attend. I.e., if you want to participate in the course "Fundamentals of Deep Learning" only, please only register for Day 1 and so on.
If the course is full, then please register to the waiting list, so that we can inform you about available places as soon as possible.
Please choose the matching registration queue
for registration is July 4, 2021 (extended deadline).
This course is free of charge.
HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.
HLRS is also member of the Baden-Württemberg initiative bwHPC-C5.
This course is provided within the framework of the bwHPC-C5 user Support.
This course is not part of the PATC curriculum and is not sponsored by the PATC program.