Deep Learning and GPU programming using OpenACC

 1^st day: Fundamentals of Deep Learning for Computer Vision (9:00 - 17:00,  Dr. Juan Durillo Barrionuevo)

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.

2^nd day: Fundamentals of Deep Learning for Multiple Data Types  (9:00 - 17:00,  Dr. Juan Durillo Barrionuevo)

This day explores how convolutional and recurrent neural networks can be combined to generate effective descriptions of content within images and video clips.

Learn how to train a network using TensorFlow and the Microsoft Common Objects in Context (COCO) dataset to generate captions from images and video by:

• Implementing deep learning workflows like image segmentation and text generation
• Comparing and contrasting data types, workflows, and frameworks
• Combining computer vision and natural language processing

Upon completion, you’ll be able to solve deep learning problems that require multiple types of data inputs.

3^rd day: Guest Lecture & Fundamentals of Accelerated Computing with OpenACC (9:00 - 17:00)

On the 3rd day we start with a guest lecture (9:00 - 10:30) about Deep Neural Networks for Data-Driven Turbulence Models, using DL in CFD.
Dr.-Ing. Andrea Beck, Institute of Aerodynamics and Gas Dynamics, University of Stuttgart. The abstract can be found here. Slides of the talk.

In the second part (10:45 - 17:00, by Dr. Volker Weinberg) 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.

4^th day: ML Examples and Methods on HLRS systems (9:00 - 16:30)

On the 4th day we cover Pre-processing of data, mathematical methods and machine learning as well as Deep learning on HLRS Systems.

• Focus on Pre-processing, Feature Engineering and Machine Learning:
  Stuttgart S-Bahn Example (Dr. Lorenzo Zanon, Li Zhong and Dennis Hoppe, HLRS)

• Data Compression of numerical data sets with the BigWhoop library (Patrick Vogler, HLRS in cooperation with EXCELLERAT)

• Waste classification using deep learning (Dr. Khatuna Kakhiani)

Technical background, basic understanding of machine learning concepts, basic C/C++ or Fortran programming skills.

For the 1st and 4th day basics in Python will be helpful. Since Python is used, the following tutorial can be used to learn the syntax: docs.python.org/2.7/tutorial/index.html

For the 2nd day familiarity with TensorFlow will be a plus as all the hands-on sessions are using TensorFlow. For those who do not program in TensorFlow, please go over TensorFlow tutorial (especially the "Learn and use ML" section): www.tensorflow.org/tutorials/

On the first three days, the excercises will be carried out on a cloud system using Jupyter Notebooks. On the last day, the exercises are done on HLRS systems.

English 

Dr. Juan Durillo Barrionuevo (LRZ), Dr. Volker Weinberg (LRZ) and Dr. Momme Allalen (LRZ), Dr. -Ing. Andrea Beck (IAG), Dr. Khatuna Kakhiani (HLRS), Dr. Lorenzo Zanon (HLRS) and Patrick Vogler (HLRS)

Registration is now closed.

This course is only open to academicals, i. e. students or members of universities. Please register with your institutional email address.

Important Information: After you are accepted, please create an account under courses.nvidia.com/join.

Deadline

for registration is June 28, 2020.

Fee

• Students without Diploma/Master: 0 EUR
• Students with Diploma/Master (PhD students) at German universities: 0 EUR
• Members of German universities and public research institutes: 0 EUR
• Members of universities and public research institutes within EU or PRACE member countries: 0 EUR.
• Members of other universities and public research institutes: 0 EUR

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.

Travel Information and Accommodation

see our How to find us page.

PRACE PATC and bwHPC-C5

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 not part of the PATC curriculum and is not sponsored by the PATC program.

Local Organizer

Rolf Rabenseifner phone 0711 685 65530, rabenseifner(at)hlrs.de
Lucienne Dettki phone 0711 685 63894, dettki(at)hlrs.de

http://www.hlrs.de/training/2020/DL1

http://www.hlrs.de/training/ and http://www.hlrs.de/training/overview/