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DTSTAMP:20260703T120226Z
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DTSTART:20220516T080000Z
DTEND:20220519T140000Z
DESCRIPTION:Overview\n\nLearn how to accelerate your applications with Open
 ACC and CUDA\, how to train and deploy a neural network to solve real-worl
 d problems\, and how to effectively parallelize training of deep neural ne
 tworks on Multi-GPUs.\n\nThe online workshop combines lectures about Accel
 erated Computing with OpenACC and CUDA with lectures about Fundamentals of
  Deep Learning for single and for Multi-GPUs.\n\nThe lectures are interlea
 ved with many hands-on sessions using Jupyter Notebooks. The exercises wil
 l be done on a fully configured GPU-accelerated workstation in the cloud.\
 n\nThe workshop is co-organised by LRZ and NVIDIA Deep Learning Institute 
 (DLI) for the Partnership for Advanced Computing in Europe (PRACE). LRZ as
  part of GCS is one of the currently 14 PRACE Training Centres which serve
  as European hubs and key drivers of advanced high-quality training for re
 searchers working in the computational sciences.\n\nNVIDIA DLI offers hand
 s-on training for developers\, data scientists\, and researchers looking t
 o solve challenging problems with deep learning.\n\nAll instructors are NV
 IDIA certified University Ambassadors.\n\nAgenda\n\n1st day: Fundamentals 
 of Accelerated Computing with OpenACC (10:00-16:00 CEST)\n\nOn the first d
 ay you learn the basics of OpenACC\, a high-level programming language for
  programming on GPUs. Discover how to accelerate the performance of your a
 pplications beyond the limits of CPU-only programming with simple pragmas.
  You’ll learn:\n\n\n	\n	How to profile and optimize your CPU-only applic
 ations to identify hot spots for acceleration\n	\n	\n	How to use OpenACC d
 irectives to GPU accelerate your codebase\n	\n	\n	How to optimize data mov
 ement between the CPU and GPU accelerator\n	\n\n\nUpon completion\, you'll
  be ready to use OpenACC to GPU accelerate CPU-only applications.\n\n2nd d
 ay: Fundamentals of Accelerated Computing with CUDA C/C++ (10:00-16:00 CES
 T)\n\nThe CUDA computing platform enables the acceleration of CPU-only app
 lications to run on the world’s fastest massively parallel GPUs. On the 
 2nd day you experience C/C++ application acceleration by:\n\n\n	\n	Acceler
 ating CPU-only applications to run their latent parallelism on GPUs\n	\n	\
 n	Utilizing essential CUDA memory management techniques to optimize accele
 rated applications\n	\n	\n	Exposing accelerated application potential for 
 concurrency and exploiting it with CUDA streams\n	\n	\n	Leveraging command
  line and visual profiling to guide and check your work\n	\n\n\nUpon compl
 etion\, you’ll be able to accelerate and optimize existing C/C++ CPU-onl
 y applications using the most essential CUDA tools and techniques. You’l
 l understand an iterative style of CUDA development that will allow you to
  ship accelerated applications fast.\n\n3rd day: Fundamentals of Deep Lear
 ning (10:00-16:00 CEST)\n\nExplore the fundamentals of deep learning by tr
 aining neural networks and using results to improve performance and capabi
 lities.\n\nDuring this day\, you’ll learn the basics of deep learning by
  training and deploying neural networks. You’ll learn how to:\n\n\n	Impl
 ement common deep learning workflows\, such as image classification and ob
 ject detection\n	Experiment with data\, training parameters\, network stru
 cture\, and other strategies to increase performance and capability\n	Depl
 oy your neural networks to start solving real-world problems\n\n\nUpon com
 pletion\, you’ll be able to start solving problems on your own with deep
  learning.\n\n4th day: Fundamentals of Deep Learning for Multi-GPUs (10:00
 -16:00 CEST)\n\nThe computational requirements of deep neural networks use
 d 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 dat
 asets like those used in self-driving car research. Using multiple GPUs fo
 r deep learning can significantly shorten the time required to train lots 
 of data\, making solving complex problems with deep learning feasible.\n\n
 On the last day we will teach you how to use multiple GPUs to train neural
  networks. You'll learn:\n\n\n	\n	Approaches to multi-GPUs training\n	\n	\
 n	Algorithmic and engineering challenges to large-scale training\n	\n	\n	K
 ey techniques used to overcome the challenges mentioned above\n	\n\n\nUpon
  completion\, you'll be able to effectively parallelize training of deep n
 eural networks using TensorFlow.\n\nImportant information\n\nAfter you are
  accepted\, please create an account under courses.nvidia.com/join .\n\nEn
 sure your laptop / PC will run smoothly by going to http://websocketstest.
 com/ Make sure that WebSockets work for you by seeing under Environment\, 
 WebSockets is supported and Data Receive\, Send and Echo Test all check Ye
 s under WebSockets (Port 80).If there are issues with WebSockets\, try upd
 ating your browser. If you have any questions\, please contact Marjut Dier
 inger at mdieringer"at"nvidia.com. \n\nNVIDIA Deep Learning Institute\n\n
 The NVIDIA Deep Learning Institute delivers hands-on training for develope
 rs\, 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 c
 ars\, healthcare\, online services\, and robotics.\n\nPrerequisites\n\nTec
 hnical background\, basic understanding of machine learning concepts\, bas
 ic C/C++ or Fortran programming skills.\nFor the 3rd day\, basics in Pytho
 n will be helpful. The following tutorial can be used to learn the syntax:
  docs.python.org/2.7/tutorial/index.html\nFor the 4th day\, familiarity wi
 th TensorFlow and Keras will be a plus as used in the hands-on sessions. F
 or those who did not use these before\, you can find tutorials here: githu
 b.com/tensorflow/docs/tree/master/site/en/r1/tutorials/keras.\n\nHands-On\
 n\nThe lectures are interleaved with many hands-on sessions using Jupyter 
 Notebooks. The exercises will be done on a fully configured GPU-accelerate
 d workstation in the cloud.\n\nContent Level\n\nThe content level of the c
 ourse is broken down as:\n\n\n	\n		\n			Beginner's content:\n			20%\n		\n	
 	\n			Intermediate content:\n			55%\n		\n		\n			Advanced content:\n			25%\
 n		\n		\n			Community-targeted content:\n			0%\n		\n	\n\n\nLanguage\n\nEng
 lish\n\nLecturer\n\nDr. Momme Allalen\, PD Dr. Juan Durillo Barrionuevo\, 
 Dr. Volker Weinberg (LRZ and NVIDIA University Ambassadors)\n\nPrices and 
 Eligibility\n\nThe course is open and free of charge for academic particip
 ants from the Member States (MS) of the European Union (EU) and Associated
  Countries to the Horizon 2020 programme.\n\n \n\n\n   \n\n \n\nhttps:
 //events.prace-ri.eu/event/1363/
SUMMARY:[ONLINE] Deep Learning and GPU Programming Workshop @ LRZ
URL;VALUE=URI:https://events.prace-ri.eu/event/1363/
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