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, and how to effectively parallelize training of deep neural networks on Multi-GPUs.
The workshop combines the DLI courses Fundamentals of Deep Learning with the Deep Learning for multi-GPUs courses Data Parallelism: How To Train Deep Learning Models on Multiple GPUs and Model Parallelism: Building and Deploying Large Neural Networks. The lectures are interleaved with many hands-on sessions using Jupyter Notebooks.
This course is organized in cooperation with LRZ (Germany). The instructor is an NVIDIA certified University Ambassador.
HLRS, University of Stuttgart Nobelstraße 19 70569 Stuttgart, Germany Room 0.439 / Rühle Saal Location and nearby accommodations
Jul 02, 2024 13:00
Jul 05, 2024 17:00
Stuttgart, Germany
English
Intermediate
Data in HPC / Deep Learning / Machine Learning
Artificial Intelligence
Big Data
Deep Learning
GPU Programming
Machine Learning
Scientific Machine Learning
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For day one, you need basic experience with C/C++ or Fortran. Suggested resources to satisfy prerequisites: the learn-c.org interactive tutorial, https://www.learn-c.org/. Familiarity with MPI is a plus.
On day two, you need 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 resources to satisfy prerequisites: Python Beginner’s Guide. Familiarity with TensorFlow and Keras will be a plus as it will be used in the hands-on sessions. For those who did not use these before, you can find tutorials here: github.com/tensorflow/docs/tree/master/site/en/r1/tutorials/keras.
Experience with Deep Learning using Python 3 and, in particular, gradient descent model training will be needed on day three and four. Further, expericen with PyTorch will be helpful, see https://pytorch.org/tutorials/ for instance.
Please be aware that while the second day offers an introduction or recap of Deep Learning most of the topics in this course are rather advanced. If you are completely unfamiliar with Deep Learning, the learning curve might be steep on days three and four.
Learn more about course curricula and content levels.
Lecture and assistant trainers: PD Dr. Juan Durillo Barrionuevo (LRZ and NVIDIA University Ambassador), Tobias Haas, Khatuna Kakhiani, and Lorenzo Zanon (HLRS).
1st day: Introduction to multi-GPU programming
2nd day: Introduction to Deep Learning
3rd day: Data Parallelism: How to Train Deep Learning Models on Multiple GPUs
4th day: Model Parallelism: Building and Deploying Large Neural Networks
- preliminary -
1st day (Tue): Introduction to multi-GPU programming (13:00 - 17:00)
On the first day you will learn the basics of multi-GPU programming. This will give you a rough idea how Deep Learning can be implemented using multi-GPUs.
2nd day (Wed): Introduction to Deep Learning (9:00 - 17:00)
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.
3rd day (Thu): Data Parallelism: How to Train Deep Learning Models on Multiple GPUs (9:00 - 17:00)
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 third day we will teach you how to use multiple GPUs to train neural networks.
4th day (Fri): Model Parallelism: Building and Deploying Large Neural Networks (9:00 - 17:00)
Exercises
The exercises will be carried out on cloud instances and on one of HLRS's clusters (on the first day).
Besides the content of the training itself, an important aspect of this event is the scientific exchange among the participants. We try to facilitate such communication by
Register via the button at the top of this page. We encourage you to register to the waiting list if the course is full. Places might become available.
If you are not interested in all days, please select only those days in which you are interested while registering.
Registration closes on June 16, 2024.
Late registrations after that date are still possible according to the course capacity, possibly with reduced quality of service.
Important Information: After you are accepted, please create an account under courses.nvidia.com/join.
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.
This course is open to academic participants only.
Our course fees include coffee breaks (in classroom courses only).
For lists of EU and EU-associated countries, and PRACE countries have a look at the Horizon Europe and PRACE website.
Tobias Haas phone 0711 685 87223, tobias.haas(at)hlrs.de
HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.
This course is provided within the framework of the bwHPC training program.
See the training overview and the Supercomputing Academy pages.
October 14 - 31, 2024
Online (flexible)
October 22, 2024
Online
October 23 - 25, 2024
Dresden, Germany
November 04 - December 13, 2024
November 04 - 08, 2024
November 04 - 06, 2024