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AI Shiksha

APPLIED ACCELERATED ARTIFICIAL INTELLIGENCE

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The Nodal Centres for Training in HPC and AI set upunder the aegis of the National Super computing Mission (NSM) in association with NVIDIA & OpenACC will conduct a 33 hrs lecture series on Applied Accelerated Artificial Intelligence.

Learning Assessment

An end of course examination will be conducted online. Top performers will be given Book Prizes The successful candidates will be awarded a certificate with grades. Others will be given certificates of participation.

Course Objectives

This course will cover the fundamentals of the compute capabilities and the system software required for implementing artificial intelligence (AI) based solutions on industrial use cases such as the ones in the domains of healthcare and Smart City. The course will discuss end to end deployments of two industrial use cases with demonstration, and hence will help participants use state-of-the-art AI SDKs effectively to solve complex problems.

Program

Course conducted by NSM Nodal Centers in collaboration with Nvidia and OpenACC
Content created and delivered by leading faculty and industry experts
Unique Opportunity to learn the fundamentals of hardware acceleration for AI tasks
Demonstrations and Code walkthroughs
Industrial use-cases of Accelerated AI

Important Dates:

Course Starts: January 31, 2022

Course Registration Period: 06 January 2022 to 16 January 2022


Course Topics Instructors

  1. Introduction to AI System Hardware CPU, RAM, GPU, Interconnects, Storage, Network Controller
  2. Introduction to System Software Operating System, Virtualization, Cloud; (Lecture)
  3. Frameworks for Accelerated Deep Learning Workloads - PyTorch, Accelerated Py
  4. TorchFrameworks for Accelerated Deep Learning Workloads - TensorFlow, Accelerated TensorFlow
  5. Industrial use-cases of Accelerated AI
 Dr. Satyajit Das

  1. Introduction to AI Accelerators GPUs
  2. Introduction to Containers and IDE (Jupyter Lab)
  3. Scheduling and Resource Management Introduction to schedulers and orchestration tools
  4. Fundamentals of Distributed AI Computing: Multi-GPU and multi-node implementation (MPI, NCCL, RDMA)
  5. Distributed AI Computing: Horovod
  6. Challenges with Distributed Deep Learning Training Convergence
  7. Accelerated Data Analytics
  8. Accelerated Machine Learning
  9. Scale Out with DASK
  10. Web visualizations to GPU accelerated crossfiltering
  11. Applied AI: Smart City
 Mr. Bharatkumar Sharma

  1. Optimizing Deep Learning Training: Automated Mixed Precision
  2. Optimizing Deep Learning Training: Transfer Learning

Adesuyi Tosin

  1. Fundamentals of Accelerating Deployment
  2. Accelerating neural network inference in PyTorch and TensorFlow

Prof Satyadhyan Chickerur
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