UH Calendar

NVIDIA DLI Workshop: Building and Deploying Large Neural Networks

Friday, April 5, 2024

9:00 am - 5:00 pm

Register

Open to current UH students, faculty and staff. CougarNet login required to register.  

Large language models (LLMs) and deep neural networks (DNNs), whether applied to natural language processing, computer vision, or speech AI, have distinguishable properties from their smaller counterparts. As LLMs and DNNs are trained on progressively larger datasets, they can adapt to new tasks with just a few training examples, accelerating the route toward general artificial intelligence. Training models that contain billions of parameters on vast datasets requires a unique combination of AI, high-performance computing (HPC), and systems knowledge. This course will demonstrate how to train the largest of neural networks and deploy them to production. 

In this workshop, you’ll learn how to:

• Scale training and deployment of LLMs and neural networks across multiple nodes.
• Use techniques such as activation checkpointing, gradient accumulation, and various forms of model parallelism to overcome the challenges associated with large-model memory footprint.
• Capture and understand training performance characteristics to optimize model architecture.
• Deploy very large multi-GPU, multi-node models to production using NVIDIA Triton™ Inference Server.

Prerequisite(s):

• Good understanding of PyTorch.
• Good understanding of deep learning and data parallel training concepts.
• Practice with multi-GPU training and natural language processing are useful, but optional.

Pricing details:

• The normal price is $500 per attendee, but this workshop is offered to UH students for free.

Certificate: 

Upon successful completion of the assessment, participants will receive an NVIDIA certificate to recognize their subject matter competency and support their professional career.

The NVIDIA Deep Learning Institute (DLI) offers instructor-led workshops for developing key skills in AI, accelerated data science, or accelerated computing.