Course Overview
Very large deep neural networks (DNNs), whether applied to natural language processing (e.g., GPT-3), computer vision (e.g., huge Vision Transformers), or speech AI (e.g., Wave2Vec 2) have certain properties that set them apart from their smaller counterparts. As DNNs become larger and are trained on progressively larger datasets, they can adapt to new tasks with just a handful of training examples, accelerating the route toward general artificial intelligence. Training models that contain tens to hundreds of billions of parameters on vast datasets isn’t trivial and requires a unique combination of AI, high-performance computing (HPC), and systems knowledge.
Please note that once a booking has been confirmed, it is non-refundable. This means that after you have confirmed your seat for an event, it cannot be cancelled and no refund will be issued, regardless of attendance.
Prerequisites
Familiarity with:
- Good understanding of PyTorch
- Good understanding of deep learning and data parallel training concepts
- Practice with deep learning and data parallel are useful, but optional
Course Objectives
In this workshop, participants will learn how to:
- Train neural networks across multiple servers
- 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 models to production using NVIDIA Triton™ Inference Server
English