Model Parallelism: Building and Deploying Large Neural Networks

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. The goal of this course is to demonstrate how to train the largest of neural networks and deploy them to production

• 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.

• 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