Model Parallelism: Building and Deploying Large Neural Networks Training

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.
Course Details


1 day


  • 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

Skills Gained

  • 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.
Course Outline
  • Introduction
  • Introduction to Training of Large Models
    • Learn about the motivation behind and key challenges of training large models.
    • Get an overview of the basic techniques and tools needed for large-scale training.
    • Get an introduction to distributed training and the Slurm job scheduler.
    • Train a Megatron-LM-based GPT model using data parallelism.
    • Profile the training process and understand execution performance.
  • Model Parallelism: Advanced Topics
    • Increase the model size using a range of memory-saving techniques.
    • Get an introduction to tensor and pipeline parallelism.
    • Go beyond natural language processing and get an introduction to DeepSpeed.
    • Auto-tune model performance.
    • Learn about mixture-of-experts models.
  • Inference of Large Models
    • Understand the challenges of deployment associated with large models.
    • Explore techniques for model reduction.
    • Learn how to use NVIDIA® TensorRT™ and Faster Transformer libraries.
    • Learn how to use Triton Inference Server.
    • Understand the process of deploying GPT checkpoint to production.
    • See an example of prompt engineering.
  • Final Review