Title

AWS re:Invent 2022 - Deep learning on AWS with NVIDIA: From training to deployment (PRT219)

Summary

NVIDIA Solution Architects Eddie and Jehong discuss the full pipeline of deep learning on AWS using NVIDIA's hardware and software.
NVIDIA's offerings include GPUs (like A100s, H100s, and Jetson for edge computing), NVIDIA AI Enterprise software, and application workflows (Clara, Merlin, Nemo, etc.).
The partnership between NVIDIA and AWS spans machine learning, virtual workstations, high-performance computing, and IoT at the edge.
AWS instances powered by NVIDIA GPUs include P4D, G5, G5G, and others, with software integrations like Amazon SageMaker and NVIDIA Triton Inference Server.
NGC (NVIDIA GPU Cloud) is a repository containing optimized containers, models, and deployment tools.
NVIDIA TAO Toolkit simplifies the training of computer vision and conversational AI models, offering pre-trained models and low-code solutions.
Nemo Megatron is NVIDIA's solution for training large language models, providing tools for hyperparameter tuning, distributed training, and state-of-the-art optimization techniques.
NVIDIA Triton Inference Server is a model server for deploying optimized models, supporting multiple frameworks and providing performance enhancements.
TensorRT is an inference performance framework that optimizes models for deployment, offering significant speedups and support for various use cases.
NVIDIA and AWS have integrated solutions like Triton Inference Server with SageMaker for efficient and scalable model deployment.

NVIDIA's deep integration with AWS provides a comprehensive ecosystem for customers to train and deploy AI models efficiently on the cloud.
The NGC repository is a critical resource for obtaining NVIDIA-optimized software, which can lead to significant performance improvements without altering existing code.
NVIDIA TAO Toolkit addresses the complexity of developing custom pipelines for different computer vision tasks by providing a unified framework and pre-trained models.
Nemo Megatron's hyperparameter tool and convergence recipes are particularly valuable for training large language models, which can be resource-intensive and complex.
NVIDIA's hardware advancements, such as the A100 and H100 GPUs with Tensor Cores and transformer engines, are designed to accelerate deep learning workloads significantly.
TensorRT's optimization capabilities, such as reduced precision and layer fusion, are essential for achieving high-performance inference, especially for real-time applications.
Triton Inference Server's support for multiple frameworks and dynamic batching makes it a versatile solution for deploying a variety of AI models in production environments.
The collaboration between NVIDIA and AWS on tools like SageMaker and Triton Inference Server demonstrates a strong commitment to simplifying and enhancing the AI deployment process for end-users.