DataBloom

NVIDIA Omniverse, a simulation and design collaboration platform for 3D virtual worlds, is already being evaluated by 700+ companies and 70,000 individuals.

TIME magazine today named NVIDIA Omniverse one of the 100 Best Inventions of 2021, saying the project is “making it easier to create ultra-realistic virtual spaces for…real-world purposes.”

Omniverse — a scalable, multi-GPU, real-time reference development platform for 3D simulation and design collaboration — is being evaluated by more than 700 companies and 70,000 individuals to create virtual worlds and unite teams, their assets, and creative applications in one streamlined interface. 

“Virtual worlds are for more than just gaming—they’re useful for planning infrastructure like roads and buildings, and they can also be used to test autonomous vehicles,” TIME wrote in the story, which hits the newsstands Nov. 22. “The platform combines the real-time ray-tracing technology of the brand’s latest graphics processing units with an array of open-source tools for collaborating live in photorealistic 3-D worlds.”

The list of 100 inventions, which TIME describes as “groundbreaking,” is based on multiple factors including originality, creativity, efficacy, ambition and impact. These projects, says TIME, are changing how we live, work, play and think about what’s possible.

In this week’s GTC keynote, NVIDIA CEO Jensen Huang announced the general availability of NVIDIA Omniverse Enterprise, and showed how companies like Ericsson, BMW and Lockheed Martin are using the platform to create digital twins to simulate 5G networks, build a robotic factory and prevent wildfires. 

He also revealed a host of new features for Omniverse, and powerful new capabilities including Omniverse Avatar for interactive conversational AI assistants and Omniverse Replicator, a powerful synthetic data generation engine for training autonomous vehicles and robots.

At the inaugural NVIDIA Omniverse Developer Day at GTC, developers were introduced to a new way to build, license, and distribute native applications, extensions, connectors, and microservices for the platform — opening new paths to market for millions of developers. To get started with NVIDIA Omniverse, download the free open beta for individuals, or explore Omniverse Enterprise. Omniverse users have access to a wide range of technical resources, tutorials and more with the NVIDIA Developer Program.

Read about the new EC2 G5 instance that powers remote graphics, visual computing, AI/ML training, and inference workloads on AWS cloud.

Today, AWS announced the general availability of the new Amazon EC2 G5 instances, powered by NVIDIA A10G Tensor Core GPUs. These instances are designed for the most demanding graphics-intensive applications, as well as machine learning inference and training simple to moderately complex machine learning models on the AWS cloud.

The new EC2 G5 instances feature up to eight NVIDIA A10G Tensor Core GPUs that are optimized for advanced visual computing workloads. With support for NVIDIA RTX technology and more RT (ray tracing) cores than any other NVIDIA GPU instance on AWS, it offers up to 3X better graphics performance. Based on NVIDIA Ampere Architecture, G5 instances offer up to 3X higher performance for machine learning inference and 3.3X higher performance for machine learning training, compared to the previous generation Amazon EC2 G4dn instances.

Customers can use the G5 instances to accelerate a broad range of graphics applications like interactive video rendering, video editing, computer-aided design, photorealistic simulations, 3D visualization, and gaming. G5 instances also deliver the best user experience for real-time AI inference performance at scale for use-cases like content and product recommendations, voice assistants, chatbots, and visual search.

Getting the most out of EC2 G5 instances using NVIDIA optimized software

To unlock the breakthrough graphics performance on the new G5 instances, creative and technical professionals can use the NVIDIA RTX Virtual Workstation (vWS) software, available from the AWS Marketplace. Only available from NVIDIA, these NVIDIA RTX vWS advancements include hundreds of certified professional ISV applications, support for all of the leading rendering apps, and optimization with all major gaming content. 

NVIDIA RTX technology delivers exceptional features like ray tracing and AI-denoising.  Creative professionals can achieve photorealistic quality with accurate shadows, reflections, and refractions—creating amazing content faster than ever before. 

NVIDIA RTX vWS also supports Deep Learning Super Sampling (DLSS). This gives designers, engineers, and artists the power of AI for producing the highest visual quality, from anywhere. They can also take advantage of technologies like NVIDIA Iray and NVIDIA OptiX for superior rendering capabilities.

Developers on AWS can use state-of-the-art pretrained AI models, GPU-optimized deep learning frameworks, SDKs, and end-to-end application frameworks from the NGC Catalog on AWS Marketplace soon. In particular, developers can take advantage of NVIDIA TensorRT and NVIDIA Triton Inference Server to optimize inference performance and serve ML models at scale using G5 instances. 

Developers have multiple options to take advantage of NVIDIA-optimized software on AWS. Whether you provision and manage the G5 instances yourself or leverage them in AWS managed services like Amazon Elastic Kubernetes service (EKS) or Amazon Elastic Container Service (ECS).

Learn more about the EC2 G5 instances and get started. >>

AWS and NVIDIA have collaborated to develop an online course that introduces Amazon SageMaker with EC2 Instances powered by NVIDIA GPUs.

AWS and NVIDIA have collaborated to develop an online course that guides you through a simple-to-follow and practical introduction to Amazon SageMaker with EC2 Instances powered by NVIDIA GPUs. This course is grounded in the practical application of services and gives you the opportunity to learn hands-on from experts in machine learning development. Through a simple and straightforward approach, once completed, you will have the confidence and competency to immediately begin working on your ML project.

Machine learning can be complex, tedious, and time-consuming. AWS and NVIDIA provide the fastest, most effective, and easy-to-use ML tools to get you started on your ML project. Amazon SageMaker helps data scientists and developers prepare, build, train, and deploy high-quality ML models quickly by bringing together a broad set of capabilities purpose-built for ML. Amazon EC2 instances powered by NVIDIA GPUs along with NVIDIA software offer high-performance, GPU-optimized instances in the cloud for efficient model training and cost-effective model inference hosting.

In this course, you will first be given a high-level overview of modern machine learning. Then, we will dive right in and get you up and running with a GPU-powered SageMaker instance. You will learn how to prepare a dataset for training a model, how to build a model, how to execute the training of a model, and how to deploy and optimize a model. You will learn hands-on how to apply this workflow for computer vision (CV) and natural language processing (NLP) use cases.

After completing this course, you will be able to build, train, deploy, and optimize ML workflows with GPU acceleration in Amazon SageMaker and understand the key SageMaker services applicable to tabular, computer vision, and language ML tasks. You will feel empowered and have the confidence and competency to solve complex machine learning problems in a more efficient manner.  By using SageMaker, you will simplify workflows so you can build and deploy ML models quickly, freeing you up to focus on other problems to solve. 

Course Overview

This course is designed for machine learning practitioners, including data scientists and developers, who have a working knowledge of machine learning workflows. In this course, you will gain hands-on experience with Amazon SageMaker and Amazon EC2 instances powered by NVIDIA GPUs. There are four modules in the course:

Module 1 – Introduction to Amazon SageMaker and NVIDIA GPUs

In this module, you will learn about the purpose-built tools available within Amazon SageMaker for modern machine learning. This includes a tour of the Amazon SageMaker Studio IDE that can be used to prepare, build, train and tune, and deploy and manage your own ML models. Then you will learn how to use Amazon SageMaker classic notebooks and Amazon SageMaker Studio notebooks to develop natural language processing (NLP), computer vision (CV), and other ML models using RAPIDS. You will also dive deep into NVIDIA GPUs, the NGC Catalog, and instances available on AWS for ML.

Module 2 – GPU Accelerated Machine Learning Workflows with RAPIDS and Amazon SageMaker

In this module, you will apply your knowledge of NVIDIA GPUs and Amazon SageMaker. You will gain a background in GPU accelerated machine learning and perform the steps required to set up Amazon SageMaker. You will then learn about data acquisition and data transformation, move on to model design and training, and finish up by evaluating hyperparameter optimization, AutoML, and GPU accelerated inferencing.

Module 3 – Computer Vision

In this module, you will learn about the application of deep learning for computer vision (CV). As humans, half of our brains are devoted to visual processing, making it critical to how we perceive the world. Endowing machines with sight has been a challenging endeavor, but advancements in compute, algorithms, and data quality have made computer vision more accessible than ever before. From mobile cameras to industrial mechanic lenses, biological labs to hospital imaging, and self-driving cars to security cameras, data in pixel format is one of the most valuable types of data for consumers and companies. In this module, you will explore common CV applications, and you will learn how to build an end-to-end object detection model on Amazon SageMaker using NVIDIA GPUs.

Module 4 – Natural Language Processing

In this module, you will learn about applying deep learning technologies to the problem of language understanding. What does it mean to understand languages? What is language modeling? What is the BERT language model, and why are such language models used in many popular services like search, office productivity software, and voice agents? Are NVIDIA GPUs a fast and cost-efficient platform to train and deploy NLP Models? In this module, you will find answers to all those questions and more. Whether you are an experienced ML engineer considering implementation or a developer wanting to learn to deploy a language understanding model like BERT quickly, this module is for you.

Conclusion

AWS and NVIDIA provide fast, effective, easy-to-use ML tools to get you started on working on your ML project. Learn more about the course to guide you through your ML journey!

submitted by /u/Left_Complaint_6668
[visit reddit] [comments]