Instance types available may vary by region. In the AWS Management Console, launch an instance from the AWS Marketplace, using the NVIDIA GPU-Optimized AMI. This AMI is validated and updated quarterly by NVIDIA with the newest drivers, security patches, and support for the latest GPUs to maximize performance. It also provides a standardized stack for you to build speech AI applications. It is preconfigured with NVIDIA GPU drivers, CUDA, Docker toolkit, runtime, and other dependencies. In this post, you use the NVIDIA GPU-optimized AMI available on the AWS Marketplace. Step 1: Launch an EC2 instance with the NVIDIA GPU-optimized AMI
To follow along, make sure that you have an AWS account with access to NVIDIA GPU-powered instances (for example, Amazon EC2 G and P instance types such as P4d instances for NVIDIA A100 GPUs and G4dn instances for NVIDIA T4 GPUs). Launch an intelligent virtual assistant application.Run the Riva ASR and TTS Hello World examples with Jupyter notebooks.Pull the Riva container from the NGC catalog.Launch an Amazon EC2 instance with NVIDIA GPU-optimized AMI.There are four simple steps to get started with Riva on an NVIDIA GPU-powered Amazon EC2 instance:
#CLARA REMOTE ASSISTANT JOB REVIEW SOFTWARE#
With a broad portfolio of NVIDIA GPU-powered Amazon EC2 instances combined with GPU-optimized software like Riva, you can accelerate every step of the speech AI pipeline. If AWS is where you develop and deploy workloads, you already have access to all the requirements needed for building speech AI applications. Running Riva ASR and TTS examples to launch a virtual assistant Here are the steps to follow for getting started with Riva on AWS. Riva can also be used to develop and deploy speech AI applications on NVIDIA GPUs anywhere: on premises, embedded devices, any public cloud, or the edge. When you deploy Riva on your platform, these models are ready for immediate use.
The state-of-the-art Riva speech models have been trained for millions of hours on thousands of hours of audio data. It helps you quickly build intelligent speech applications, such as AI virtual assistants.īy using powerful optimizations with NVIDIA TensorRT and NVIDIA Triton, Riva can build and deploy customizable, pretrained, out-of-the-box models that can deliver interactive client responses in less than 300ms, with 7x higher throughput on NVIDIA GPUs compared to CPUs. Riva is a GPU-accelerated SDK for building real-time speech AI applications. After following along, this virtual assistant demo could be running on your web browser powered by NVIDIA GPUs on Amazon EC2.Īlong with the step-by-step guide, we also provide you with resources to help expand your knowledge so you can go on to build and deploy powerful speech AI applications with NVIDIA support.īut first, here is how the Riva SDK works.
#CLARA REMOTE ASSISTANT JOB REVIEW HOW TO#
With no prior knowledge or experience, you learn how to quickly configure a GPU-optimized development environment and run NVIDIA Riva ASR and TTS examples using Jupyter notebooks. In this post, we walk through how you can simplify the speech AI development process by using NVIDIA Riva to run GPU-optimized applications. From setting up GPU-optimized development environments to deploying speech AI inferences using customized large transformer-based language models in under 300ms, speech AI pipelines require dedicated time, expertise, and investment. Sign up for the latest Speech AI News from NVIDIA.īuilding these real-time speech AI applications from scratch is no easy task.
0 kommentar(er)
