With the changing world, it is important for companies to transform accordingly. One of the most widely used technologies used these days is cloud computing. The adoption of cloud computing has been increasing rapidly. Either company has already adopted it or is moving towards adopting it. The advantages that cloud computing provides are immaculate.

Register free for NVIDIA GTC to learn from experts on how AI and the evolution of the 3D internet are profoundly impacting industries--and society as a whole. We have prepared several AWS sessions to give you guidance on how to use AWS services powered by NVIDIA technology to meet your goals. Amazon Elastic Compute Cloud (Amazon EC2) instances powered by NVIDIA GPUs deliver the scalable performance needed for fast machine learning (ML) training, cost-effective ML inference, flexible remote virtual workstations, and powerful HPC computations. AWS is a Global Diamond Sponsor of the conference. Scaling Deep Learning Training on Amazon EC2 using PyTorch (Presented by Amazon Web Services) [A41454] As deep learning models grow in size and complexity, they need to be trained using distributed architectures.

Speech AI can assist human agents in contact centers, power virtual assistants and digital avatars, generate live captioning in video conferencing, and much more. Under the hood, these voice-based technologies orchestrate a network of automatic speech recognition (ASR) and text-to-speech (TTS) pipelines to deliver intelligent, real-time responses. Building these real-time speech AI applications from scratch is no easy task. 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. In this post, we walk through how you can simplify the speech AI development process by using NVIDIA Riva to run GPU-optimized applications.

After the article in Nature about the open-source of AlphaFold v2.0 on GitHub by DeepMind, many in the scientific and research community have wanted to try out DeepMind's AlphaFold implementation firsthand. With compute resources through Amazon Elastic Compute Cloud (Amazon EC2) with Nvidia GPU, you can quickly get AlphaFold running and try it out yourself. In this post, I provide you with step-by-step instructions on how to install AlphaFold on an EC2 instance with Nvidia GPU. The process starts with a Deep Learning Amazon Machine Image (DLAMI). After installation, we run predictions using the AlphaFold model with CASP14 samples on the instance.

Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.

In October 2019, AWS announced that it was working with Facebook, Microsoft, and the Partnership on AI on the first Deepfake Detection Challenge. Deepfake algorithms are the same as the underlying technology that has given us realistic animation effects in movies and video games. Unfortunately, those same algorithms have been used by bad actors to blur the distinction between reality and fiction. Deepfake videos result from using artificial intelligence to manipulate audio and video to make it appear as though someone did or said something they didn't. For more information about deepfake content, see The Partnership on AI Steering Committee on AI and Media Integrity.

Due to the massive size of the neural network models and training datasets used in machine learning today, it is imperative to distribute stochastic gradient descent (SGD) by splitting up tasks such as gradient evaluation across multiple worker nodes. However, running distributed SGD can be prohibitively expensive because it may require specialized computing resources such as GPUs for extended periods of time. We propose cost-effective strategies to exploit volatile cloud instances that are cheaper than standard instances, but may be interrupted by higher priority workloads. To the best of our knowledge, this work is the first to quantify how variations in the number of active worker nodes (as a result of preemption) affects SGD convergence and the time to train the model. By understanding these trade-offs between preemption probability of the instances, accuracy, and training time, we are able to derive practical strategies for configuring distributed SGD jobs on volatile instances such as Amazon EC2 spot instances and other preemptible cloud instances. Experimental results show that our strategies achieve good training performance at substantially lower cost.

In the current age of cloud computing, there is now a multitude of mature services available -- offering security, scalability, and reliability for many business computing needs. What was once a colossal undertaking to build a data center, install server racks, and design storage arrays has given way to an entire marketplace of services that are always just a click away. One leader in that marketplace is Amazon Web Services, which consists of 175 products and services in a vast catalog that provides cloud storage, compute power, app deployment, user account management, data warehousing, tools for managing and controlling Internet of Things devices, and just about anything you can think of that a business needs. AWS really grew in popularity and capability over the last decade. One reason is that AWS is so reliable and secure.

Amazon wants to make it easier to get AI-powered apps up and running on Amazon Web Services. Toward that end, it today launched AWS Deep Learning Containers, a library of Docker images preinstalled with popular deep learning frameworks. "We've done all the hard work of building, compiling, and generating, configuring, optimizing all of these frameworks, so you don't have to," Dr. Matt Wood, general manager of deep learning and AI at AWS, said onstage at the AWS Summit in Santa Clara this morning. "And that means that you do less of the undifferentiated heavy lifting of installing these very, very complicated frameworks and then maintaining them." The new AWS container images in question -- which are preconfigured and validated by Amazon -- support Google's TensorFlow machine learning framework and Apache MXNet, with Facebook's PyTorch and other deep learning frameworks to come.

The first instances to include NVIDIA Tesla V100 GPUs, P3 instances are the most powerful GPU instances available in the cloud. P3 instances allow customers to build and deploy advanced applications with up to 14 times better performance than previous-generation Amazon EC2 GPU compute instances, and reduce training of machine learning applications from days to hours. With up to eight NVIDIA Tesla V100 GPUs, P3 instances provide up to one petaflop of mixed-precision, 125 teraflops of single-precision, and 62 teraflops of double-precision floating point performance, as well as a 300 GB/s second-generation NVIDIA NVLink interconnect that enables high-speed, low-latency GPU-to-GPU communication. P3 instances also feature up to 64 vCPUs based on custom Intel Xeon E5 (Broadwell) processors, 488 GB of DRAM, and 25 Gbps of dedicated aggregate network bandwidth using the Elastic Network Adapter (ENA). "When we launched our P2 instances last year, we couldn't believe how quickly people adopted them," said Matt Garman, Vice President of Amazon EC2.