AWS announces G4 instances to accelerate machine learning
G4 instances provide the industry’s most cost-effective machine learning inference for applications, like adding metadata to an image, object detection, recommender systems, automated speech recognition, and language translation. G4 instances also provide a very cost-effective platform for building and running graphics-intensive applications, such as remote graphics workstations, video transcoding, photo-realistic design, and game streaming in the cloud.
Machine learning involves two processes that require compute – training and inference. Training entails using labelled data to create a model that is capable of making predictions, a compute-intensive task that requires powerful processors and high-speed networking. Inference is the process of using a trained machine learning model to make predictions, which typically requires processing a lot of small compute jobs simultaneously, a task that can be most cost-effectively handled by accelerating computing with energy-efficient NVIDIA GPUs.
With the launch of P3 instances in 2017, AWS was the first to introduce instances optimized for machine learning training in the cloud with powerful NVIDIA V100 Tensor Core GPUs, allowing customers to reduce machine learning training from days to hours. However, inference is what actually accounts for the vast majority of machine learning’s cost. According to customers, machine learning inference can represent up to 90% of overall operational costs for running machine learning workloads.
New G4 instances feature the latest generation NVIDIA T4 GPUs, custom 2nd Generation Intel Xeon Scalable (Cascade Lake) processors, up to 100 Gbps of networking throughput, and up to 1.8 TB of local NVMe storage, to deliver the most cost-effective GPU instances for machine learning inference. And with up to 65 TFLOPs of mixed-precision performance, G4 instances not only deliver superior price/performance for inference, but also can be used cost-effectively for small-scale and entry-level machine learning training jobs that are less sensitive to time-to-train.
G4 instances also provide an ideal compute engine for graphics-intensive workloads, offering up to a 1.8x increase in graphics performance and up to 2x video transcoding capability over the previous generation G3 instances. These performance enhancements enable customers to use remote workstations in the cloud for running graphics-intensive applications like Autodesk Maya or 3D Studio Max, as well as efficiently create photo-realistic and high-resolution 3D content for movies and games.
Customers with machine learning workloads can launch G4 instances using Amazon SageMaker or AWS Deep Learning AMIs, which include machine learning frameworks such as TensorFlow, TensorRT, MXNet, PyTorch, Caffe2, CNTK, and Chainer. G4 instances will also support Amazon Elastic Inference in the coming weeks, which will allow developers to dramatically reduce the cost of inference by up to 75% by provisioning just the right amount of GPU performance.
Customers with graphics and streaming applications can launch G4 instances using Windows, Linux, or AWS Marketplace AMIs from NVIDIA with NVIDIA Quadro Virtual Workstation software pre-installed. A bare metal version will be available in the coming months.
Amazon Web Services – aws.amazon.com
If you enjoyed this article, you will like the following ones: don't miss them by subscribing to :
