Facebook AI and AWS have partnered to release new libraries that target high-performance PyTorch model deployment and large scale model training. As part of the broader PyTorch community, Facebook AI and AWS engineers have partnered to develop new libraries targeted at large-scale elastic and fault-tolerant model training and high-performance PyTorch model deployment. These libraries enable the community to efficiently productionize AI models at scale and push the state of the art on model exploration as model architectures continue to increase in size and complexity.
Today, we are sharing new details on these features. Available now, TorchServe is an easy-to-use, open source framework for deploying PyTorch models for high-performance inference. Cloud and environment agnostic, the framework’s library includes features such as multimodel serving, logging, metrics for monitoring, and the creation of RESTful endpoints for application integration.
With these features, TorchServe provides a clear path to deploying PyTorch models to production at scale. To get started, visit the AWS News blog for more information.