
The ability to package a model, learning weights, and supporting files (e.g., class mappings, vocabularies) into a single, persistent artifact (a.k.a. the “model archive”)
Robust management capability, allowing full configuration of models, versions, and individual worker threads via command line, config file, or run-time API
Automatic batching of individual inferences across HTTP requests
Logging including common metrics, and the ability to incorporate custom metrics
Ready-made Dockerfile for easy deployment
HTTPS support for secure deployment

To learn more about the APIs and the design of this feature, see the links below:
* See  for a full multi-node deployment reference architecture.
* The full documentation can be found here.
TorchElastic integration with Kubernetes (Experimental)