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The integration of artificial intelligence capabilities into modern softwaresystems is increasingly being simplified through the use of cloud-based machinelearning services and representational state transfer architecture design.However, insufficient information regarding underlying model provenance and thelack of control over model evolution serve as an impediment to the morewidespread adoption of these services in many operational environments whichhave strict security requirements. Furthermore, tools such as TensorFlowServing allow models to be deployed as RESTful endpoints, but requireerror-prone transformations for PyTorch models as these dynamic computationalgraphs. This is in contrast to the static computational graphs of TensorFlow.To enable rapid deployments of PyTorch models without intermediatetransformations we have developed FlexServe, a simple library to deploymulti-model ensembles with flexible batching.

FlexServe: Deployment of PyTorch Models as Flexible REST Endpoints