Principle:Tensorflow Serving Model Metadata Query
| Knowledge Sources | |
|---|---|
| Domains | Model Serving, Model Metadata |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Model Metadata Query defines how clients discover model interface information (signature definitions) from loaded models without performing inference.
Description
The Model Metadata Query principle provides a non-inference API for model introspection. Clients can query loaded models to discover their signature definitions, which describe the model's inputs, outputs, and supported operations (predict, classify, regress).
The implementation supports an extensible metadata field system, though currently only "signature_def" is supported. The response packages SignatureDefMap protobuf data using protobuf Any fields, allowing future metadata types to be added without protocol changes.
Design principles:
- Read-only operation: Metadata queries do not modify model state or trigger computation.
- Versioned responses: The response includes the model's name and version, enabling clients to confirm they are querying the expected model.
- Validation first: Requests are validated for required fields and supported metadata types before accessing the servable.
Usage
Apply this principle when clients need to discover model capabilities at runtime, such as determining available signatures before constructing inference requests, or for service discovery and documentation generation.
Theoretical Basis
Model metadata query implements the concept of interface introspection, analogous to reflection in programming languages. It enables:
- Late binding: Clients can adapt their requests based on discovered model capabilities.
- Contract verification: Clients can verify that a model supports expected signatures before sending inference requests.
- Documentation generation: Automated tools can query models to generate API documentation.