Local runtime · Server engine
TensorFlow Serving
Production model server that loads versioned SavedModel servables from a watched model directory and answers predict, classify, regress, and metadata calls over a gRPC-first interface with a mirrored REST surface.
- Maintainer
- Accelerators
- CPU · NVIDIA CUDA
- Model formats
- TensorFlow SavedModel
- Source model
- Open source
- Licenses
- Apache License 2.0
- Boundary
- Represents the tensorflow_model_server binary and the serving libraries in the tensorflow/serving repository, not the TensorFlow framework in tensorflow/tensorflow. The framework builds and trains models and supplies the session runtime this server links against; the server's own documentation states that it deals with the inference aspect of machine learning, taking models after training and managing their lifetimes, which is the separate product this record reviews. TensorFlow Lite and LiteRT are a different execution stack for mobile and embedded targets and are out of scope here; the server carries only an experimental --prefer_tflite_model flag that picks up a model.tflite file inside a SavedModel directory, which the flag text marks as removable at any time. Managed Google Cloud offerings that host this server for customers, including Vertex AI prebuilt TensorFlow prediction containers, hold the serving contract themselves and are inference-service boundaries rather than part of this record. The server is headless, operated as a container, an APT-installed binary, or a Kubernetes deployment, and ships no graphical or conversational interface, which keeps it on the runtime side of the ADR 015 runtime-versus-assistant test.
Open in the directory → Open official documentation ↗ Repository ↗
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