Principle:Tensorflow Serving TFRT Inference
| Knowledge Sources | |
|---|---|
| Domains | Model Serving, TFRT, Inference |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
TFRT Inference defines the inference execution pipeline for classification, regression, prediction, and multi-inference operations using the TFRT (TensorFlow RunTime) SavedModel backend.
Description
The TFRT Inference principle governs how inference requests are processed when models are loaded through the TFRT runtime. Each inference type follows a consistent three-phase pattern: pre-processing (input validation and tensor name resolution), execution (TFRT SavedModel invocation), and post-processing (output validation, formatting, and metrics recording).
The TFRT inference modules serve as parallel implementations to the standard TensorFlow session-based inference, but operate on tfrt::SavedModel instances rather than tensorflow::Session objects. This enables TFRT-specific optimizations including lazy function initialization, MLRT (Machine Learning RunTime) integration, and direct function metadata introspection for validation.
Key characteristics of TFRT inference:
- Function-based execution: Instead of tensor name resolution through a session graph, TFRT uses named functions with typed metadata.
- Shared validation patterns: All inference types validate function metadata (input/output counts and names) against the expected signature constants.
- Unified serialization: Input examples are serialized to tensors using the shared InputToSerializedExampleTensor utility.
- Runtime metrics: All inference paths record TFRT runtime latency via RecordRuntimeLatency with the "tfrt" runtime label.
- Output filtering: Predict operations support output tensor filtering for bandwidth optimization.
- Multi-signature execution: Multi-inference leverages RunMultipleSignatures for efficient batch evaluation of multiple functions.
- Error logging: TFRT-specific error logging to external services when enabled via environment variable.
Usage
Apply this principle when implementing or extending TFRT-based inference operations. Follow the three-phase pattern (pre-process, execute, post-process) and ensure proper metrics recording and error handling. All new TFRT inference types should validate function metadata, use shared input serialization utilities, and record latency metrics.
Theoretical Basis
The TFRT inference architecture is based on the separation of model representation (SavedModel) from execution runtime. TFRT provides an alternative execution path to the TensorFlow Session API, optimized for serving workloads with:
- Eager-style execution: TFRT's host runtime executes operations eagerly, reducing the overhead of session-based graph execution.
- Concurrent kernel execution: TFRT can execute independent kernels concurrently within a single function invocation.
- Lazy initialization: Functions can be compiled on first use, spreading JIT compilation costs and reducing initial load time for models with many signatures.
- Function metadata: Rich metadata about function inputs and outputs enables pre-execution validation without requiring a test run, improving error detection.
The multi-inference optimization of running all tasks in a single RunMultipleSignatures call reduces overhead by sharing common subgraph computations across signature evaluations.