Principle:Tensorflow Serving Multi Inference
| Knowledge Sources | |
|---|---|
| Domains | Model Serving, Multi Inference |
| Last Updated | 2026-02-13 00:00 GMT |
Overview
Multi Inference defines how multiple classification and regression tasks are executed efficiently in a single request against a shared TensorFlow session, minimizing redundant computation.
Description
The Multi Inference principle addresses the common serving scenario where a client needs multiple inference results (classifications and/or regressions) from the same model and input data. Rather than making separate RPC calls for each task, the multi-inference API combines them into a single request.
The key optimization is shared Session::Run execution: all tasks' input and output tensor names are collected, deduplicated, and passed to a single Session::Run call. This ensures that shared subgraphs (e.g., feature extraction layers) are computed only once, regardless of how many tasks reference them.
Design principles:
- Single model constraint: All tasks must reference the same model, ensuring they operate on the same graph.
- Unique signatures: Each task must reference a distinct signature to prevent duplicate evaluation.
- Method-based dispatching: Tasks are routed to classification or regression pre/post-processing based on their method_name field.
- Shared input serialization: The input is serialized once and fed to all required input tensor names.
Usage
Apply this principle when clients need multiple inference outputs from a single model. It is particularly efficient when tasks share common subgraphs, as the single Session::Run call avoids redundant computation.
Theoretical Basis
Multi-inference is an application of computation sharing in dataflow graphs. When multiple output operations share common ancestors in the computation graph, executing them in a single graph traversal (Session::Run) computes shared nodes once. This is equivalent to common subexpression elimination at the inference request level, providing:
- Reduced computation: Shared layers (embedding, feature extraction) are computed once.
- Reduced RPC overhead: One request instead of N separate requests.
- Atomic execution: All tasks see the same model version and input data.