Principle:Triton inference server Server Ensemble Inference

Field	Value
Principle Name	Ensemble_Inference
Knowledge Sources	Triton Server|https://github.com/triton-inference-server/server, source::Doc|Ensemble Models|https://docs.nvidia.com/deeplearning/triton-inference-server/user-guide/docs/user_guide/ensemble_models.html
Domains	Model_Serving, Inference, Pipeline_Architecture
Status	Active
Last Updated	2026-02-13 17:00 GMT

Overview

Process of sending inference requests to an ensemble model that transparently executes a multi-model pipeline. From the client perspective, ensemble inference is identical to single-model inference — the client addresses the ensemble by name and is unaware of the internal pipeline structure.

Description

Ensemble inference is identical to single-model inference from the client perspective. The client sends inputs to the ensemble model name and receives outputs. Triton internally routes tensors through the ensemble DAG, executing composing models in dependency order. The client is unaware of the internal pipeline structure.

Key characteristics:

• Transparent orchestration — The client sees a single model endpoint; Triton handles all internal routing
• Protocol support — Both HTTP (REST) and gRPC protocols are supported
• Synchronous and streaming — Standard synchronous inference and streaming modes are both available
• Partial output requests — Clients can request a subset of the ensemble's declared outputs
• Stateful support — Sequence IDs can be passed through for stateful ensemble pipelines

The inference flow is:

1. Client sends request to ensemble model name with input tensors
2. Triton receives the request and identifies it as an ensemble model
3. The ensemble scheduler routes input tensors to the first step(s) via input_map
4. Each composing model executes when all its input dependencies are satisfied
5. Output tensors flow through output_map to the next step(s) or to ensemble outputs
6. Final output tensors are returned to the client

Usage

Ensemble inference is used whenever a deployed ensemble model needs to be invoked. It applies when:

• Sending inference requests to a multi-model pipeline through a single endpoint
• Using standard tritonclient libraries (HTTP or gRPC) to interact with ensemble models
• Building client applications that consume ensemble model outputs
• Testing ensemble pipelines end-to-end

Theoretical Basis

The ensemble inference principle is based on transparent orchestration:

• Client abstraction — Client sees single model → server executes DAG → client receives final outputs
• Topological execution — Composing model execution follows topological order derived from tensor dependencies
• Parallel branches — Independent branches in the DAG can execute concurrently
• Backpressure — max_inflight_requests prevents resource exhaustion under load

The HTTP endpoint follows the KServe V2 inference protocol:

POST /v2/models/<ensemble_name>/versions/<ver>/infer

The gRPC endpoint uses the ModelInfer RPC on port 8001 (default).

Source: src/http_server.cc:L3667-3795 (HandleInfer), qa/L0_simple_ensemble/ensemble_test.py:L99-176

Related Pages

• Implementation:Triton_inference_server_Server_Ensemble_Infer_Request