Principle:Triton inference server Server Ensemble Configuration

Field	Value
Principle Name	Ensemble_Configuration
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, Configuration, Pipeline_Architecture
Status	Active
Last Updated	2026-02-13 17:00 GMT

Overview

Process of creating the complete config.pbtxt for an ensemble model with the platform: "ensemble" designation, tensor specifications, and step definitions. The ensemble model itself has no model files and exists purely as an orchestration configuration.

Description

An ensemble configuration combines the standard model config fields (name, max_batch_size, input, output) with platform: "ensemble" and the ensemble_scheduling block. The ensemble model itself has no model files — it requires an empty version directory (e.g., ensemble_name/1/) and purely coordinates inference across composing models.

The configuration must ensure:

• Tensor type compatibility — Ensemble input/output data types must match what the composing models expect and produce
• Shape compatibility — Tensor dimensions must be consistent across connected steps in the DAG
• Batch size compatibility — max_batch_size must be compatible across the ensemble and all composing models
• Name consistency — Ensemble tensor names used in input_map and output_map must match the ensemble-level input and output declarations

The configuration structure has three sections:

1. Model metadata — name, platform: "ensemble", max_batch_size
2. Tensor declarations — input and output blocks defining the ensemble's external interface
3. Scheduling definition — ensemble_scheduling block with step definitions and tensor mappings

Usage

Ensemble configuration is required for every ensemble model deployment. It applies when:

• Assembling the final configuration after component models are prepared
• Defining the external interface (inputs/outputs) that clients will interact with
• Specifying the internal tensor routing between composing models
• Setting up the model repository structure for ensemble models

Theoretical Basis

The ensemble configuration principle is based on configuration validation:

• Type matching — Ensemble inputs/outputs must type-match with step input_map/output_map declarations
• Shape matching — Composing model inputs/outputs must shape-match through the tensor routing
• Batch compatibility — max_batch_size must be compatible across all steps; the ensemble's max_batch_size cannot exceed any composing model's limit
• Completeness — Every ensemble input must be consumed by at least one step, and every ensemble output must be produced by at least one step

The platform designation "ensemble" is a reserved literal that tells Triton to treat this model as an orchestrator rather than an inference model. No backend is loaded for ensemble models.

Source: docs/user_guide/ensemble_models.md:L60-123, qa/common/gen_ensemble_model_utils.py:L768-847

Related Pages

• Implementation:Triton_inference_server_Server_Ensemble_Config_Pbtxt