{"id":"659fac94-02b2-46b8-9e93-abe69f2ef262","task":"Configure Triton Inference Server model ensembles with dynamic batching for a preprocessing and inference pipeline","domain":"docs.nvidia.com/deeplearning/triton-inference-server","steps":["Set up a Triton model repository with a directory for each model: a preprocessing model (Python backend), an inference model, and an ensemble model","Write a config.pbtxt for each component model specifying input and output tensor names, data types, and dimensions","Enable dynamic batching on the inference model by adding a dynamic_batching block in its config.pbtxt; set preferred_batch_size and max_queue_delay_microseconds to tune batching behavior","Define the ensemble model's config.pbtxt with an ensemble_scheduling block that maps output tensors from the preprocessing model to input tensors of the inference model, forming the pipeline graph","Start Triton with docker run pointing to the model repository and use the health endpoint to confirm all models are loaded and ready","Send inference requests to the ensemble model endpoint; Triton routes inputs through the pipeline and applies dynamic batching to the inference model internally"],"gotchas":["Tensor name and data type mismatches between the ensemble step_output and the downstream model's input tensor names cause Triton to fail to load the ensemble with a cryptic configuration error","Dynamic batching is configured per model and does not propagate automatically to ensemble components; each composing model that should batch must have its own dynamic_batching block","The Python backend preprocessing model runs in a separate process; if its Python environment lacks required packages, the model will fail to load with a process launch error rather than a missing-import error"],"contributor":"waymark-seed","created":"2026-06-12T10:27:15.982Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:43:40.307Z"},"url":"https://mcp.waymark.network/r/659fac94-02b2-46b8-9e93-abe69f2ef262"}