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Implementation:Triton inference server Server L0 Batcher Test

From Leeroopedia
Knowledge Sources
Domains Testing, Dynamic_Batching
Last Updated 2026-02-13 17:00 GMT

Overview

QA test script for validating dynamic batcher functionality in the Triton Inference Server.

Description

This test orchestrates comprehensive validation of the dynamic batcher subsystem. It exercises batching behavior across multiple dimensions including preferred batch sizes, max queue delay, priority-based scheduling, request queuing semantics, sequence batching interactions, and batch formation under varying load patterns. The script generates model configurations with different batching parameters, sends concurrent inference requests with controlled timing, and verifies that requests are batched according to the configured policies. It checks correct batch sizes in server logs, validates request latencies against queue delay settings, and ensures priority levels are respected during batch formation.

Usage

Run as part of the Triton QA test suite to validate dynamic batching. Typically executed in CI/CD pipelines within the Triton Docker container environment.

Code Reference

Source Location

Signature

#!/bin/bash
# Key functions and variables
source ../common/util.sh
SERVER=/opt/tritonserver/bin/tritonserver
SERVER_ARGS="--model-repository=${MODELDIR}"
SERVER_LOG="./batcher_test.server.log"
CLIENT_LOG="./batcher_test.client.log"
BATCHER_TEST="./batcher_test.py"

Import

# Sources common utilities
source ../common/util.sh

I/O Contract

Inputs

Name Type Required Description
DATADIR env var No Directory containing test data
TRITON_DIR env var No Path to Triton server installation
MODEL_REPO directory Yes Generated test model repository with batcher-configured models

Outputs

Name Type Description
exit code int 0 on success, 1 on failure
test logs files Server logs and test output in test directory

Usage Examples

Running the Test

cd qa/L0_batcher/
bash test.sh

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