dynamo/benchmarks
Yan Ru Pei cc4c3516bb
feat: mocker disagg (#3833)
Signed-off-by: PeaBrane <yanrpei@gmail.com>
2025-10-28 20:36:02 +00:00
..
burstgpt_loadgen fix: minor robustness improvement for datagen analyze (#3483) 2025-10-08 09:18:56 -07:00
incluster feat: remove deploy/utils rbac (#3771) 2025-10-21 16:27:29 -07:00
llm fix: update invalid AIPerf scripts and parsing logic (#3675) 2025-10-16 13:30:11 -07:00
prefix_data_generator fix: minor robustness improvement for datagen analyze (#3483) 2025-10-08 09:18:56 -07:00
profiler feat: turn profiling k8s jobs into sample DGDR requests (#3864) 2025-10-27 13:36:31 -07:00
router feat: mocker disagg (#3833) 2025-10-28 20:36:02 +00:00
sin_load_generator fix: update invalid AIPerf scripts and parsing logic (#3675) 2025-10-16 13:30:11 -07:00
utils fix: remove invalid aiperf args (#3710) 2025-10-17 14:36:34 -07:00
README.md feat: update benchmarking script to use aiperf (#3306) 2025-10-14 05:26:29 +00:00
pyproject.toml fix: git lfs support is erratic with uv so set env var to force it (#3671) 2025-10-16 18:49:42 -04:00

README.md

Benchmarks

This directory contains benchmarking scripts and tools for performance evaluation of Dynamo deployments. The benchmarking framework is a wrapper around aiperf that makes it easy to benchmark DynamoGraphDeployments or other deployments with exposed endpoints.

Quick Start

Benchmark a Dynamo Deployment

First, deploy your DynamoGraphDeployment using the deployment documentation, then:

# Port-forward your deployment to http://localhost:8000
kubectl port-forward -n <namespace> svc/<frontend-service-name> 8000:8000 > /dev/null 2>&1 &

# Run benchmark
python3 -m benchmarks.utils.benchmark \
    --benchmark-name my-benchmark \
    --endpoint-url http://localhost:8000 \
    --model "<your-model>"

# Generate plots
python3 -m benchmarks.utils.plot --data-dir ./benchmarks/results

# Or plot only specific benchmark experiments
python3 -m benchmarks.utils.plot --data-dir ./benchmarks/results --benchmark-name my-benchmark

Features

Benchmark any HTTP endpoints! The benchmarking framework supports:

Flexible Configuration:

  • User-defined benchmark names using --benchmark-name flag
  • Support for single endpoint benchmarking with --endpoint-url flag
  • Customizable concurrency levels (configurable via CONCURRENCIES env var), sequence lengths, and models
  • Automated performance plot generation with custom benchmark names

Supported Backends:

  • DynamoGraphDeployments with port-forwarded endpoints
  • External HTTP endpoints (for comparison with non-Dynamo backends or platforms)

Installation

This is already included as part of the Dynamo container images. To install locally or standalone:

pip install -e .

Data Generation Tools

This directory also includes lightweight tools for:

  • Analyzing prefix-structured data (datagen analyze)
  • Synthesizing structured data customizable for testing purposes (datagen synthesize)

Detailed information is provided in the prefix_data_generator directory.

Comprehensive Guide

For detailed documentation, configuration options, and advanced usage, see the complete benchmarking guide.