benchscope

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A single-process, pip-installable web UI for benchmarking vLLM / SGLang and any OpenAI-compatible inference service.

Introduction

benchscope is a performance-testing tool for LLM inference services. It connects to a vLLM / SGLang (or any OpenAI-compatible) API and runs throughput/latency benchmarks through a clean admin-console style web UI.

• How it runs — the bench tool (vllm bench serve / sglang.bench_serving) executes as a subprocess on the machine where benchscope is installed. The inference server only needs to expose an OpenAI-compatible API — no server-side plugin required.
• Live feedback — every concurrency result streams into the table and charts in real time.
• Single process — pip install + one command starts the web server and the built-in front end.

Quick Start

# Install from PyPI
pip install benchscope

# Start (default http://127.0.0.1:8080, opens browser automatically)
benchscope

# Options
benchscope --port 8080 --no-browser

Open the page and:

1. Confirm the top nav shows Service (app) and Environment (inference) as online.
2. Go to Settings (top-right) and set the inference service Base URL (any OpenAI-compatible endpoint).
3. Open the vLLM / SGLang page, choose a model (from /v1/models), a dataset (Random / ShareGPT / Custom) and a concurrency list.
4. Use Test Progress → Start Test. The Test Results panel updates live with a bilingual table and six curves.
5. Logs are written under logs/<MMDD-HHMMSS>/ with a benchmark-*.xlsx summary (mean + P99 sheets).

Features

• Dual framework — vLLM (vllm bench serve) and SGLang (sglang.bench_serving).
• Datasets
  • random — multiple input/output length pairs (default 3K/1K, 1K/1K, 256/256, customisable).
  • sharegpt — auto-downloaded from ModelScope.
  • custom — upload a jsonl file or point to a local path (same behaviour as ShareGPT).
• Configurable benchmark — editable concurrency list (default 1,4,8,16,32,40,64,128), --max-concurrency = --num-prompts, inf request rate, framework parameter forms plus a free-form flag editor, and command preview.
• GPU auto-detect (nvidia-smi) with a manual fallback, plus an editable TPOT threshold used to highlight the best/nearest rows.
• Live results — bilingual table + six curves (Output & Total throughput, TTFT/TPOT mean & P99) against concurrency.
• Logs — per-run MMDD-HHMMSS directory with raw bench logs, mean/P99 summary CSV and benchmark-*.xlsx (mean + P99 sheets); preview & download in the UI.
• Analysis — mean / P99 blocks with output/peakoutput/total/ttft/itl/tpot curves and best-concurrency highlight (closest to, and below, a TPOT threshold).
• Admin-console UI — fixed top nav, fixed left nav (test workflow / test records), and a fixed sub-nav; the content area scrolls internally.
• Status monitoring — Service & Environment online/offline indicators with live updates.

Roadmap

Project Structure

benchscope/
├── benchscope/
│   ├── cli.py            # `benchscope` command entry
│   ├── config.py         # config persistence (~/.benchscope/config.json)
│   ├── datasets.py       # ShareGPT download/convert, custom datasets
│   ├── gpu.py            # GPU auto-detect
│   ├── parser.py         # bench output parsing (mean + P99)
│   ├── summary.py        # CSV & xlsx summary generation
│   ├── benches/          # vllm/sglang command building & execution
│   └── server/           # FastAPI + WebSocket + test orchestration
├── web/                  # Vue 3 + Ant Design Vue front-end source
└── tests/                # mock OpenAI server & UI smoke tests

Development

# Backend
python -m benchscope.cli --port 8080 --no-browser

# Frontend (hot-reload, proxies /api and /ws to :8080)
cd web && npm install && npm run dev    # http://127.0.0.1:5173

• Run the UI without a vLLM/SGLang install: BENCHSCOPE_FAKE_BENCH=1 python -m benchscope.
• Mock an inference service locally: python tests/mock_openai_server.py (port 8001), then point Base URL to http://127.0.0.1:8001 in Settings.

Open Source

• License — Apache License 2.0
• Published on — PyPI: benchscope
• Source — https://github.com/LABELNET/benchscope
• Contributing — feel free to open issues / pull requests on the source repository.