yy-fighting 8b82f700e4 feat: add H200 SGLang baseline experiment and fix health check

- Add experiments/dsv4_h200_sglang (config, server start, orchestrator, parser, README)
- Fix start_server.sh to wait for HTTP 200 via curl --fail instead of treating 503 as ready
- Add H200 SGLang baseline to root README and update .gitignore for per-run raw_outputs/logs
- Remove stale experiments/dsv4_h200_dspark/DSPARK_FIX_PR_PREP.md

2026-07-08 06:56:45 +00:00

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DSV4 H200 SGLang Baseline Benchmark

NVIDIA H200 + native sglang + DeepSeek-V4-Flash baseline benchmark experiment.

This experiment uses:

/data/user1/yy/envs/sglang as both the server and benchmark client environment

Quick Start

# Run the full experiment (start server + benchmark + parse)
bash experiments/dsv4_h200_sglang/run_bench.sh

# Reuse an already-running server
SKIP_MANAGE_SERVER=1 bash experiments/dsv4_h200_sglang/run_bench.sh

Results land in experiments/dsv4_h200_sglang/results/<RUN_ID>/.

Configuration

Edit config.env or override via environment variables:

MODEL_PATH=/data/models/DeepSeek-V4-Flash \
PORT=30006 \
SCENARIOS=("32 512 256" "128 512 256") \
bash experiments/dsv4_h200_sglang/run_bench.sh

Files

File	Purpose
`config.env`	Experiment-level configuration (model, port, venv paths, scenarios)
`start_server.sh`	Start a plain SGLang baseline server for DeepSeek-V4-Flash
`run_bench.sh`	Orchestrator: metadata → server → benchmark → parse
`parse_results.py`	Parse raw JSONL outputs into `results.json` + `report.md`

Platform

This experiment targets platforms/nvidia_h200.env.

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