- rewrite README with project purpose, standard workflow, corrected index - add docs/NEW_PLATFORM_GUIDE.md (new GPU onboarding SOP, GLM5.2 reuse) - fix ../../scripts/common -> ../../../scripts/common in 42 experiment scripts - refresh stale docs (EXPERIMENT_GUIDE, H200_QUICKSTART, ADAPTIVE_CONCURRENCY_USAGE, BENCHMARK_WORKFLOW) - remove dead code (dp_proxy.py) and .bak leftovers - add p800 adaptive results (tp4_dp2/tp8_dp1 metrics + summary) - gitignore envs/charts and .tmp_charts
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H200 Quick Start Guide
This guide covers how to use this benchmark repository on an NVIDIA H200 machine.
Current state
The older H200 experiments (e.g. experiments/h200/dsv4_h200_dspark/) are based on native host virtual environments (not Docker):
- Server engine:
vllm-dspark - Server env:
envs/vllm-dspark - Benchmark client env:
envs/sglang(usessglang.bench_serving --backend vllm) - Default model:
/data/models/DeepSeek-V4-Flash-DSpark - Default port:
30004
Note: the current mainstream H200 experiments — the TP/DP matrix adaptive-concurrency suites experiments/h200/dsv4_h200_vllm_tp_dp_matrix/ and experiments/h200/dsv4_h200_sglang_tp_dp_matrix/ — are Docker-based (USE_DOCKER=1 for the server, benchmark client via DOCKER_CLIENT_IMAGE); see experiments/ADAPTIVE_CONCURRENCY_USAGE.md.
The legacy benchmark suite scripts/benchmark_dspark_0707/ has been removed from this repo. The migration wrapper experiment experiments/h200/dsv4_h200_dspark/ still exists and produces the experiments/h200/<name>/results/<RUN_ID>/ layout, but its default LEGACY_GRID_SCRIPT / SERVER_START_SCRIPT point at the removed scripts and must be overridden to run.
1. Pull and verify
git clone <repo-url> sskj # clone path: adjust to your machine
cd sskj
# Platform should auto-detect as nvidia_h200
source scripts/common/platform.sh
If auto-detection fails, set it explicitly:
PLATFORM=nvidia_h200 source scripts/common/platform.sh
2. Legacy DSpark benchmark grid (removed)
The legacy suite scripts/benchmark_dspark_0707/ has been removed from this repo, along with its bench_results/dspark_grid_<RUN_ID>/ output location (historical results were archived elsewhere). Use the wrapper experiment below, or the current tp_dp matrix experiments (experiments/h200/dsv4_h200_{vllm,sglang}_tp_dp_matrix/), instead.
3. Run the migration wrapper experiment
This produces results in the experiment-centric layout:
bash experiments/h200/dsv4_h200_dspark/run_bench.sh
Results land in experiments/h200/dsv4_h200_dspark/results/<RUN_ID>/.
Caveat: the wrapper's default LEGACY_GRID_SCRIPT and SERVER_START_SCRIPT refer to the removed scripts/benchmark_dspark_0707/ suite and scripts/start_dsv4_dspark_8card.sh; set both explicitly (or restore equivalent scripts) before running.
4. Create a new H200 experiment
To add a new H200 benchmark (for example a different model or engine), create:
experiments/h200/<your_name>/
├── README.md # What this experiment measures
├── config.env # Model, port, scenarios, venv paths
├── start_server.sh # (optional) native server launch
├── run_bench.sh # Orchestrator: server → benchmark → stop
└── parse_results.py # Generate results.json + report.md
Minimum config.env:
EXPERIMENT="${EXPERIMENT:-<your_name>}"
MODEL_NAME="${MODEL_NAME:-DeepSeek-V4-Flash-DSpark}"
MODEL_PATH="${MODEL_PATH:-/data/models/DeepSeek-V4-Flash-DSpark}"
PORT="${PORT:-30004}"
BACKEND="${BACKEND:-vllm}"
ENGINE="${ENGINE:-vllm-dspark}"
# Native venvs (paths are per-machine; adjust to yours)
VENV_SERVER="${VENV_SERVER:-/path/to/envs/vllm-dspark}"
VENV_CLIENT="${VENV_CLIENT:-/path/to/envs/sglang}"
SCENARIOS=(
"32 512 256"
)
Then source the platform loader and shared helpers at the top of run_bench.sh:
source "${SCRIPT_DIR}/../../../scripts/common/lib.sh"
source "${SCRIPT_DIR}/../../../scripts/common/platform.sh"
For a concrete example, see experiments/h200/dsv4_h200_dspark/. For the current mainstream form (TP/DP matrix + adaptive concurrency search, Docker-based), copy experiments/h200/dsv4_h200_vllm_tp_dp_matrix/ instead; usage in experiments/ADAPTIVE_CONCURRENCY_USAGE.md.
5. Cross-platform comparison
跨平台对比脚本目前未统一提供。可分别读取各实验 results/<run_id>/results.json 中的结构化数据,按 scenario 聚合后生成对比表。
Notes
platforms/nvidia_h200.envstill carries venv defaults under/data/user1/yy(paths from another machine). OverrideVENV_VLLM_DSPARK,VENV_SGLANG,MODEL_ROOT, orSERVER_START_SCRIPTto match your machine.- Native server management helpers are not as mature as the Docker helpers in
scripts/common/server_docker.sh. The wrapper's defaultSERVER_START_SCRIPT(scripts/start_dsv4_dspark_8card.sh) no longer exists in this repo and must be overridden.