# H200 Quick Start Guide

This guide covers how to use this benchmark repository on an NVIDIA H200 machine.

## Current state

The H200 path is currently based on **native host virtual environments** (not Docker):

- Server engine: `vllm-dspark`
- Server env: `envs/vllm-dspark`
- Benchmark client env: `envs/sglang` (uses `sglang.bench_serving --backend vllm`)
- Default model: `/data/models/DeepSeek-V4-Flash-DSpark`
- Default port: `30004`

The legacy benchmark scripts live under `scripts/benchmark_dspark_0707/`. A migration wrapper experiment `experiments/dsv4_h200_dspark/` is also provided so the results follow the new `experiments/<name>/results/<RUN_ID>/` layout.

## 1. Pull and verify

```bash
git clone <repo-url> /data/user1/yy
cd /data/user1/yy

# Platform should auto-detect as nvidia_h200
source scripts/common/platform.sh
```

If auto-detection fails, set it explicitly:

```bash
PLATFORM=nvidia_h200 source scripts/common/platform.sh
```

## 2. Run the existing DSpark benchmark grid

This uses the legacy scripts and writes results to `bench_results/dspark_grid_<RUN_ID>/`.

```bash
bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
```

To reuse an already-running server:

```bash
SKIP_MANAGE_SERVER=1 bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
```

## 3. Run the migration wrapper experiment

This produces results in the new experiment-centric layout:

```bash
bash experiments/dsv4_h200_dspark/run_bench.sh
```

Results land in `experiments/dsv4_h200_dspark/results/<RUN_ID>/`.

## 4. Create a new H200 experiment

To add a new H200 benchmark (for example a different model or engine), create:

```
experiments/<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`:

```bash
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
VENV_SERVER="${VENV_SERVER:-/data/user1/yy/envs/vllm-dspark}"
VENV_CLIENT="${VENV_CLIENT:-/data/user1/yy/envs/sglang}"

SCENARIOS=(
  "32 512 256"
)
```

Then source the platform loader and shared helpers at the top of `run_bench.sh`:

```bash
source "${SCRIPT_DIR}/../../scripts/common/lib.sh"
source "${SCRIPT_DIR}/../../scripts/common/platform.sh"
```

For a concrete example, see `experiments/dsv4_h200_dspark/`.

## 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.env` assumes the legacy directory layout under `/data/user1/yy`. If your H200 machine uses different paths, override `VENV_VLLM_DSPARK`, `VENV_SGLANG`, `MODEL_ROOT`, or `SERVER_START_SCRIPT`.
- Native server management helpers are not yet as mature as the Docker helpers in `scripts/common/server_docker.sh`. The H200 wrapper currently delegates to the existing `scripts/start_dsv4_dspark_8card.sh`.