a4e38b9e33
Move all experiments under hardware-specific folders: - experiments/h200/ : H200 GPU experiments (15 dirs) - experiments/h20/ : H20 GPU experiments (2 dirs) - experiments/p800/ : Kunlun P800 experiments (3 dirs) - experiments/pro6000/ : RTX 6000D experiments (2 dirs) This improves discoverability and keeps hardware-specific configs isolated from each other.
44 lines
1.2 KiB
Markdown
44 lines
1.2 KiB
Markdown
# DSV4 H200 vLLM Baseline Benchmark
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NVIDIA H200 + native `vllm` + `DeepSeek-V4-Flash` baseline benchmark experiment.
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This experiment uses:
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- `/data/user1/yy/envs/vllm` as the server environment
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- `/data/user1/yy/envs/sglang` as the benchmark client environment
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## Quick Start
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```bash
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# Run the full experiment (start server + benchmark + parse)
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bash experiments/dsv4_h200_vllm/run_bench.sh
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# Reuse an already-running server
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SKIP_MANAGE_SERVER=1 bash experiments/dsv4_h200_vllm/run_bench.sh
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```
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Results land in `experiments/dsv4_h200_vllm/results/<RUN_ID>/`.
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## Configuration
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Edit `config.env` or override via environment variables:
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```bash
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MODEL_PATH=/data/models/DeepSeek-V4-Flash \
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PORT=30005 \
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SCENARIOS="32 512 256 128 512 256" \
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bash experiments/dsv4_h200_vllm/run_bench.sh
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```
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## Files
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| File | Purpose |
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|---|---|
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| `config.env` | Experiment-level configuration (model, port, venv paths, scenarios) |
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| `start_server.sh` | Start a plain vLLM baseline server for DeepSeek-V4-Flash |
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| `run_bench.sh` | Orchestrator: metadata → server → benchmark → parse |
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| `parse_results.py` | Parse raw JSONL outputs into `results.json` + `report.md` |
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## Platform
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This experiment targets `platforms/nvidia_h200.env`.
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