8652a685e6
- 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
51 lines
1.9 KiB
Markdown
51 lines
1.9 KiB
Markdown
# Platform Configurations
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Each `.env` file in this directory describes one accelerator platform.
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They are meant to be sourced by benchmark scripts through
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`scripts/common/platform.sh`, not executed directly.
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## Usage
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```bash
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# Default platform for the current machine
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bash experiments/dsv4_p800_sglang/run_bench.sh
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# Explicitly select a platform
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PLATFORM=kunlun_p800 bash experiments/dsv4_p800_sglang/run_bench.sh
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```
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## Current platforms
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| File | Chip/Accelerator | Engine | Notes |
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|---|---|---|---|
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| `kunlun_p800.env` | Kunlun P800 XPU | `sglang-xpu` | Docker-based SGLang serving image |
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| `nvidia_h200.env` | NVIDIA H200 | `vllm-dspark` | Native host virtual environments |
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| `nvidia_h20.env` | NVIDIA H20 | `vllm` / `sglang` | Docker-based（vllm-openai / sglang 官方镜像） |
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| `nvidia_rtx6000d.env` | NVIDIA RTX 6000D | `vllm` / `sglang` | Docker-based，SM120 部署见 `envs/SM120_DSV4_DEPLOYMENT_GUIDE.md` |
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## What belongs here
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- Chip/accelerator identity (`CHIP`, `ACCELERATOR`, `HARDWARE`, `ENGINE`).
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- Device selection environment variables.
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- Platform-wide paths that rarely change (model root, default port).
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- Container image / interpreter paths for Docker-based platforms.
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- Native interpreter / venv paths for host-based platforms.
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## What does NOT belong here
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- Specific model names or experiment scenarios — those go in
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`experiments/<name>/config.env`.
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- Engine-specific launch flags — those go in the experiment's
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`start_server.sh` or `run_bench.sh`.
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## Adding a new platform
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1. Create `platforms/<chip>.env` with at least `CHIP`, `ACCELERATOR`,
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`HARDWARE`, `ENGINE`, `DEFAULT_PORT`, `MODEL_ROOT`.
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2. If the platform runs inside Docker, set `DOCKER_IMAGE`, `CONTAINER_NAME`,
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`CONTAINER_PYTHON`, and `PATCH_ROOT` (see `kunlun_p800.env`).
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3. If the platform runs natively on the host, set the relevant venv paths
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(see `nvidia_h200.env`).
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4. Add a row to the table above and write a quick-start experiment under
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`experiments/<name>/`.
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