sskj/platforms
yy-fighting 8652a685e6 rewrite README, add new platform onboarding guide, fix broken scripts/common paths
- 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
2026-07-17 06:18:05 +00:00
..

Platform Configurations

Each .env file in this directory describes one accelerator platform. They are meant to be sourced by benchmark scripts through scripts/common/platform.sh, not executed directly.

Usage

# Default platform for the current machine
bash experiments/dsv4_p800_sglang/run_bench.sh

# Explicitly select a platform
PLATFORM=kunlun_p800 bash experiments/dsv4_p800_sglang/run_bench.sh

Current platforms

File Chip/Accelerator Engine Notes
kunlun_p800.env Kunlun P800 XPU sglang-xpu Docker-based SGLang serving image
nvidia_h200.env NVIDIA H200 vllm-dspark Native host virtual environments
nvidia_h20.env NVIDIA H20 vllm / sglang Docker-basedvllm-openai / sglang 官方镜像)
nvidia_rtx6000d.env NVIDIA RTX 6000D vllm / sglang Docker-basedSM120 部署见 envs/SM120_DSV4_DEPLOYMENT_GUIDE.md

What belongs here

  • Chip/accelerator identity (CHIP, ACCELERATOR, HARDWARE, ENGINE).
  • Device selection environment variables.
  • Platform-wide paths that rarely change (model root, default port).
  • Container image / interpreter paths for Docker-based platforms.
  • Native interpreter / venv paths for host-based platforms.

What does NOT belong here

  • Specific model names or experiment scenarios — those go in experiments/<name>/config.env.
  • Engine-specific launch flags — those go in the experiment's start_server.sh or run_bench.sh.

Adding a new platform

  1. Create platforms/<chip>.env with at least CHIP, ACCELERATOR, HARDWARE, ENGINE, DEFAULT_PORT, MODEL_ROOT.
  2. If the platform runs inside Docker, set DOCKER_IMAGE, CONTAINER_NAME, CONTAINER_PYTHON, and PATCH_ROOT (see kunlun_p800.env).
  3. If the platform runs natively on the host, set the relevant venv paths (see nvidia_h200.env).
  4. Add a row to the table above and write a quick-start experiment under experiments/<name>/.