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yy-fighting 5c749cda03 feat(pro6000/GLM-5.3): 方案 D/E/F 部署资产入库（TP2PP4 生产配方 / TP8+DFlash2 / PD 分离四角色链） 
- scripts: 11 个服务器原样脚本入库（md5 对照表更新至 README）；D=60.1 生产原样配方、
  E=v5 DFlash 底稿、F=PD 链四角色部署+launch+双场景压测驱动
- profiles: 新增 6 个 .env（D/E 单机 + F 四角色，均带镜像 digest
  sha256:28e0d260…，对齐 kimi3 PD 多角色先例）
- deploy/PD_CHAIN.md: 方案 F 编排手册（启动顺序 mc-master→prefill→decode→router、
  基础设施依赖表、质量门口径、拆链恢复、÷2 单机等效判决）
- platforms/patches/pro6000/glm53_pd_chain/: sglang 补丁树 vs 镜像原版 11 文件
  unified diff 快照——宿主树无 .git，此为唯一版本记录（DFlash+PP+PD 解锁全集）
- deploy/manifests/: GLM-5.3-NVFP4(47分片)/GLM-5.3-DFlash2(单分片) 权重 md5 清单
- deploy/CURRENT.md: 全集群现役状态页（2026-09-08 八机实测）
- deploy/verify_profile.sh: 防漂移核验工具（digest+参数 token 比对+端口/health，
  已在 60.1 生产容器实测 PASS）
2026-09-08 16:36:15 +08:00
..
patches
feat(pro6000/GLM-5.3): 方案 D/E/F 部署资产入库（TP2PP4 生产配方 / TP8+DFlash2 / PD 分离四角色链）
2026-09-08 16:36:15 +08:00
ascend_910c.env
feat(platform): add Ascend 910C NPU platform support
2026-07-27 22:00:05 +08:00
kunlun_p800.env
refactor(paths): derive repository files from root
2026-07-20 17:33:51 +08:00
nvidia_h20.env
refactor(paths): derive repository files from root
2026-07-20 17:33:51 +08:00
nvidia_h200.env
docs(h200): H200 quick-start guide, porting workflow, and example experiment
2026-07-08 05:55:11 +00:00
nvidia_rtx6000d.env
Add dsv4_pro6000_sglang_tp_dp_matrix experiment for RTX 6000D
2026-07-13 02:18:34 +00:00
README.md
feat(platform): add Ascend 910C NPU platform support
2026-07-27 22:00:05 +08:00

README.md

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
ascend_910c.env Huawei Ascend 910C vllm-ascend Docker-based，8 卡 16 die / 64GB HBM；Ascend Docker Runtime 为默认 runtime，见 envs/ASCEND_910C_ENV_SETUP.md
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-based（vllm-openai / sglang 官方镜像）
nvidia_rtx6000d.env NVIDIA RTX 6000D vllm / sglang Docker-based，SM120 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>/.