bf03201168
sitecustomize.py is imported automatically by Python at startup in every process, including the SGLang scheduler/TP worker processes spawned for tensor parallelism. Adding the p800_timing import here ensures the monkey-patches are applied to all 8 TP workers, not just the launch_server parent process (which never runs the model forward and would record empty timing data). The timing module self-disables unless SGLANG_TIMING_ENABLED=1, so this import is a no-op when timing is not in use. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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-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.shorrun_bench.sh.
Adding a new platform
- Create
platforms/<chip>.envwith at leastCHIP,ACCELERATOR,HARDWARE,ENGINE,DEFAULT_PORT,MODEL_ROOT. - If the platform runs inside Docker, set
DOCKER_IMAGE,CONTAINER_NAME,CONTAINER_PYTHON, andPATCH_ROOT(seekunlun_p800.env). - If the platform runs natively on the host, set the relevant venv paths
(see
nvidia_h200.env). - Add a row to the table above and write a quick-start experiment under
experiments/<name>/.