sskj/platforms

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.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>/.