docs(h200): H200 quick-start guide, porting workflow, and example experiment
- Update platforms/nvidia_h200.env with ENGINE and configurable venv/paths. - Update platforms/README.md with H200 notes and platform-addition guide. - Add docs/H200_QUICKSTART.md with concrete H200 usage steps. - Add experiments/dsv4_h200_dspark/ as a migration wrapper around the existing scripts/benchmark_dspark_0707/ grid benchmark, writing results into the new experiments/<name>/results/<RUN_ID>/ layout. - Update BENCHMARK_WORKFLOW.md with a 'Adding a New Platform or Experiment' section and mention the H200 wrapper in Quick Start. - Update main README.md to index the H200 experiment and link to the guide.
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@ -218,12 +218,18 @@ The JSON file inside each `bench_results/<run>/` directory should follow a stabl
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## Quick Start
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### Run P800 SGLang benchmark
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### Run P800 SGLang benchmark (Kunlun P800, Docker)
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```bash
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bash experiments/dsv4_p800_sglang/run_bench.sh
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```
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### Run H200 DSpark benchmark (NVIDIA H200, native venv)
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```bash
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bash experiments/dsv4_h200_dspark/run_bench.sh
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```
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### Run legacy DSpark grid benchmark
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```bash
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@ -254,6 +260,63 @@ python3 experiments/dsv4_p800_sglang/parse_results.py \
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python3 scripts/analysis/compare_experiments.py
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```
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## Adding a New Platform or Experiment
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### 1. Add or update a platform config
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Create `platforms/<chip>.env` with identity and platform-wide paths:
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```bash
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CHIP="my_chip"
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ACCELERATOR="My Accelerator"
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HARDWARE="8x My Accelerator"
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ENGINE="vllm-myengine"
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DEFAULT_PORT="30000"
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MODEL_ROOT="/data/models"
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```
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For Docker-based platforms, also set `DOCKER_IMAGE`, `CONTAINER_NAME`,
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`CONTAINER_PYTHON`, and `PATCH_ROOT` (see `platforms/kunlun_p800.env`).
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For host-native platforms, set the relevant venv paths (see
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`platforms/nvidia_h200.env`).
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### 2. Create an experiment directory
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```
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experiments/<name>/
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├── README.md # Purpose and usage
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├── config.env # Model, port, scenarios, engine overrides
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├── start_server.sh # (optional) platform-specific server launch
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├── run_bench.sh # Orchestrator
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└── parse_results.py # Convert raw outputs to results.json + report.md
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```
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At minimum, `run_bench.sh` should:
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1. Source `scripts/common/lib.sh` and `scripts/common/platform.sh`.
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2. Read `config.env`.
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3. Create `experiments/<name>/results/<RUN_ID>/`.
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4. Call `write_metadata_json` to create `results.json`.
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5. Run the benchmark scenarios.
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6. Call `parse_results.py` to generate `report.md`.
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### 3. Reuse shared helpers
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- `scripts/common/lib.sh`: logging, health checks, metadata JSON.
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- `scripts/common/platform.sh`: platform auto-detection and env loading.
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- `scripts/common/server_docker.sh`: Docker-based server lifecycle.
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- `scripts/common/bench_client_docker.sh`: run `sglang.bench_serving` inside a container.
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Native (non-Docker) platforms can delegate to existing start scripts or add
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new shared helpers under `scripts/common/`.
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### 4. Example experiments
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- Docker / XPU: `experiments/dsv4_p800_sglang/`
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- Native / H200: `experiments/dsv4_h200_dspark/`
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See `docs/H200_QUICKSTART.md` for a concrete H200 porting walkthrough.
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## Checklist Before Committing / Archiving
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- [ ] No `.jsonl`, `.json`, `.log`, or `.md` files left in the project root.
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13
README.md
13
README.md
@ -1,6 +1,7 @@
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# DSV4 / DeepSeek-V4-Flash 推理测速项目
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> 记录 vllm-dspark、SGLang、vLLM 等后端在 DeepSeek-V4-Flash 上的 benchmark 脚本与最终结果。
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> H200 快速上手指南见 [`docs/H200_QUICKSTART.md`](docs/H200_QUICKSTART.md)。
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## 目录说明
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@ -24,6 +25,7 @@
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| 实验 | 脚本 | 说明 |
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|---|---|---|
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| DSV4 P800 SGLang | `experiments/dsv4_p800_sglang/run_bench.sh` | Kunlun P800 + SGLang + DeepSeek-V4-Flash-INT8 |
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| DSV4 H200 DSpark | `experiments/dsv4_h200_dspark/run_bench.sh` | NVIDIA H200 + vllm-dspark + DeepSeek-V4-Flash-DSpark |
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### 旧结构(scripts/ + bench_results/)
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@ -46,7 +48,13 @@
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bash experiments/dsv4_p800_sglang/run_bench.sh
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```
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### DSpark grid
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### H200 DSpark
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```bash
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bash experiments/dsv4_h200_dspark/run_bench.sh
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```
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### DSpark grid(旧结构)
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```bash
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bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
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@ -65,6 +73,9 @@ bash scripts/benchmark_dspark_0707/run_dspark_st_comparison.sh
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python3 experiments/dsv4_p800_sglang/parse_results.py \
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experiments/dsv4_p800_sglang/results/<run_id>
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python3 experiments/dsv4_h200_dspark/parse_results.py \
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experiments/dsv4_h200_dspark/results/<run_id>
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# 旧结构
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/data/user1/yy/envs/sglang/bin/python scripts/benchmark_dspark_0707/parse_results.py \
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/data/user1/yy/bench_results/dspark_grid_20260707-132641
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111
docs/H200_QUICKSTART.md
Normal file
111
docs/H200_QUICKSTART.md
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@ -0,0 +1,111 @@
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# H200 Quick Start Guide
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This guide covers how to use this benchmark repository on an NVIDIA H200 machine.
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## Current state
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The H200 path is currently based on **native host virtual environments** (not Docker):
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- Server engine: `vllm-dspark`
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- Server env: `envs/vllm-dspark`
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- Benchmark client env: `envs/sglang` (uses `sglang.bench_serving --backend vllm`)
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- Default model: `/data/models/DeepSeek-V4-Flash-DSpark`
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- Default port: `30004`
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The legacy benchmark scripts live under `scripts/benchmark_dspark_0707/`. A migration wrapper experiment `experiments/dsv4_h200_dspark/` is also provided so the results follow the new `experiments/<name>/results/<RUN_ID>/` layout.
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## 1. Pull and verify
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```bash
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git clone <repo-url> /data/user1/yy
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cd /data/user1/yy
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# Platform should auto-detect as nvidia_h200
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source scripts/common/platform.sh
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```
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If auto-detection fails, set it explicitly:
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```bash
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PLATFORM=nvidia_h200 source scripts/common/platform.sh
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```
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## 2. Run the existing DSpark benchmark grid
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This uses the legacy scripts and writes results to `bench_results/dspark_grid_<RUN_ID>/`.
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```bash
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bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
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```
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To reuse an already-running server:
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```bash
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SKIP_MANAGE_SERVER=1 bash scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh
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```
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## 3. Run the migration wrapper experiment
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This produces results in the new experiment-centric layout:
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```bash
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bash experiments/dsv4_h200_dspark/run_bench.sh
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```
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Results land in `experiments/dsv4_h200_dspark/results/<RUN_ID>/`.
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## 4. Create a new H200 experiment
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To add a new H200 benchmark (for example a different model or engine), create:
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```
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experiments/<your_name>/
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├── README.md # What this experiment measures
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├── config.env # Model, port, scenarios, venv paths
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├── start_server.sh # (optional) native server launch
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├── run_bench.sh # Orchestrator: server → benchmark → stop
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└── parse_results.py # Generate results.json + report.md
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```
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Minimum `config.env`:
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```bash
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EXPERIMENT="${EXPERIMENT:-<your_name>}"
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MODEL_NAME="${MODEL_NAME:-DeepSeek-V4-Flash-DSpark}"
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MODEL_PATH="${MODEL_PATH:-/data/models/DeepSeek-V4-Flash-DSpark}"
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PORT="${PORT:-30004}"
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BACKEND="${BACKEND:-vllm}"
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ENGINE="${ENGINE:-vllm-dspark}"
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# Native venvs
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VENV_SERVER="${VENV_SERVER:-/data/user1/yy/envs/vllm-dspark}"
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VENV_CLIENT="${VENV_CLIENT:-/data/user1/yy/envs/sglang}"
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SCENARIOS=(
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"32 512 256"
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)
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```
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Then source the platform loader and shared helpers at the top of `run_bench.sh`:
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```bash
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source "${SCRIPT_DIR}/../../scripts/common/lib.sh"
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source "${SCRIPT_DIR}/../../scripts/common/platform.sh"
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```
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For a concrete example, see `experiments/dsv4_h200_dspark/`.
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## 5. Cross-platform comparison
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After running experiments on both P800 and H200, compare them with:
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```bash
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python3 scripts/analysis/compare_experiments.py \
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experiments/dsv4_p800_sglang/results/<run_id> \
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experiments/dsv4_h200_dspark/results/<run_id>
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```
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## Notes
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- `platforms/nvidia_h200.env` assumes the legacy directory layout under `/data/user1/yy`. If your H200 machine uses different paths, override `VENV_VLLM_DSPARK`, `VENV_SGLANG`, `MODEL_ROOT`, or `SERVER_START_SCRIPT`.
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- Native server management helpers are not yet as mature as the Docker helpers in `scripts/common/server_docker.sh`. The H200 wrapper currently delegates to the existing `scripts/start_dsv4_dspark_8card.sh`.
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41
experiments/dsv4_h200_dspark/README.md
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41
experiments/dsv4_h200_dspark/README.md
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@ -0,0 +1,41 @@
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# DSV4 H200 DSpark Benchmark
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NVIDIA H200 + native `vllm-dspark` + `DeepSeek-V4-Flash-DSpark` benchmark experiment.
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This is a migration wrapper: it reuses the proven legacy scripts under
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`scripts/benchmark_dspark_0707/` and `scripts/start_dsv4_dspark_8card.sh`,
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but stores results in the new `experiments/<name>/results/<RUN_ID>/` layout.
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## Quick Start
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```bash
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# Run the full DSpark P1/P2/P3 grid
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bash experiments/dsv4_h200_dspark/run_bench.sh
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# Reuse an already-running server
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SKIP_MANAGE_SERVER=1 bash experiments/dsv4_h200_dspark/run_bench.sh
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```
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Results land in `experiments/dsv4_h200_dspark/results/<RUN_ID>/`.
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## Configuration
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Edit `config.env` or override via environment variables:
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```bash
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MODEL_PATH=/data/models/DeepSeek-V4-Flash-DSpark \
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PORT=30004 \
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bash experiments/dsv4_h200_dspark/run_bench.sh
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```
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## Files
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| File | Purpose |
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|---|---|
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| `config.env` | Experiment-level configuration (model, port, venv paths) |
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| `run_bench.sh` | Orchestrator: metadata → legacy grid benchmark → parse |
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| `parse_results.py` | Wraps the legacy parser to generate `report.md` |
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## Platform
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This experiment targets `platforms/nvidia_h200.env`.
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22
experiments/dsv4_h200_dspark/config.env
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22
experiments/dsv4_h200_dspark/config.env
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# Experiment-level configuration for dsv4_h200_dspark
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# All values can be overridden via environment variables.
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EXPERIMENT="${EXPERIMENT:-dsv4_h200_dspark}"
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MODEL_NAME="${MODEL_NAME:-DeepSeek-V4-Flash-DSpark}"
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MODEL_PATH="${MODEL_PATH:-/data/models/DeepSeek-V4-Flash-DSpark}"
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SERVED_MODEL_NAME="${SERVED_MODEL_NAME:-deepseek-v4-flash-dspark}"
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PORT="${PORT:-30004}"
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BACKEND="${BACKEND:-vllm}"
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ENGINE="${ENGINE:-vllm-dspark}"
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# Native virtual environments on the host.
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# Override if your H200 machine uses different paths.
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VENV_SERVER="${VENV_SERVER:-/data/user1/yy/envs/vllm-dspark}"
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VENV_CLIENT="${VENV_CLIENT:-/data/user1/yy/envs/sglang}"
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# Legacy server start script.
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SERVER_START_SCRIPT="${SERVER_START_SCRIPT:-${ROOT_DIR}/scripts/start_dsv4_dspark_8card.sh}"
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# Legacy benchmark orchestrator.
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LEGACY_GRID_SCRIPT="${LEGACY_GRID_SCRIPT:-${ROOT_DIR}/scripts/benchmark_dspark_0707/run_dspark_benchmark_grid.sh}"
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LEGACY_PARSE_SCRIPT="${LEGACY_PARSE_SCRIPT:-${ROOT_DIR}/scripts/benchmark_dspark_0707/parse_results.py}"
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36
experiments/dsv4_h200_dspark/parse_results.py
Executable file
36
experiments/dsv4_h200_dspark/parse_results.py
Executable file
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#!/usr/bin/env python3
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"""Parse H200 DSpark benchmark outputs.
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This is a thin wrapper around the legacy parser in
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scripts/benchmark_dspark_0707/parse_results.py. It exists so that the
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experiment keeps the same entry point as other experiments.
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Usage:
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python3 parse_results.py <result_root>
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"""
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import os
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import subprocess
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import sys
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from pathlib import Path
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def main() -> None:
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result_root = Path(sys.argv[1]) if len(sys.argv) > 1 else Path("results")
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repo_root = Path(__file__).resolve().parents[2]
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legacy_parser = repo_root / "scripts" / "benchmark_dspark_0707" / "parse_results.py"
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if not legacy_parser.exists():
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raise SystemExit(f"legacy parser not found: {legacy_parser}")
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venv_client = os.environ.get("VENV_CLIENT", "/data/user1/yy/envs/sglang")
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python = Path(venv_client) / "bin" / "python"
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if not python.exists():
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python = Path(sys.executable)
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subprocess.run([str(python), str(legacy_parser), str(result_root)], check=True)
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print(f"Parsed {result_root}")
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if __name__ == "__main__":
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main()
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66
experiments/dsv4_h200_dspark/run_bench.sh
Executable file
66
experiments/dsv4_h200_dspark/run_bench.sh
Executable file
@ -0,0 +1,66 @@
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#!/usr/bin/env bash
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# H200 native vllm-dspark benchmark orchestrator.
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# This wraps the legacy scripts/benchmark_dspark_0707/ grid benchmark so that
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# results live under experiments/dsv4_h200_dspark/results/<RUN_ID>/.
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set -Eeuo pipefail
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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EXPERIMENT_NAME="$(basename "$SCRIPT_DIR")"
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# shellcheck source=/dev/null
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source "${SCRIPT_DIR}/../../scripts/common/lib.sh"
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# shellcheck source=/dev/null
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source "${SCRIPT_DIR}/../../scripts/common/platform.sh"
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# shellcheck source=/dev/null
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source "${SCRIPT_DIR}/config.env"
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RUN_ID="${RUN_ID:-$(date '+%Y%m%d-%H%M%S')}"
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RESULT_ROOT="${RESULT_ROOT:-${SCRIPT_DIR}/results/${RUN_ID}}"
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LOG_DIR="${RESULT_ROOT}/logs"
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ensure_result_root "$RESULT_ROOT"
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log_init "${LOG_DIR}/orchestrator.log"
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log "experiment=${EXPERIMENT_NAME}"
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log "run_id=${RUN_ID}"
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log "result_root=${RESULT_ROOT}"
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log "platform=${PLATFORM}"
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log "chip=${CHIP}"
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log "accelerator=${ACCELERATOR}"
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log "engine=${ENGINE}"
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log "hardware=${HARDWARE}"
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log "model=${MODEL_PATH}"
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log "server_start_script=${SERVER_START_SCRIPT}"
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# Write initial metadata for this run.
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METADATA_JSON="${RESULT_ROOT}/results.json"
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write_metadata_json \
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"$METADATA_JSON" \
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"$EXPERIMENT_NAME" \
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"$RUN_ID" \
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"$MODEL_PATH" \
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"$BACKEND" \
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"$ENGINE" \
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"$HARDWARE" \
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"$ACCELERATOR" \
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"$CHIP" \
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"experiments/${EXPERIMENT_NAME}/run_bench.sh" \
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"$VENV_SERVER" \
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"H200 native vllm-dspark benchmark for DeepSeek-V4-Flash-DSpark"
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log "starting legacy DSpark grid benchmark"
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RUN_ID="$RUN_ID" \
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RESULT_ROOT="$RESULT_ROOT" \
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PORT="$PORT" \
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MODEL_PATH="$MODEL_PATH" \
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SERVER_START_SCRIPT="$SERVER_START_SCRIPT" \
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SKIP_MANAGE_SERVER="${SKIP_MANAGE_SERVER:-}" \
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bash "$LEGACY_GRID_SCRIPT" >> "${LOG_DIR}/legacy_grid.log" 2>&1
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log "parsing results"
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"$VENV_CLIENT/bin/python" "$LEGACY_PARSE_SCRIPT" "$RESULT_ROOT" >> "${LOG_DIR}/parse.log" 2>&1 || {
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log "WARNING: legacy parser failed; see ${LOG_DIR}/parse.log"
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}
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log "all results saved to ${RESULT_ROOT}"
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@ -19,14 +19,15 @@ PLATFORM=kunlun_p800 bash experiments/dsv4_p800_sglang/run_bench.sh
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| File | Chip/Accelerator | Engine | Notes |
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|---|---|---|---|
|
||||
| `kunlun_p800.env` | Kunlun P800 XPU | `sglang-xpu` | Docker-based SGLang serving image |
|
||||
| `nvidia_h200.env` | NVIDIA H200 | native vllm/sglang | Host virtual environments |
|
||||
| `nvidia_h200.env` | NVIDIA H200 | `vllm-dspark` | Native host virtual environments |
|
||||
|
||||
## What belongs here
|
||||
|
||||
- Chip/accelerator identity (`CHIP`, `ACCELERATOR`, `HARDWARE`).
|
||||
- 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
|
||||
|
||||
@ -34,3 +35,14 @@ PLATFORM=kunlun_p800 bash experiments/dsv4_p800_sglang/run_bench.sh
|
||||
`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>/`.
|
||||
|
||||
@ -4,6 +4,7 @@
|
||||
CHIP="nvidia_h200"
|
||||
ACCELERATOR="NVIDIA H200"
|
||||
HARDWARE="8x NVIDIA H200 143GB"
|
||||
ENGINE="vllm-dspark"
|
||||
|
||||
# Device selection
|
||||
DEVICE_SELECT_ENV="CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7"
|
||||
@ -13,6 +14,10 @@ CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
|
||||
DEFAULT_PORT="30004"
|
||||
MODEL_ROOT="/data/models"
|
||||
|
||||
# Virtual environments on the host (used by native H200 scripts)
|
||||
VENV_VLLM_DSPARK="/data/user1/yy/envs/vllm-dspark"
|
||||
VENV_SGLANG="/data/user1/yy/envs/sglang"
|
||||
# Virtual environments on the host (used by native H200 scripts).
|
||||
# Override these if your H200 machine uses different paths.
|
||||
VENV_VLLM_DSPARK="${VENV_VLLM_DSPARK:-/data/user1/yy/envs/vllm-dspark}"
|
||||
VENV_SGLANG="${VENV_SGLANG:-/data/user1/yy/envs/sglang}"
|
||||
|
||||
# Default server start script for the legacy DSpark benchmark grid.
|
||||
SERVER_START_SCRIPT="${SERVER_START_SCRIPT:-${ROOT_DIR}/scripts/start_dsv4_dspark_8card.sh}"
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user