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benchmark-on-ddp

가격 미확인★ 459 GitHub 스타목록 업데이트 · 2026년 10월 9일agent-skill

개요

Measure a LiteRT .tflite model with `benchmark_model` on Developer Device Platform (DDP) lab phones through `litert benchmark --ddp`, or on the Mac you run it from, and turn the session into rows of the performance leaderboard under benchmark/leaderboard - one matrix entry per model, one session per accelerator, collect results.pb and runtime_info.pb, rebuild board.json, check the page, commit the data. Use when a model needs latency and memory numbers per platform, device and accelerator, or when a new benchmark_model release means re-measuring the board.

전체 설명 읽기

소스 문서이며 이 웹사이트의 실행 지침이 아닙니다. 명령 실행 전에 권한을 확인하세요.

Benchmark on DDP

A board row is done when three things hold:

it comes from the results.pb of one benchmark job, with the delegate and the nodes it replaced (N/M) from that job's runtime_info.pb, or from the log's Replacing N out of M line where a session has no such file,
it names the model file, the platform, the device id, the accelerator and the benchmark_model release,
data/board.json was rebuilt from data/measurements.jsonl and the page shows it.

A number without those is not a row. This skill covers .tflite models on LiteRT; a LiteRT-LM bundle is one litert benchmark <bundle>.litertlm --ddp session per backend, collected by collect_lm.py (the board README, step 5). Every DDP session consumes device time billed to the Google Cloud project; the dry run prints every command it would run, so read it first. A run on the Mac you are on bills nothing.

Before you start

python3 with PyYAML, and protoc on PATH (brew install protobuf, or apt install protobuf-compiler).
For new DDP sessions: gcloud auth application-default login, a project with the Device Run API enabled (run_matrix.py --gcp-project or LITERT_GCP_PROJECT), and the litert CLI of LiteRT-CLI with the --ddp target (release 0.2.1 or newer; litert-cli-nightly has it too). Its DDP runs default to the LiteRT 2.2.0 benchmark_model (0.2.1); a row carries the version the job ran:

pip install litert-cli

For rows from a macOS host (Apple silicon; run_local.py exits on any other machine): nothing more; it fetches the macOS binary of the pinned release.

Loop

Work from the board folder, cd benchmark/leaderboard; every command below is relative to it, and the driver sits in ../driver/.

1. One model, one matrix entry. In ../driver/matrix.yaml add repo (the Hugging Face repo), file (one .tflite; a repo with several variants gets one entry per variant you want on the board), task (the repo's pipeline tag) and accelerators. Platforms and DDP devices are listed once under platforms.

2. Print the plan, then run it. On DDP:

python3 ../driver/run_matrix.py --dry-run --only litert-community/MobileNet-v2
LITERT_GCP_PROJECT=your-project-id python3 ../driver/run_matrix.py --only litert-community/MobileNet-v2

The dry run prints one litert benchmark … --ddp line per accelerator, each followed by the collect.py call that turns its session into rows. The second line submits each session, waits for it, pulls the job outputs to ~/.cache/litert-cli/ddp/<session>/<job>/, collects them and rebuilds the board; --only <repo> limits it to one model. On a macOS host, the same two steps with run_local.py (same --only): the dry run names the machine and the session; the run writes ~/.cache/litert-samples-benchmark/local/<session>/<job>/, collects it and rebuilds the board:

python3 ../driver/run_local.py --dry-run --only litert-community/MobileNet-v2
python3 ../driver/run_local.py --only litert-community/MobileNet-v2

3. Or collect a session you already have (step 2's drivers collect their own).

python3 ../driver/collect.py ~/.cache/litert-cli/ddp/session-fff9643f --model litert-community/MobileNet-v2

One row per job; a row with the same id replaces the earlier one, so a re-run is safe. A job with no results is printed on stderr and skipped: read the tail of its log before submitting it again. A DDP session is Android, named from the matrix; a session from run_local.py or from ../ios/run_ios.sh (an iPhone) carries its platform, device and OS in session.json. The row's runtime version is collect.py --runtime-version (the CLI's pin) if given, else the session's, else runtime.version from the matrix.

4. Rebuild the board (after step 3; step 2 did it) and look at the row.

python3 ../driver/build_board.py
python3 -m http.server 8000

Open http://localhost:8000/ (any free port) and click the new row. Nodes delegated reads N/M with N = M for a graph that ran fully on the accelerator; a low N on a GPU row means most of the graph ran on the CPU, and a session without runtime_info.pb reads n/a. A GPU row with Init far above Median is the delegate initializing the graph, not a defect. Numbers from: log means results.pb could not be decoded, usually a missing protoc: install it and collect again.

5. Commit the data. ../driver/matrix.yaml, data/measurements.jsonl and data/board.json in one commit; the page is static.

Watch for

A new benchmark_model release is a new row, not an edit. The row id carries the runtime version; the board shows the newest per model, platform, device and accelerator, and earlier rows stay in measurements.jsonl.
DDP device names come from the matrix. An id it does not list renders as the id; add it under platforms.android.devices.measured first. A Mac names itself from system_profiler.
Never edit a row by hand. Re-collect the session; the drivers are the only writers.

Tested on

macOS host (Mac Studio, M4 Max, macOS 27.0), Python 3.14, protoc 34.1. 2026-09-18: the Android rows in the repo (21 model files) came from run_matrix.py end to end, 42 sessions on caiman-35 (Pixel 9 Pro) and pa3q-35 (Galaxy S25 Ultra), CPU and GPU, binary 2.2.0; the macOS rows (the same files) from run_local.py end to end on that Mac; every row was then re-collected from the cached outputs and the board rebuilt. 2026-10-07: the install line above was run in a fresh venv (litert-cli 0.2.1; litert benchmark --help lists --ddp and the .litertlm bundle options, as does litert-cli-nightly 0.3.0.dev20261006), and the dry runs, collect.py and build_board.py were re-run on the same host.

소스 보기 ↗

파일 메타데이터

name: benchmark-on-ddp
description: Measure a LiteRT .tflite model with `benchmark_model` on Developer Device Platform (DDP) lab phones through `litert benchmark --ddp`, or on the Mac you run it from, and turn the session into rows of the performance leaderboard under benchmark/leaderboard - one matrix entry per model, one session per accelerator, collect results.pb and runtime_info.pb, rebuild board.json, check the page, commit the data. Use when a model needs latency and memory numbers per platform, device and accelerator, or when a new benchmark_model release means re-measuring the board.

원문 보기

---
name: benchmark-on-ddp
description: Measure a LiteRT .tflite model with `benchmark_model` on Developer Device Platform (DDP) lab phones through `litert benchmark --ddp`, or on the Mac you run it from, and turn the session into rows of the performance leaderboard under benchmark/leaderboard - one matrix entry per model, one session per accelerator, collect results.pb and runtime_info.pb, rebuild board.json, check the page, commit the data. Use when a model needs latency and memory numbers per platform, device and accelerator, or when a new benchmark_model release means re-measuring the board.
---

# Benchmark on DDP

A board row is done when three things hold:

1. it comes from the `results.pb` of one benchmark job, with the delegate and the nodes it replaced (`N/M`) from that job's `runtime_info.pb`, or from the log's `Replacing N out of M` line where a session has no such file,
2. it names the model file, the platform, the device id, the accelerator and the `benchmark_model` release,
3. `data/board.json` was rebuilt from `data/measurements.jsonl` and the page shows it.

A number without those is not a row. This skill covers `.tflite` models on [LiteRT](https://github.com/google-ai-edge/litert); a [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM)
bundle is one `litert benchmark <bundle>.litertlm --ddp` session per backend, collected by `collect_lm.py` (the board README, step 5). Every DDP session consumes device time billed to the Google Cloud project; the dry run prints every command it would run, so read it first. A run on the Mac you are on bills nothing.

## Before you start

- `python3` with PyYAML, and `protoc` on PATH (`brew install protobuf`, or `apt install protobuf-compiler`).
- For new DDP sessions: `gcloud auth application-default login`, a project with the Device Run API enabled (`run_matrix.py --gcp-project` or `LITERT_GCP_PROJECT`),
and the `litert` CLI of [LiteRT-CLI](https://github.com/google-ai-edge/LiteRT-CLI) with the `--ddp` target (release 0.2.1 or newer; `litert-cli-nightly` has it too). Its DDP runs default to the LiteRT 2.2.0 `benchmark_model` (0.2.1); a row carries the version the job ran:

```bash
pip install litert-cli
```

- For rows from a macOS host (Apple silicon; `run_local.py` exits on any other machine): nothing more; it fetches the macOS binary of the pinned release.

## Loop

Work from the board folder, `cd benchmark/leaderboard`; every command below is relative to it, and the driver sits in `../driver/`.

**1. One model, one matrix entry.** In `../driver/matrix.yaml` add `repo` (the Hugging Face repo), `file` (one `.tflite`; a repo with several variants gets one entry per
variant you want on the board), `task` (the repo's pipeline tag) and `accelerators`. Platforms and DDP devices are listed once under `platforms`.

**2. Print the plan, then run it.** On DDP:

```bash
python3 ../driver/run_matrix.py --dry-run --only litert-community/MobileNet-v2
LITERT_GCP_PROJECT=your-project-id python3 ../driver/run_matrix.py --only litert-community/MobileNet-v2
```

The dry run prints one `litert benchmark … --ddp` line per accelerator, each followed by the `collect.py` call that turns its session into rows. The second
line submits each session, waits for it, pulls the job outputs to `~/.cache/litert-cli/ddp/<session>/<job>/`, collects them and rebuilds the board; `--only <repo>`
limits it to one model. On a macOS host, the same two steps with `run_local.py` (same `--only`): the dry run names the machine and the session; the run writes `~/.cache/litert-samples-benchmark/local/<session>/<job>/`, collects it and rebuilds the board:

```bash
python3 ../driver/run_local.py --dry-run --only litert-community/MobileNet-v2
python3 ../driver/run_local.py --only litert-community/MobileNet-v2
```

**3. Or collect a session you already have** (step 2's drivers collect their own).

```bash
python3 ../driver/collect.py ~/.cache/litert-cli/ddp/session-fff9643f --model litert-community/MobileNet-v2
```

One row per job; a row with the same id replaces the earlier one, so a re-run is safe. A job with no results is printed on stderr and skipped: read the
tail of its log before submitting it again. A DDP session is Android, named from the matrix; a session from `run_local.py` or from `../ios/run_ios.sh` (an
iPhone) carries its platform, device and OS in `session.json`. The row's runtime version is `collect.py --runtime-version` (the CLI's pin) if given, else the session's, else `runtime.version` from the matrix.

**4. Rebuild the board (after step 3; step 2 did it) and look at the row.**

```bash
python3 ../driver/build_board.py
python3 -m http.server 8000
```

Open http://localhost:8000/ (any free port) and click the new row. Nodes delegated reads `N/M` with `N = M` for a graph that ran fully on the
accelerator; a low `N` on a GPU row means most of the graph ran on the CPU, and a session without `runtime_info.pb` reads n/a. A GPU row with Init far above Median is the delegate initializing the graph, not a defect. `Numbers from: log`
means `results.pb` could not be decoded, usually a missing `protoc`: install it and collect again.

**5. Commit the data.** `../driver/matrix.yaml`, `data/measurements.jsonl` and `data/board.json` in one commit; the page is static.

## Watch for

- **A new `benchmark_model` release is a new row, not an edit.** The row id carries the runtime version; the board shows the newest per model,
platform, device and accelerator, and earlier rows stay in `measurements.jsonl`.
- **DDP device names come from the matrix.** An id it does not list renders as the id; add it under `platforms.android.devices.measured` first. A Mac names itself from `system_profiler`.
- **Never edit a row by hand.** Re-collect the session; the drivers are the only writers.

## Tested on

macOS host (Mac Studio, M4 Max, macOS 27.0), Python 3.14, protoc 34.1. 2026-09-18: the Android rows in the repo (21 model files) came from `run_matrix.py` end to end, 42
sessions on caiman-35 (Pixel 9 Pro) and pa3q-35 (Galaxy S25 Ultra), CPU and GPU, binary 2.2.0; the macOS rows (the same files) from `run_local.py` end to end on that Mac; every row was then re-collected from the cached outputs and the board rebuilt. 2026-10-07: the install line above was run in a fresh venv (litert-cli 0.2.1; `litert benchmark --help` lists `--ddp` and the `.litertlm` bundle options, as does litert-cli-nightly 0.3.0.dev20261006), and the dry runs, `collect.py` and `build_board.py` were re-run on the same host.

소스 확인

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라이선스: Apache-2.0
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소스 저장소: google-ai-edge/litert-samples
라이선스: Apache-2.0
버전: Unknown
최근 GitHub 푸시: 2026년 10월 9일
목록 업데이트: 2026년 10월 9일
지침 경로: skills/benchmark-on-ddp/SKILL.md @ 1be763d24dff

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품질

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유망

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검토 필요

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Dependency or permission surface needs review
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Financial research output is not financial advice; require human review before any live investment decision
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Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution
Review status: AI review approval is missing

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      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 76,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "AI review approval is missing",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Dependency/runtime risk: command execution surface, credential or environment access"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 68,
    "label": "Promising"
  },
  "supply": {
    "track": "Coding and developer agents",
    "scenario": "Coding agents",
    "maintenance": "3d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "AI review approval is missing"
  ],
  "agent_contract": {
    "task_input": "Use benchmark-on-ddp in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 36/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "google-ai-edge-benchmark-on-ddp (benchmark-on-ddp)",
      "install_command": "",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "google-ai-edge-benchmark-on-ddp",
      "task": "Use benchmark-on-ddp in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp",
    "api": "https://www.openagentskill.com/api/agent/skills/google-ai-edge-benchmark-on-ddp",
    "audit": "https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=google-ai-edge-benchmark-on-ddp&task=Use%20benchmark-on-ddp%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20benchmark-on-ddp%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20benchmark-on-ddp%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/google-ai-edge-benchmark-on-ddp/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/google-ai-edge-benchmark-on-ddp"
  }
}

제작자 도구

등록 출처

Registry 색인

소유권 주장 가능

이 등록은 공개 소스에서 색인되었으며 유지보수자 소유권 주장이 승인될 때까지 공식으로 표시되지 않습니다.

제작자: google-ai-edge
출처: google-ai-edge/litert-samples
색인 주체: OpenAgentSkill 커뮤니티 인덱스

귀속은 공개 저장소 또는 제작자 프로필에 연결됩니다. 제작자는 등록을 주장하여 소유권 신호를 업데이트할 수 있습니다.

이 스킬 소유권 주장

소유자 소유권 주장

이 스킬 등록 소유권 주장

이 Registry 색인 등록은 google-ai-edge에게 귀속되어 있지만 아직 공식으로 표시되지 않았습니다. 소유권을 주장하면 확인된 소유자 신호가 추가되어 이후 출시, 설치 및 감사 업데이트를 더 신뢰할 수 있습니다.

공유 키트

크리에이터 백링크 키트

README에 증거 배지 추가

개발자가 저장소를 평가하는 위치에 정규 등록, 현재 신뢰 및 감사 신호, 실제 Agent-Proven 증거를 표시합니다.

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/google-ai-edge-benchmark-on-ddp?metric=listed&label=Listed)](https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/google-ai-edge-benchmark-on-ddp?metric=trust&label=Trust)](https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/google-ai-edge-benchmark-on-ddp?metric=audit&label=Audit)](https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/google-ai-edge-benchmark-on-ddp?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/google-ai-edge-benchmark-on-ddp?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

배지 미리보기 감사 열기 크리에이터 키트

커뮤니티 신호

이 스킬이 Agent 워크플로에 유용한지 알려 주세요. 집계된 피드백은 시간이 지날수록 순위를 개선합니다.

개요

Benchmark on DDP

Before you start

Loop

Watch for

Tested on

소스 확인

가격 및 실행 비용

작은 작업부터 시작

출처 및 사용 안내

Agent 연결

관련 스킬

제작자 도구

Registry 색인

이 스킬 등록 소유권 주장

README에 증거 배지 추가

커뮤니티 신호