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benchmark-on-ddp

Precio sin confirmar★ 459 Estrellas de GitHubRegistro actualizado · 9 oct 2026agent-skill

Resumen

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.

Leer documentación completa

Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

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.

Ver fuente ↗

Metadatos del archivo

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.

Ver texto original

---
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.

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Repositorio fuente: google-ai-edge/litert-samples
Licencia: Apache-2.0
Versión: Unknown
Último push de GitHub: 9 oct 2026
Registro actualizado: 9 oct 2026
Ruta de instrucciones: skills/benchmark-on-ddp/SKILL.md @ 1be763d24dff

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    "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"
  }
}

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Creador: google-ai-edge
Fuente: google-ai-edge/litert-samples
Indexado por: Índice comunitario de OpenAgentSkill

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