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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 mo
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.
Source documentation, not instructions for this website. Review permissions before running any commands.
A board row is done when three things hold:
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,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.
python3 with PyYAML, and protoc on PATH (brew install protobuf, or apt install protobuf-compiler).gcloud auth application-default login, a project with the Device Run API enabled (--gcp-project or LITERT_GCP_PROJECT),
and the litert CLI of LiteRT-CLI with the --ddp target (the 0.2.0 release predates it):pip install litert-cli-nightly
run_local.py fetches the macOS binary of the pinned release.Work from the board folder, cd benchmark/leaderboard (or wherever the board lives); 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, 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
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 this Mac, 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
python3 ../driver/run_local.py
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 --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. Check: 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; 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.
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.platforms.android.devices.measured first. A Mac names itself from system_profiler.macOS host (Mac Studio, M4 Max, macOS 27.0), Python 3.14, protoc 34.1: the Android rows in the repo (21 model files, 2026-09-18) 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, 2026-09-18) from run_local.py end to end on that Mac; every row was then re-collected from the cached outputs and the board rebuilt (2026-09-18). The install line above was run in a fresh venv on 2026-09-23 (litert-cli-nightly 0.3.0.dev20260922; litert benchmark --help lists --ddp and the .litertlm bundle options).
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.
--- 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 (`--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 (the 0.2.0 release predates it): ```bash pip install litert-cli-nightly ``` - For rows from this Mac: nothing more; `run_local.py` fetches the macOS binary of the pinned release. ## Loop Work from the board folder, `cd benchmark/leaderboard` (or wherever the board lives); 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`, `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 ``` 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 this Mac, 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 python3 ../driver/run_local.py ``` **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 `--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. Check: 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; 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: the Android rows in the repo (21 model files, 2026-09-18) 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, 2026-09-18) from `run_local.py` end to end on that Mac; every row was then re-collected from the cached outputs and the board rebuilt (2026-09-18). The install line above was run in a fresh venv on 2026-09-23 (litert-cli-nightly 0.3.0.dev20260922; `litert benchmark --help` lists `--ddp` and the `.litertlm` bundle options).
Skill source recorded
Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.
Review before install: Avoid automatic install
License: Apache-2.0
Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.
Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.
Version reported in registry metadata; check source releases before relying on it.
Quality
68/100
Promising
Trust
62/100
This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.
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