| # code/scripts |
|
|
| | script | purpose | device | |
| |---|---|---| |
| | `fetch_samples.sh` | downloads the public sample data that is not redistributable (sha256-checked) | no | |
| | `bench.py` | stage breakdown of warm forwards (host pre-processing with decode / squash split, staging copy, H2D, one blocking replay, D2H with copy / host-conversion split, host post-processing, e2e p50 / p99, back-to-back replays), AICLK / power sampling, per-input check against the stored fp32 CPU reference; `--variant`, `--dispatch`, `--cqs`, `--input` / `--golden`, `--json` (the card / OPT_BASELINE numbers) | yes, via `bin/devrun` | |
| | `profile_ops.py` | one eager forward (with `m:<block>` / `c:<layer>` signposts) + one traced replay between the `eager` / `trace` signposts under the device profiler (`python -m tracy -r -p -v --op-support-count 6000 ...`: a frame is more than the default 1000 programs) | yes | |
| | `profile_summary.py` | CPU-only summary of a `profile_ops.py` ops CSV: programs per frame, kernel sum vs span, op-to-op gaps, tables per category / block / op code / layer, top ops, the per-layer cost split (compute, conv data movement, activation split, fp32 sum / bias / cast) (the OPT_BASELINE.md profile tables) | no | |
| | `visualizer_report.py` | ttnn-visualizer memory + graph report (`db.sqlite`, `config.json`) of one eager forward; never for timing | yes | |
| | `container_smoke.sh` | serve ONE serve profile of the built package (`--profile NAME`; default profile otherwise), run `server/smoke_test.py` against it (asserts ETH dispatch, the 12x10 grid and the profile's pins; compares with the stored CPU reference), keep the evidence (container log, `/info`, the `/predict` output and the result, in `logs/smoke/` of the repo or `--log-dir DIR`), always stop it; one `bin/devrun -t 3600 -k 150` window per profile | yes | |
| | `make_demo.py` | renders `media/*.png` (BEV / overlay views) from the shipped sample | yes | |
| | `ref_golden.py` | goldens of the fp32 CPU reference (`tt_sceneseg.reference`): small ones in `tests/goldens/`, the smoke references `samples/highway_normal_1.<variant>.reference.json`, large ones (module taps, public frames, comma10k with the training ROI crop) in the workspace's `research/sceneseg/goldens/` (research venv, CPU) | no | |
| | `make_weights_upload.py` | stages the re-hosted weights for `changh95/sceneseg-p150` `weights/` (PLAN.md D10): the deployed ONNX byte for byte, the `.pth` as bit-identical safetensors, LICENSE / NOTICE, `provenance.json` (Drive ids, sha256), in the git-ignored `weights_upload/` with `UPLOAD.md` for the orchestrator (research venv, CPU) | no | |
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| Everything here ships in `code/` on the Hub and inside the image (`source.extra_code` lists `scripts`). |
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