| |
| """ASGI serving app of meteor-p150 on one Tenstorrent Blackhole p150: the HTTP contract of the Autoware |
| collection (vendored ``ttaw.server.app``, BUNDLE_CONVENTIONS.md section 7) bound to :class:`METEOR`. |
| |
| Served by tt-model-manager as ``kind: tt-dit-server``:: |
| |
| python -m uvicorn --host 0.0.0.0 --port <p> --lifespan on tt_meteor.server.app:app |
| |
| Routes: ``GET /``, ``/health`` and ``/v1/health`` (always 200: ``ok`` / ``starting`` / ``error``), ``/info``, |
| ``/v1/models`` (stub), ``POST /predict``; errors 400 / 422 / 503 / 500 (SERVING.md section 3). Everything that |
| touches the device happens in the lifespan: weights -> device (ETH dispatch, 12x10) -> graph -> trace capture of |
| every warm-up variant, so uvicorn's ``Application startup complete`` (the line ``tt-model serve`` waits for) means |
| warm; SIGTERM (``tt-model stop``, 120 s) closes the model under the lock. ``/predict`` calls the Python API, so it |
| returns exactly what ``model(...)`` returns. Importing this module has no side effects beyond importing fastapi and |
| pydantic (the image's ``verify:`` imports it without a device); the environment is read in the lifespan only. |
| |
| METEOR's request adds one top-level field, ``ego_speed`` (m/s, the graph's ``v0``), decoded into the call kwargs |
| (``METEOR.EXTRA_INPUTS``). ``images`` must hold all eight cameras with their calibration (``ttaw.io.decode_cameras`` |
| with ``CAMERA_ORDER``); an absent narrow camera is sent as an all-zero image (METEOR's data convention: the host |
| replaces its calibration by the donor's). Host tests swap the model before starting the app: |
| ``app.state.ttaw.model_factory = Stub``; ``app.state.ttaw.predict(request)`` is the route handler itself. |
| """ |
| from pathlib import Path |
| from typing import Any, Dict, Optional |
|
|
| from .. import __version__ |
| from ..api import METEOR |
| from ..ttaw.server.app import PredictRequest, ServerSpec, create_app, parse_mesh_shape |
|
|
| __all__ = ["SPEC", "app", "PredictRequest", "MeteorRequest", "parse_mesh_shape"] |
|
|
|
|
| class MeteorRequest(PredictRequest): |
| """``POST /predict`` of METEOR: the common envelope + ``ego_speed`` (m/s).""" |
|
|
| ego_speed: Optional[float] = None |
|
|
|
|
| def decode_extra(req: MeteorRequest, kwargs: Dict[str, Any], max_bytes: int) -> None: |
| if req.ego_speed is not None: |
| kwargs["ego_speed"] = float(req.ego_speed) |
|
|
|
|
| SPEC = ServerSpec( |
| model_name=METEOR.MODEL_NAME, |
| env_prefix=METEOR.ENV_PREFIX, |
| model_cls=METEOR, |
| task="multi-camera multi-task driving perception and end-to-end planning", |
| default_weights=METEOR.DEFAULT_REPO, |
| owner="changh95", |
| io="eight surround-view camera images (768x432 RGB, raw and unrectified) with intrinsics, camera extrinsics and the ego speed in; a BEV lane map, 3D boxes (vehicle / VRU) with stationary flags and agent futures, per-camera 2D boxes, 2D segmentation and depth, 3D occupancy, the ego-relevant traffic light, a risk field and three ego paths with steer / accel / brake out", |
| autoware={"package": "none (METEOR runtime: tier4/METEOR deploy/cpp)", |
| "path": "deploy/cpp", |
| "autoware_universe": "dc193a81e9959de57b751e269127ae9b67e3f300"}, |
| source={"repo": "https://huggingface.co/changh95/meteor-p150", "license": "Apache-2.0"}, |
| calib_dir=Path(__file__).resolve().parents[1] / "calib", |
| version=__version__, |
| description="METEOR (TIER IV, AutowareFoundation/meteor) on one Tenstorrent Blackhole p150. " |
| "Not an OpenAI-compatible API.", |
| request_model=MeteorRequest, |
| decode_extra=decode_extra, |
| ) |
|
|
| app = create_app(SPEC) |
|
|
