diff --git a/examples/python/modal-mock-inference/.env.example b/examples/python/modal-mock-inference/.env.example new file mode 100644 index 0000000..0464ac9 --- /dev/null +++ b/examples/python/modal-mock-inference/.env.example @@ -0,0 +1,8 @@ +# Copy to `.env` and fill in your LiveKit credentials. +# +# The policy runs on Modal and dials OUT to LiveKit, so this must be a +# publicly reachable server, LiveKit Cloud or your own hosted instance. +# A localhost devserver won't work: Modal can't reach it. +LIVEKIT_URL=wss://your-project.livekit.cloud +LIVEKIT_API_KEY=API... +LIVEKIT_API_SECRET=... diff --git a/examples/python/modal-mock-inference/README.md b/examples/python/modal-mock-inference/README.md new file mode 100644 index 0000000..d13fc58 --- /dev/null +++ b/examples/python/modal-mock-inference/README.md @@ -0,0 +1,212 @@ +# Modal mock inference + +In this tutorial we walk you through running a mock policy with `livekit-portal` +on [Modal](https://modal.com). The example creates synthetic video frames and +measures round-trip glass-to-glass latency, the full loop from a pixel leaving +the robot to the matching action coming back. + +The policy is a mock. It reads a QR code instead of running a real model, so the +tutorial stays focused on how to run a policy on Modal. When you have a real +policy, it drops straight in (see [step 6](#step-6-swap-in-a-real-policy)). + +> **This is a demo, read the numbers with that in mind.** Two things are not +> production-grade on purpose: +> - **QR over H264.** H264 is lossy, so it can blur the QR and some frames fail +> to decode and show up as `dropped`. Pushing a QR through a lossy video codec +> is a fun way to see the pipeline, not a robust data channel. If you need data +> to arrive pixel-exact, use a byte-stream codec (`codec: png` or `mjpeg` in +> `portal.yaml`). +> - **Latency is mostly distance.** The loop crosses the network twice +> (robot to policy and back), so a robot and a policy in different regions is +> inherently a few hundred ms. The `rtt` field is the pure network round trip +> for comparison. For low latency, run the policy near the robot. + +## The idea in one picture + +```mermaid +flowchart LR + subgraph robot["robot.py (your machine)"] + direction TB + paint["paint (seq, capture_us)
into a frame as a QR code"] + measure["latency = now - capture_us"] + end + + subgraph modal["policy.py (Modal cloud)"] + direction TB + decode["decode the QR"] + echo["echo the token back"] + decode --> echo + end + + paint -->|"H264 video"| decode + paint -->|"state"| decode + echo -->|"action"| measure +``` + +The timestamp is painted into the picture, so it travels through the real +video encoder and jitter buffer. That is what makes the number glass-to-glass +and not just a network ping. + +## What you need + +- A [LiveKit Cloud](https://cloud.livekit.io) project (free tier is fine). The + policy runs in the cloud and dials out, so `localhost` will not work. +- Python 3.12 and [`uv`](https://docs.astral.sh/uv/). +- A [Modal](https://modal.com) account. + +--- + +## Step 1: Install everything + +`uv sync` reads `pyproject.toml` and installs Portal, Modal, and the rest into +a local `.venv`. + +```bash +uv sync +``` + +## Step 2: Connect the Modal CLI to your account + +This opens a browser once to link the CLI. You only do it a single time. + +```bash +uv run modal setup +``` + +## Step 3: Give the cloud policy its LiveKit credentials + +The policy runs on Modal, so it needs your LiveKit keys there. A **Modal +secret** is the standard way to hand credentials to a cloud function. Modal +injects each `KEY=VALUE` into the policy as an environment variable. + +Grab your URL and keys from the LiveKit Cloud dashboard, then: + +```bash +uv run modal secret create livekit-credentials \ + LIVEKIT_URL=wss://your-project.livekit.cloud \ + LIVEKIT_API_KEY=API... \ + LIVEKIT_API_SECRET=... +``` + +The name `livekit-credentials` is what `policy.py` looks up. Keep it as is. + +Check it later with `uv run modal secret list`. Delete it with +`uv run modal secret delete livekit-credentials`. + +## Step 4: Point the local robot at the same project + +The robot reads its credentials from a local `.env` file. Copy the template +and fill in the **same** URL and keys you put in the secret. + +```bash +cp .env.example .env +``` + +Both sides default to the room `portal-modal-mock`, so they will find each +other automatically. + +## Step 5: Run it + +Start the policy on Modal. The first run builds the image, which takes a +minute. After that it connects and waits for a robot. + +```bash +uv run modal run policy.py +``` + +In a second terminal, start the robot. + +```bash +uv run robot.py +``` + +They meet in the LiveKit room and the robot starts printing latency. + +--- + +## Reading the output + +The policy prints how many frames it decoded: + +``` +[policy] decoded=142 missed=1 decode_rate=99% +``` + +The robot prints the measurements once a second: + +``` +[robot] t= 4s sent=75 replies=71 dropped=4 glass2glass=88.40ms codec_lag=12.10ms e2e=91.20ms/128.6ms active=policy +``` + +What each field means: + +- **`sent`** frames the robot has published. +- **`replies`** decoded tokens that came back from the policy. +- **`dropped`** frames that never made the round trip. A `seq` gap means the + frame was lost in the video pipe, its QR was too degraded to read, or the + policy skipped it. The policy keeps only the freshest observation (a small + `deque`), so if decoding cannot keep up it drops the backlog and acts on the + newest frame. That keeps latency near the network round trip instead of + letting a queue build, which is the whole point of running a policy on Portal. +- **`glass2glass`** the full video round trip, `now - capture_us`. This is the + headline number. +- **`codec_lag`** how much later the camera frame arrived than the joint state + for the same tick, measured on the policy. Portal pairs a frame and a state + by the capture time the robot stamped, so the video's extra delay does not + show up as a timestamp gap. It shows up as the observation firing later than + the raw state. That gap is the one-way cost of pushing pixels through the + video codec and jitter buffer, on top of the plain data channel. +- **`e2e`** the same glass-to-glass latency, aggregated by Portal as + `metrics.policy.e2e_us_p50/p95`. Watch the p95: control cares about the tail, + not the average. + +Latency depends on the distance to the Modal region and your uplink. A +cross-continent run will show a larger `e2e`. + +## Step 6: Swap in a real policy + +The mock is CPU-only. To run a real model, change two things and leave the +operator loop alone. + +**1. Give the Modal function a GPU** in `policy.py` and install your model: + +```python +image = image.pip_install("lerobot", "torch") # your stack + +@app.function(image=image, secrets=[...], gpu="A10G", timeout=3600) +def run_on_modal(): + ... +``` + +**2. Replace the `Policy` class** in `policy.py`. It already has the LeRobot +call shape: + +```python +class Policy: + def __init__(self): + self.model = ACTPolicy.from_pretrained("your-org/your-checkpoint") + + def get_action(self, obs): + return self.model.select_action(obs) # your real forward pass +``` + +For getting a checkpoint onto the GPU, see the +[LeRobot plugin docs](../../../docs/10-lerobot.md). + +## The wire contract + +The shared schema (video track, state and action fields, fps) lives in +`portal.yaml`. Both sides load it with `RobotConfig.from_yaml_file` and +`OperatorConfig.from_yaml_file`, so there is one place to edit and no chance of +the two drifting apart. To switch the camera to a lossless codec, for example, +change `codec: h264` to `codec: png` there. + +## Knobs + +Frame size and run length are constants at the top of `robot.py`: `WIDTH`, +`HEIGHT`, `DURATION_S`, and `FPS` (keep `FPS` matching `fps` in `portal.yaml`). + +If `dropped` climbs or `decode_rate` falls, the QR is getting mangled before +the plain video would. Raise `WIDTH`/`HEIGHT`, or set `codec: png` in +`portal.yaml` for a lossless comparison. See +[frame video](../../../docs/05-frame-video.md). diff --git a/examples/python/modal-mock-inference/policy.py b/examples/python/modal-mock-inference/policy.py new file mode 100644 index 0000000..bc9b164 --- /dev/null +++ b/examples/python/modal-mock-inference/policy.py @@ -0,0 +1,199 @@ +"""Policy side. Runs on Modal. + +A normal inference operator: connect, claim control, and on every observation +call `policy.get_action(obs)` and publish the result. The only twist is that +our policy reads a QR code instead of running a model, so the tutorial is about +running a policy on Modal, not about weights. + +The `Policy` class matches a LeRobot policy's call shape: + + policy = ACTPolicy.from_pretrained("your-org/your-checkpoint") + action = policy.select_action(observation) + +Swap `Policy` for a real one and the loop below does not change. + + uv run modal run policy.py # on Modal + uv run python policy.py # locally, for a quick check +""" +from __future__ import annotations + +import asyncio +import datetime +import os +import pathlib +import time +from collections import deque +from typing import Optional + +import modal +from livekit import api +from livekit.portal import ( + Observation, Operator, OperatorConfig, State, frame_bytes_to_numpy_rgb, +) + +TRACK = "cam1" +ROOM = "portal-modal-mock" +DURATION_S = 30 + + +def config_path() -> pathlib.Path: + """portal.yaml, next to this file locally or mounted at /root on Modal.""" + here = pathlib.Path(__file__).parent / "portal.yaml" + return here if here.exists() else pathlib.Path("/root/portal.yaml") + + +def connect(identity: str): + """Mint a room token from the LiveKit creds in the environment. + + On Modal the secret sets LIVEKIT_URL / KEY / SECRET as env vars. For a + local run, export them first (for example: `set -a; . .env; set +a`). + """ + grants = api.VideoGrants( + room_join=True, room=ROOM, can_publish=True, + can_subscribe=True, can_update_own_metadata=True, + ) + token = ( + api.AccessToken(os.environ["LIVEKIT_API_KEY"], os.environ["LIVEKIT_API_SECRET"]) + .with_identity(identity).with_grants(grants) + .with_ttl(datetime.timedelta(hours=1)).to_jwt() + ) + return os.environ["LIVEKIT_URL"], token + + +class Policy: + """Stand-in for a LeRobot policy. It reads the QR clock out of the frame.""" + + def __init__(self) -> None: + import cv2 + + self._cv2 = cv2 + self._detector = cv2.QRCodeDetector() + + def get_action(self, obs: Observation) -> Optional[dict]: + """The decoded token, or None if the QR was unreadable this frame.""" + frame = obs.frames.get(TRACK) + if frame is None: + return None + rgb = frame_bytes_to_numpy_rgb(bytes(frame.data), frame.width, frame.height) + gray = self._cv2.cvtColor(rgb, self._cv2.COLOR_RGB2GRAY) + payload, _points, _straight = self._detector.detectAndDecode(gray) + if not payload.startswith("p1:"): + return None + seq, capture_us = payload[3:].split(",") + return {"seq": float(seq), "t_capture_us": float(capture_us)} + + +async def main() -> None: + url, token = connect("policy") + + cfg = OperatorConfig.from_yaml_file(config_path(), ROOM) + op = Operator(cfg) + policy = Policy() + hits = misses = 0 + + # When each raw state packet arrived, by seq, on our own clock. + state_arrival: dict[int, float] = {} + + # Only ever hold the freshest observations. If decoding cannot keep up with + # the frame rate, we drop the backlog and act on the newest frame instead of + # working through stale ones. Acting on a stale observation is what would + # defeat the point of Portal, and it shows up as latency that climbs far + # above the network round trip. + latest: deque = deque(maxlen=2) + got_obs = asyncio.Event() + + def on_state(state: State) -> None: + state_arrival[int(state.values["seq"])] = time.monotonic() + + def on_observation(obs: Observation) -> None: + # Measure codec lag here, at arrival, so a decode backlog cannot distort + # it. Portal pairs the frame and state on the capture time the robot + # stamped, so the video's extra delay is not a timestamp gap. It is the + # observation firing later than the state, once the frame lands. That gap + # (obs arrival minus state arrival) is the one-way video-over-data cost. + seq = int(obs.state["seq"]) + arrived = state_arrival.pop(seq, None) + lag_us = int((time.monotonic() - arrived) * 1_000_000) if arrived is not None else 0 + latest.append((obs, lag_us)) + got_obs.set() + + op.on_state(on_state) + op.on_observation(on_observation) + + print(f"[policy] connecting to {url} in room '{ROOM}' ...") + await op.connect(url, token) + print("[policy] connected, waiting for the robot to join ...") + + # Claim control, or the robot's gate drops our actions. set_active_operator + # RPCs the robot, so the robot must already be in the room. On Modal the + # policy usually starts first, so wait for the robot to appear. + while op.robot_identity() is None: + await asyncio.sleep(0.2) + await op.set_active_operator(op.local_identity()) + print(f"[policy] robot '{op.robot_identity()}' joined, controlling as '{op.local_identity()}'") + + stop_at = time.monotonic() + DURATION_S + 5.0 # outlast the robot a little + last_log = time.monotonic() + try: + while time.monotonic() < stop_at: + try: + await asyncio.wait_for(got_obs.wait(), timeout=1.0) + except asyncio.TimeoutError: + pass + got_obs.clear() + + if latest: + obs, lag_us = latest[-1] # freshest; any older frames are skipped + latest.clear() + action = policy.get_action(obs) + if action is None: + misses += 1 + else: + action["codec_lag_us"] = float(lag_us) + # in_reply_to_ts_us feeds Portal's built-in e2e metric the QR + # capture time, making that metric a glass-to-glass number. + op.send_action(action, in_reply_to_ts_us=int(action["t_capture_us"])) + hits += 1 + + now = time.monotonic() + if now - last_log >= 1.0: + total = hits + misses + rate = (hits / total * 100.0) if total else 0.0 + print(f"[policy] decoded={hits} missed={misses} decode_rate={rate:.0f}%") + last_log = now + finally: + print(f"[policy] decoded {hits} frames, disconnecting...") + await op.disconnect() + op.close() + + +# --- Deploy to Modal ---------------------------------------------------------- +# A Portal operator only dials out to LiveKit, so it drops straight onto Modal. +# livekit-portal ships on PyPI, so the image is a plain pip install. The two apt +# packages are OpenCV's shared libs. This mock is CPU-only; for a real model add +# gpu="A10G" here and pip-install your stack. +app = modal.App("portal-modal-mock") +image = ( + modal.Image.debian_slim(python_version="3.12") + .apt_install("libgl1", "libglib2.0-0") + .pip_install( + "livekit-portal", "livekit-api>=0.7", + "numpy>=1.24", "opencv-python-headless>=4.9", + ) + .add_local_python_source("policy") + .add_local_file(str(pathlib.Path(__file__).parent / "portal.yaml"), "/root/portal.yaml") +) + + +@app.function(image=image, secrets=[modal.Secret.from_name("livekit-credentials")], timeout=3600) +def run_on_modal(): + asyncio.run(main()) + + +@app.local_entrypoint() +def modal_entry(): + run_on_modal.remote() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/python/modal-mock-inference/portal.yaml b/examples/python/modal-mock-inference/portal.yaml new file mode 100644 index 0000000..1c33ee3 --- /dev/null +++ b/examples/python/modal-mock-inference/portal.yaml @@ -0,0 +1,18 @@ +# Wire contract shared by robot.py and policy.py, loaded with +# RobotConfig.from_yaml_file / OperatorConfig.from_yaml_file. It holds only the +# shareable schema, no identity or secrets: the room and token are set in code. + +version: 1 + +fps: 15 + +videos: + - { name: cam1, codec: h264 } # the real WebRTC video path + +state: + - { name: seq, dtype: f64 } + +action: + - { name: seq, dtype: f64 } + - { name: t_capture_us, dtype: f64 } # capture time, carried through the QR + - { name: codec_lag_us, dtype: f64 } # how much later video arrived than state diff --git a/examples/python/modal-mock-inference/pyproject.toml b/examples/python/modal-mock-inference/pyproject.toml new file mode 100644 index 0000000..d4f29f1 --- /dev/null +++ b/examples/python/modal-mock-inference/pyproject.toml @@ -0,0 +1,21 @@ +[project] +name = "livekit-portal-modal-mock-inference-example" +version = "0.1.0" +description = "Run a Portal operator on Modal: a robot streams synthetic QR video, a cloud policy decodes it and closes a glass-to-glass latency loop" +requires-python = ">=3.12" +dependencies = [ + "livekit-portal", + "livekit-api>=0.7", + "python-dotenv>=1", + "numpy>=1.24", + # robot: render QR codes into frames + "qrcode>=7", + "pillow>=10", + # policy: decode QR codes out of frames + "opencv-python-headless>=4.9", + # deploy: run the policy on Modal + "modal>=0.64", +] + +[tool.uv.sources] +livekit-portal = { path = "../../../python/packages/livekit-portal", editable = true } diff --git a/examples/python/modal-mock-inference/robot.py b/examples/python/modal-mock-inference/robot.py new file mode 100644 index 0000000..68c647e --- /dev/null +++ b/examples/python/modal-mock-inference/robot.py @@ -0,0 +1,175 @@ +"""Robot side. Runs on your machine. + +Each tick we build a frame, paint a (seq, capture_us) token into it as a QR +code, and send it over H264 with a matching state sample. The policy on Modal +reads the QR back and sends the token straight back as an action. We stamped +capture_us on our own clock, so `now - capture_us` is the round-trip +glass-to-glass latency. + + cp .env.example .env + uv sync + uv run robot.py # this file, here + uv run modal run policy.py # the policy, in another terminal +""" +from __future__ import annotations + +import asyncio +import datetime +import os +import pathlib +import time +from typing import Optional + +import numpy as np +from dotenv import load_dotenv +from livekit import api +from livekit.portal import Action, Robot, RobotConfig + +TRACK = "cam1" +ROOM = "portal-modal-mock" +FPS = 15 # keep in sync with fps in portal.yaml +WIDTH, HEIGHT = 640, 480 +DURATION_S = 30 + +# The wire contract (video track, state and action schemas, fps) lives in +# portal.yaml, shared with policy.py. +CONFIG = pathlib.Path(__file__).parent / "portal.yaml" + + +def connect(identity: str): + """Read creds from .env (or the Modal secret) and mint a room token.""" + load_dotenv(pathlib.Path(__file__).parent / ".env") # no-op if the file is absent + grants = api.VideoGrants( + room_join=True, room=ROOM, can_publish=True, + can_subscribe=True, can_update_own_metadata=True, + ) + token = ( + api.AccessToken(os.environ["LIVEKIT_API_KEY"], os.environ["LIVEKIT_API_SECRET"]) + .with_identity(identity).with_grants(grants) + .with_ttl(datetime.timedelta(hours=1)).to_jwt() + ) + return os.environ["LIVEKIT_URL"], token + + +def make_frame(seq: int, capture_us: int) -> np.ndarray: + """A grey frame with a QR code of `p1:seq,capture_us` in the middle. The + grey drifts a little so the stream looks alive.""" + import qrcode + from PIL import Image + + qr = qrcode.QRCode(error_correction=qrcode.constants.ERROR_CORRECT_Q, box_size=10, border=2) + qr.add_data(f"p1:{seq},{capture_us}") + qr.make(fit=True) + img = qr.make_image(fill_color="black", back_color="white").convert("RGB") + px = min(WIDTH, HEIGHT) // 2 + code = np.asarray(img.resize((px, px), Image.NEAREST), dtype=np.uint8) + + frame = np.full((HEIGHT, WIDTH, 3), 100 + seq % 40, dtype=np.uint8) + top, left = (HEIGHT - px) // 2, (WIDTH - px) // 2 + frame[top:top + px, left:left + px] = code + return frame + + +def fmt_us(value) -> str: + if not value: + return "-" + return f"{value}us" if value < 1000 else f"{value / 1000:.1f}ms" + + +class Stats: + """Latency and loss, all measured on the robot's own clock.""" + + def __init__(self) -> None: + self.replies = 0 + self.highest_seq = -1 + self.glass_us: Optional[int] = None # full video round trip + self.codec_us: Optional[int] = None # extra time the video path took + + def record(self, seq: int, glass_us: int, codec_us: int) -> None: + self.replies += 1 + self.highest_seq = max(self.highest_seq, seq) + self.glass_us = glass_us + self.codec_us = codec_us + + @property + def dropped(self) -> int: + """Frames we sent, up to the newest reply, that never came back.""" + return max(0, self.highest_seq + 1 - self.replies) + + +async def main() -> None: + url, token = connect("robot") + + cfg = RobotConfig.from_yaml_file(CONFIG, ROOM) + robot = Robot(cfg) + stats = Stats() + + def on_action(action: Action) -> None: + v = action.values + glass_us = int(time.time() * 1_000_000) - int(v["t_capture_us"]) + stats.record(int(v["seq"]), glass_us, int(v["codec_lag_us"])) + + robot.on_action(on_action) + + print(f"[robot] connecting to {url} in room '{ROOM}' ...") + await robot.connect(url, token) + + # Wait for a policy to take control before streaming, so we do not stream + # into an empty room and count those early frames as dropped. + print("[robot] connected, waiting for a policy to take control ...") + while robot.active_operator() is None: + await asyncio.sleep(0.2) + print(f"[robot] '{robot.active_operator()}' in control, streaming " + f"{WIDTH}x{HEIGHT} H264 at {FPS} fps for {DURATION_S}s") + + interval = 1.0 / FPS + start = time.monotonic() + next_tick = start + last_log = start + + try: + for seq in range(DURATION_S * FPS): + capture_us = int(time.time() * 1_000_000) + frame = make_frame(seq, capture_us) + # Same timestamp on the frame and the state so they fuse into one + # observation on the policy side. + robot.send_video_frame(TRACK, frame, timestamp_us=capture_us) + robot.send_state({"seq": float(seq)}, timestamp_us=capture_us) + + now = time.monotonic() + if now - last_log >= 1.0: + m = robot.metrics() + print( + f"[robot] t={int(now - start):>2}s sent={seq + 1} " + f"replies={stats.replies} dropped={stats.dropped} " + f"glass2glass={fmt_us(stats.glass_us)} codec_lag={fmt_us(stats.codec_us)} " + f"e2e={fmt_us(m.policy.e2e_us_p50)}/{fmt_us(m.policy.e2e_us_p95)} " + f"rtt={fmt_us(m.rtt.rtt_us_last)} active={robot.active_operator()}" + ) + last_log = now + + next_tick += interval + sleep_for = next_tick - time.monotonic() + if sleep_for > 0: + await asyncio.sleep(sleep_for) + + # Let the last in-flight replies land before the final tally, or they + # would look like dropped frames. + await asyncio.sleep(1.0) + + m = robot.metrics() + print() + print("[robot] final:") + print(f" glass-to-glass p50/p95: {fmt_us(m.policy.e2e_us_p50)} / {fmt_us(m.policy.e2e_us_p95)}") + print(f" network rtt (last): {fmt_us(m.rtt.rtt_us_last)}") + print(f" codec lag (last): {fmt_us(stats.codec_us)}") + print(f" frames sent: {DURATION_S * FPS}") + print(f" replies / dropped: {stats.replies} / {stats.dropped}") + finally: + print("[robot] disconnecting...") + await robot.disconnect() + robot.close() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git 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