mirror of
https://github.com/huggingface/lerobot.git
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feat(eval): implement docker runtime with HTTP policy inference server
Add docker_runtime.py (host-side) and lerobot_eval_worker.py (container-side) for --eval.runtime=docker. Policy loads once on the host GPU; Docker containers run env-only workers that call back via HTTP for action chunks, maximising GPU utilisation across parallel benchmark tasks. - _InferenceServer: HTTP server wrapping predict_action_chunk with a single lock - run_eval_in_docker: spawns instance_count containers, collects + merges per-task JSON, writes eval_info.json compatible with _aggregate_eval_from_per_task - lerobot-eval-worker CLI: make_env → shard tasks → run episodes → write JSON - EvalDockerConfig: add port field (default 50051) - pyproject.toml: add lerobot-eval-worker entry point Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Pepijn
@@ -247,6 +247,7 @@ lerobot-replay="lerobot.scripts.lerobot_replay:main"
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lerobot-setup-motors="lerobot.scripts.lerobot_setup_motors:main"
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lerobot-teleoperate="lerobot.scripts.lerobot_teleoperate:main"
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lerobot-eval="lerobot.scripts.lerobot_eval:main"
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lerobot-eval-worker="lerobot.scripts.lerobot_eval_worker:main"
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lerobot-train="lerobot.scripts.lerobot_train:main"
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lerobot-train-tokenizer="lerobot.scripts.lerobot_train_tokenizer:main"
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lerobot-dataset-viz="lerobot.scripts.lerobot_dataset_viz:main"
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@@ -64,6 +64,8 @@ class EvalDockerConfig:
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gpus: str | None = "all"
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# Docker --shm-size value (increase when using larger eval.batch_size values).
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shm_size: str = "8g"
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# Port on which the host HTTP policy inference server listens.
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port: int = 50051
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@dataclass
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@@ -0,0 +1,312 @@
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Docker runtime for lerobot-eval.
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The policy stays on the host GPU; gym environments run inside Docker containers.
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Each container runs `lerobot-eval-worker`, which calls back to a host HTTP inference
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server for action chunks.
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Architecture:
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host (GPU):
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1. Load policy + preprocessors from EvalPipelineConfig.
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2. Start HTTP policy-inference server (one lock — serialises GPU calls).
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3. Spawn ``instance_count`` Docker containers (one per shard).
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4. Wait; collect per-task JSON written to the mounted output volume.
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5. Merge shards → aggregate → write eval_info.json.
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container (CPU only):
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1. make_env(cfg.env) → shard tasks by (instance_id, instance_count).
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2. For each task: run n_episodes, POST obs to /predict_chunk, step env.
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3. Write per-task JSON to /results/worker_{instance_id}.json.
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"""
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from __future__ import annotations
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import json
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import logging
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import pickle # nosec B403 — internal serialisation only
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import platform
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import subprocess # nosec B404
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import sys
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import threading
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import time
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from http.server import BaseHTTPRequestHandler, HTTPServer
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from pathlib import Path
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from typing import TYPE_CHECKING, Any
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import numpy as np
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import torch
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from lerobot.envs.factory import make_env_pre_post_processors
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from lerobot.policies.factory import make_policy, make_pre_post_processors
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from lerobot.utils.utils import get_safe_torch_device
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if TYPE_CHECKING:
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from lerobot.configs.eval import EvalPipelineConfig
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# HTTP inference server (host side)
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# ---------------------------------------------------------------------------
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class _PolicyInferenceHandler(BaseHTTPRequestHandler):
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"""POST /predict_chunk → pickled numpy action chunk."""
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server: _InferenceServer
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def do_POST(self) -> None:
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if self.path != "/predict_chunk":
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self.send_error(404)
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return
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length = int(self.headers["Content-Length"])
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body = self.rfile.read(length)
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payload: dict = pickle.loads(body) # nosec B301
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obs_t: dict = payload["obs_t"]
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with self.server._lock:
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chunk_np = self.server._predict(obs_t)
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resp = pickle.dumps(chunk_np) # nosec B301
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self.send_response(200)
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self.send_header("Content-Type", "application/octet-stream")
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self.send_header("Content-Length", str(len(resp)))
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self.end_headers()
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self.wfile.write(resp)
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def log_message(self, fmt: str, *args: Any) -> None: # noqa: ANN401
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pass # suppress per-request logs
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class _InferenceServer(HTTPServer):
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"""Wraps the loaded policy behind a trivial HTTP interface."""
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def __init__(
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self,
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addr: tuple[str, int],
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policy: Any,
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env_preprocessor: Any,
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preprocessor: Any,
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postprocessor: Any,
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) -> None:
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super().__init__(addr, _PolicyInferenceHandler)
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self._policy = policy
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self._env_preprocessor = env_preprocessor
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self._preprocessor = preprocessor
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self._postprocessor = postprocessor
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self._lock = threading.Lock()
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self._device = torch.device(str(policy.config.device))
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def _predict(self, obs_t: dict) -> np.ndarray:
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"""Apply full preprocessing pipeline and return (T, A) numpy chunk."""
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obs = self._env_preprocessor(obs_t)
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obs = self._preprocessor(obs)
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obs_gpu: dict = {k: v.to(self._device) if isinstance(v, torch.Tensor) else v for k, v in obs.items()}
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with torch.no_grad():
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chunk: torch.Tensor = self._policy.predict_action_chunk(obs_gpu) # (B, T, A)
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# Postprocessor expects (B, A); apply it treating each timestep as a batch element.
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# For linear transforms (unnormalize) this is identical to applying it to (B, T, A).
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batch, n_steps, action_dim = chunk.shape
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chunk_2d = chunk.reshape(batch * n_steps, action_dim) # (B*T, A)
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chunk_2d = self._postprocessor(chunk_2d) # (B*T, A)
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# Return only the first env's chunk — batch_size=1 per container.
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return chunk_2d[:n_steps].cpu().numpy() # (T, A)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _get_host_ip() -> str:
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"""Return the IP that containers can use to reach the host."""
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if platform.system() in ("Darwin", "Windows"):
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return "host.docker.internal"
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return "172.17.0.1" # Linux Docker bridge default gateway
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def _resolve_image(cfg: EvalPipelineConfig) -> str:
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"""Return the Docker image name to use for the env containers."""
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if cfg.eval.docker.image:
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return cfg.eval.docker.image
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return f"lerobot-benchmark-{cfg.env.type}"
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def _env_argv() -> list[str]:
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"""Extract --env.* args from sys.argv to forward verbatim to the worker."""
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return [arg for arg in sys.argv[1:] if arg.startswith("--env.")]
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def _spawn_container(
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*,
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image: str,
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instance_id: int,
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instance_count: int,
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server_address: str,
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n_episodes: int,
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seed: int,
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output_dir: Path,
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docker_cfg: Any,
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env_argv: list[str],
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) -> subprocess.Popen:
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output_dir.mkdir(parents=True, exist_ok=True)
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container_results = "/results"
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cmd: list[str] = ["docker", "run", "--rm"]
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if docker_cfg.gpus:
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cmd += [f"--gpus={docker_cfg.gpus}"]
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cmd += [f"--shm-size={docker_cfg.shm_size}"]
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cmd += ["-v", f"{output_dir.resolve()}:{container_results}"]
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# Allow containers on Linux to resolve host.docker.internal.
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cmd += ["--add-host=host.docker.internal:host-gateway"]
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cmd.append(image)
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cmd += [
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"lerobot-eval-worker",
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*env_argv,
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f"--server_address={server_address}",
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f"--n_episodes={n_episodes}",
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f"--seed={seed}",
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f"--instance_id={instance_id}",
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f"--instance_count={instance_count}",
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f"--output_path={container_results}/worker_{instance_id}.json",
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]
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logger.info(
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"Spawning container %d/%d: %s",
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instance_id + 1,
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instance_count,
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" ".join(cmd),
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)
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return subprocess.Popen(cmd) # nosec B603 B607
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# ---------------------------------------------------------------------------
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# Public entry point
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# ---------------------------------------------------------------------------
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def run_eval_in_docker(cfg: EvalPipelineConfig) -> None:
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"""Run eval with env in Docker containers and policy on the host GPU.
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Writes ``eval_info.json`` to ``cfg.output_dir``. Called by
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``lerobot_eval._run_eval_worker`` when ``eval.runtime == "docker"``.
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"""
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# Import here to avoid circular import at module level.
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from lerobot.scripts.lerobot_eval import _aggregate_eval_from_per_task
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start_t = time.time()
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output_dir = Path(cfg.output_dir)
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output_dir.mkdir(parents=True, exist_ok=True)
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docker_cfg = cfg.eval.docker
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# Optionally pull the image before starting.
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image = _resolve_image(cfg)
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if docker_cfg.pull:
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logger.info("Pulling Docker image: %s", image)
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subprocess.run(["docker", "pull", image], check=True) # nosec B603 B607
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# ── Load policy + all preprocessors on the host GPU ──────────────────
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device = get_safe_torch_device(cfg.policy.device, log=True)
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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policy = make_policy(cfg=cfg.policy, env_cfg=cfg.env, rename_map=cfg.rename_map)
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policy.eval()
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preprocessor_overrides: dict = {
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"device_processor": {"device": str(device)},
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"rename_observations_processor": {"rename_map": cfg.rename_map},
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}
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preprocessor, postprocessor = make_pre_post_processors(
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policy_cfg=cfg.policy,
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pretrained_path=cfg.policy.pretrained_path,
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preprocessor_overrides=preprocessor_overrides,
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)
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env_preprocessor, _env_postprocessor = make_env_pre_post_processors(
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env_cfg=cfg.env,
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policy_cfg=cfg.policy,
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)
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# ── Start HTTP inference server ───────────────────────────────────────
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port = docker_cfg.port
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server = _InferenceServer(
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("0.0.0.0", port), # nosec B104 — only alive for the duration of eval
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policy=policy,
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env_preprocessor=env_preprocessor,
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preprocessor=preprocessor,
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postprocessor=postprocessor,
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)
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server_thread = threading.Thread(target=server.serve_forever, daemon=True)
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server_thread.start()
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logger.info("Policy inference server running on port %d", port)
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host_ip = _get_host_ip()
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server_address = f"{host_ip}:{port}"
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instance_count = cfg.eval.instance_count
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env_argv = _env_argv()
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# ── Spawn containers ──────────────────────────────────────────────────
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container_dirs: list[Path] = []
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procs: list[subprocess.Popen] = []
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try:
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for i in range(instance_count):
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shard_dir = output_dir / "shards" / str(i)
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container_dirs.append(shard_dir)
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proc = _spawn_container(
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image=image,
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instance_id=i,
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instance_count=instance_count,
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server_address=server_address,
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n_episodes=cfg.eval.n_episodes,
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seed=cfg.seed,
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output_dir=shard_dir,
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docker_cfg=docker_cfg,
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env_argv=env_argv,
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)
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procs.append(proc)
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failed: list[tuple[int, int]] = []
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for i, proc in enumerate(procs):
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rc = proc.wait()
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if rc != 0:
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failed.append((i, rc))
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logger.error("Container %d/%d exited with code %d", i + 1, instance_count, rc)
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if failed:
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raise RuntimeError(f"Docker eval containers failed (instance_id, exit_code): {failed}")
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finally:
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server.shutdown()
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# ── Collect and merge per-task results ───────────────────────────────
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per_task: list[dict] = []
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for i, shard_dir in enumerate(container_dirs):
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result_file = shard_dir / f"worker_{i}.json"
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with open(result_file) as f:
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shard_data: dict = json.load(f)
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per_task.extend(shard_data.get("per_task", []))
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per_task.sort(key=lambda x: (x["task_group"], x["task_id"]))
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info = _aggregate_eval_from_per_task(per_task, total_eval_s=time.time() - start_t)
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with open(output_dir / "eval_info.json", "w") as f:
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json.dump(info, f, indent=2)
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logger.info("Docker eval complete. Results: %s/eval_info.json", output_dir)
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@@ -0,0 +1,192 @@
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
|
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# Licensed under the Apache License, Version 2.0 (the "License");
|
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# you may not use this file except in compliance with the License.
|
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# You may obtain a copy of the License at
|
||||
#
|
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# http://www.apache.org/licenses/LICENSE-2.0
|
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#
|
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# Unless required by applicable law or agreed to in writing, software
|
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# distributed under the License is distributed on an "AS IS" BASIS,
|
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
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# See the License for the specific language governing permissions and
|
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# limitations under the License.
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"""Docker eval worker — runs inside a benchmark container.
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Runs gym episodes for a sharded subset of the configured env's tasks, calling
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a remote HTTP policy inference server (running on the host GPU) for action chunks.
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Usage (normally invoked by docker_runtime.run_eval_in_docker, not directly):
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lerobot-eval-worker \\
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--env.type=libero_plus \\
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--server_address=host.docker.internal:50051 \\
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--n_episodes=5 \\
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--seed=1000 \\
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--instance_id=0 \\
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--instance_count=2 \\
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--output_path=/results/worker_0.json
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"""
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from __future__ import annotations
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import json
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import logging
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import pickle # nosec B403 — internal serialisation only
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import urllib.request
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from dataclasses import dataclass, field
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from pathlib import Path
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import numpy as np
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from lerobot import envs # noqa: F401 — registers all env subclasses
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from lerobot.configs import parser
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from lerobot.envs.configs import EnvConfig
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from lerobot.envs.factory import make_env
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from lerobot.envs.utils import add_envs_task, preprocess_observation
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from lerobot.utils.utils import init_logging
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logger = logging.getLogger(__name__)
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@dataclass
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class EvalWorkerConfig:
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env: EnvConfig
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# Address of the policy inference HTTP server on the host.
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server_address: str = "host.docker.internal:50051"
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# Number of episodes to run per task.
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n_episodes: int = 1
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# Starting random seed; episode i of a task uses seed + i.
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seed: int = 0
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# 0-indexed shard id for this worker.
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instance_id: int = 0
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# Total number of shards (workers).
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instance_count: int = 1
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# Path (inside the container) to write the JSON per-task results.
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output_path: Path = field(default_factory=lambda: Path("/results/worker.json"))
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# Timeout in seconds for each HTTP request to the policy server.
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server_timeout: float = 120.0
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def _call_server(server_address: str, obs_t: dict, timeout: float) -> np.ndarray:
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"""POST pickled obs to /predict_chunk, return numpy chunk (T, action_dim)."""
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body = pickle.dumps({"obs_t": obs_t}) # nosec B301
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req = urllib.request.Request(
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f"http://{server_address}/predict_chunk",
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data=body,
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method="POST",
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headers={"Content-Type": "application/octet-stream"},
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)
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with urllib.request.urlopen(req, timeout=timeout) as resp: # nosec B310
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return pickle.loads(resp.read()) # nosec B301
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def run_worker(cfg: EvalWorkerConfig) -> dict:
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"""Run cfg.n_episodes episodes per assigned task. Returns per-task results dict."""
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# Build envs: {task_group: {task_id: vec_env}}
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envs_dict = make_env(cfg.env, n_envs=1)
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# Flatten to list of (task_group, task_id, env)
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tasks = [
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(task_group, task_id, vec)
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for task_group, group in envs_dict.items()
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for task_id, vec in group.items()
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]
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# Shard: this worker handles tasks where index % instance_count == instance_id
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if cfg.instance_count > 1:
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total = len(tasks)
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tasks = [t for idx, t in enumerate(tasks) if idx % cfg.instance_count == cfg.instance_id]
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logger.info(
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"Shard %d/%d: %d/%d tasks assigned.",
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cfg.instance_id + 1,
|
||||
cfg.instance_count,
|
||||
len(tasks),
|
||||
total,
|
||||
)
|
||||
|
||||
per_task: list[dict] = []
|
||||
|
||||
for task_group, task_id, env in tasks:
|
||||
sum_rewards: list[float] = []
|
||||
max_rewards: list[float] = []
|
||||
successes: list[bool] = []
|
||||
|
||||
for ep_idx in range(cfg.n_episodes):
|
||||
obs, _info = env.reset(seed=[cfg.seed + ep_idx])
|
||||
obs_t = preprocess_observation(obs)
|
||||
obs_t = add_envs_task(env, obs_t)
|
||||
|
||||
action_buffer: list[np.ndarray] = [] # each element: (1, action_dim)
|
||||
ep_rewards: list[float] = []
|
||||
ep_success = False
|
||||
done = np.zeros(1, dtype=bool)
|
||||
|
||||
while not np.all(done):
|
||||
if not action_buffer:
|
||||
chunk_np = _call_server(cfg.server_address, obs_t, cfg.server_timeout)
|
||||
# chunk_np: (T, action_dim) — split into per-step slices of shape (1, action_dim)
|
||||
action_buffer = [chunk_np[i : i + 1] for i in range(chunk_np.shape[0])]
|
||||
|
||||
action_np = action_buffer.pop(0) # (1, action_dim)
|
||||
obs, reward, terminated, truncated, info = env.step(action_np)
|
||||
|
||||
done = terminated | truncated | done
|
||||
ep_rewards.append(float(np.mean(reward)))
|
||||
|
||||
if "final_info" in info:
|
||||
final_info = info["final_info"]
|
||||
if isinstance(final_info, dict) and "is_success" in final_info:
|
||||
ep_success = bool(final_info["is_success"][0])
|
||||
|
||||
if not np.all(done):
|
||||
obs_t = preprocess_observation(obs)
|
||||
obs_t = add_envs_task(env, obs_t)
|
||||
|
||||
sum_rewards.append(float(np.sum(ep_rewards)))
|
||||
max_rewards.append(float(np.max(ep_rewards)) if ep_rewards else 0.0)
|
||||
successes.append(ep_success)
|
||||
logger.info(
|
||||
"Task %s[%d] ep %d/%d — success=%s",
|
||||
task_group,
|
||||
task_id,
|
||||
ep_idx + 1,
|
||||
cfg.n_episodes,
|
||||
ep_success,
|
||||
)
|
||||
|
||||
per_task.append(
|
||||
{
|
||||
"task_group": task_group,
|
||||
"task_id": task_id,
|
||||
"metrics": {
|
||||
"sum_rewards": sum_rewards,
|
||||
"max_rewards": max_rewards,
|
||||
"successes": successes,
|
||||
"video_paths": [],
|
||||
},
|
||||
}
|
||||
)
|
||||
env.close()
|
||||
|
||||
return {"per_task": per_task}
|
||||
|
||||
|
||||
def worker_main(cfg: EvalWorkerConfig) -> None:
|
||||
results = run_worker(cfg)
|
||||
output = Path(cfg.output_path)
|
||||
output.parent.mkdir(parents=True, exist_ok=True)
|
||||
output.write_text(json.dumps(results, indent=2))
|
||||
logger.info("Worker %d wrote results to %s", cfg.instance_id, output)
|
||||
|
||||
|
||||
def main() -> None:
|
||||
init_logging()
|
||||
cfg = parser.parse(EvalWorkerConfig)
|
||||
worker_main(cfg)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user