ai-agent-book 精选快照（<2MB 代码与文档，来自 github.com/bojieli/ai-agent-book）

chapter6/robotics_lab_common.py

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+"""Small, deterministic helpers shared by the chapter 9 robotics labs.
+
+The labs deliberately avoid pretending that a Mac MPS run is a CUDA/ManiSkill
+run.  They expose the accelerator used in the evidence and fail closed when a
+caller asks for an accelerator that is not available.
+"""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import os
+import random
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+
+def seed_everything(seed: int) -> None:
+    """Seed every local RNG used by the self-contained experiments."""
+
+    os.environ["PYTHONHASHSEED"] = str(seed)
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+
+
+def select_device(require_accelerator: bool = True) -> torch.device:
+    """Prefer CUDA, then Apple MPS, and optionally reject CPU fallback."""
+
+    if torch.cuda.is_available():
+        return torch.device("cuda")
+    if getattr(torch.backends, "mps", None) is not None and torch.backends.mps.is_available():
+        return torch.device("mps")
+    if require_accelerator:
+        raise RuntimeError("no local GPU accelerator is available (expected CUDA or Apple MPS)")
+    return torch.device("cpu")
+
+
+def device_info(device: torch.device) -> dict[str, Any]:
+    info: dict[str, Any] = {"device": str(device), "torch": torch.__version__}
+    if device.type == "cuda":
+        info["name"] = torch.cuda.get_device_name(device)
+        info["capability"] = list(torch.cuda.get_device_capability(device))
+    elif device.type == "mps":
+        info["name"] = "Apple Metal Performance Shaders"
+    else:
+        info["name"] = "CPU"
+    return info
+
+
+def sha256(path: Path) -> str:
+    digest = hashlib.sha256()
+    with path.open("rb") as handle:
+        for chunk in iter(lambda: handle.read(1024 * 1024), b""):
+            digest.update(chunk)
+    return digest.hexdigest()
+
+
+def write_json(path: Path, value: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(value, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+
+def relative_or_absolute(path: Path, root: Path) -> str:
+    try:
+        return str(path.resolve().relative_to(root.resolve()))
+    except ValueError:
+        return str(path.resolve())
+