Add Wall-X serving and Turtle2 TCP WebSocket bridge

scripts/run_serving.sh

@@ -226,6 +226,42 @@ if [[ -z "${CHECKPOINT_PATH}" ]]; then
     exit 2
 fi
 
+# The CLI action horizon overrides the checkpoint config. Reject mismatches
+# before loading a large model or handing an incompatible packet to Turtle2.
+if [[ -z "${TRAIN_CONFIG_PATH}" && -f "${CHECKPOINT_PATH%/}/config.yml" ]]; then
+    TRAIN_CONFIG_PATH="${CHECKPOINT_PATH%/}/config.yml"
+fi
+if [[ -n "${TRAIN_CONFIG_PATH}" ]]; then
+    if ! "${PYTHON_BIN}" - "${TRAIN_CONFIG_PATH}" "${ACTION_HORIZON}" <<'PY'
+import sys
+import yaml
+
+path, requested = sys.argv[1], int(sys.argv[2])
+try:
+    with open(path, encoding="utf-8") as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader) or {}
+except (OSError, yaml.YAMLError) as exc:
+    raise SystemExit(f"error: cannot read training config {path}: {exc}")
+
+task = config.get("task") or {}
+data = config.get("data") or {}
+configured = (
+    task.get("action_horizon_flow")
+    or task.get("action_horizon")
+    or data.get("action_horizon_flow")
+    or data.get("action_horizon")
+)
+if configured is not None and requested != int(configured):
+    raise SystemExit(
+        f"error: action horizon {requested} disagrees with training config "
+        f"{path}: {configured}; use --action-horizon {configured}"
+    )
+PY
+    then
+        exit 2
+    fi
+fi
+
 export CUDA_VISIBLE_DEVICES="${CUDA_ID}"
 export ENABLE_FAST_PREPROCESS="${ENABLE_FAST_PREPROCESS:-true}"