fix(eval): address review comments

- Wrap rollout loop in try/finally so finalize() runs on crash/interrupt
- Guard push_to_hub with num_episodes > 0 to avoid pushing empty datasets
- Hoist loop-invariant multi_env and base_repo_id out of creation loop

src/lerobot/scripts/lerobot_eval.py

@@ -216,10 +216,10 @@ def rollout(
         features = _env_features_to_dataset_features(env_features)
         fps = env.unwrapped.metadata.get("render_fps", 30)
         recording_datasets = []
-        for i in range(env.num_envs):
         multi_env = env.num_envs > 1
-            root = str(recording_dir / f"env_{i}") if multi_env else str(recording_dir)
         base_repo_id = recording_repo_id or "eval_recording"
+        for i in range(env.num_envs):
+            root = str(recording_dir / f"env_{i}") if multi_env else str(recording_dir)
             repo_id = f"{base_repo_id}_env_{i}" if multi_env else base_repo_id
             recording_datasets.append(
                 LeRobotDataset.create(
@@ -253,6 +253,7 @@ def rollout(
         leave=False,
     )
     check_env_attributes_and_types(env)
+    try:
         while not np.all(done) and step < max_steps:
             # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
             observation = preprocess_observation(observation)
@@ -303,7 +304,9 @@ def rollout(
             elif "is_success" in info:
                 is_success = info["is_success"]
                 successes = (
-                is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
+                    is_success.tolist()
+                    if hasattr(is_success, "tolist")
+                    else [bool(is_success)] * env.num_envs
                 )
             else:
                 successes = [False] * env.num_envs
@@ -347,6 +350,15 @@ def rollout(
             )
             progbar.set_postfix({"running_success_rate": f"{running_success_rate.item() * 100:.1f}%"})
             progbar.update()
+    finally:
+        if recording_datasets is not None:
+            for ds in recording_datasets:
+                ds.finalize()
+                if recording_repo_id is not None:
+                    if ds.num_episodes > 0:
+                        ds.push_to_hub(private=recording_private)
+                    else:
+                        logging.warning("No episodes recorded for %s — skipping push to hub.", ds.repo_id)
 
     # Track the final observation.
     if return_observations:
@@ -366,12 +378,6 @@ def rollout(
             stacked_observations[key] = torch.stack([obs[key] for obs in all_observations], dim=1)
         ret[OBS_STR] = stacked_observations
 
-    if recording_datasets is not None:
-        for ds in recording_datasets:
-            ds.finalize()
-            if recording_repo_id is not None:
-                ds.push_to_hub(private=recording_private)
-
     if hasattr(policy, "use_original_modules"):
         policy.use_original_modules()