chore(dependencies): upgrade rerun (#2237)

* chore(dependencies): upgrade rerun

Co-authored-by: Ben Zhang <benzhangniu@gmail.com>

* test(utils): fix rerun scalars

---------

Co-authored-by: Ben Zhang <benzhangniu@gmail.com>

pyproject.toml

@@ -82,7 +82,7 @@ dependencies = [
 
     "draccus==0.10.0", # TODO: Remove ==
     "gymnasium>=1.0.0",
-    "rerun-sdk>=0.21.0,<0.23.0", # TODO: Bumb dependency
+    "rerun-sdk>=0.24.0,<0.27.0",
 
     # Support dependencies
     "deepdiff>=7.0.1,<9.0.0",

src/lerobot/scripts/lerobot_dataset_viz.py

@@ -141,15 +141,15 @@ def visualize_dataset(
     gc.collect()
 
     if mode == "distant":
-        rr.serve(open_browser=False, web_port=web_port, ws_port=ws_port)
+        rr.serve_web_viewer(open_browser=False, web_port=web_port)
 
     logging.info("Logging to Rerun")
 
     for batch in tqdm.tqdm(dataloader, total=len(dataloader)):
         # iterate over the batch
         for i in range(len(batch["index"])):
-            rr.set_time_sequence("frame_index", batch["frame_index"][i].item())
-            rr.set_time_seconds("timestamp", batch["timestamp"][i].item())
+            rr.set_time("frame_index", sequence=batch["frame_index"][i].item())
+            rr.set_time("timestamp", timestamp=batch["timestamp"][i].item())
 
             # display each camera image
             for key in dataset.meta.camera_keys:
@@ -159,21 +159,21 @@ def visualize_dataset(
             # display each dimension of action space (e.g. actuators command)
             if ACTION in batch:
                 for dim_idx, val in enumerate(batch[ACTION][i]):
-                    rr.log(f"{ACTION}/{dim_idx}", rr.Scalar(val.item()))
+                    rr.log(f"{ACTION}/{dim_idx}", rr.Scalars(val.item()))
 
             # display each dimension of observed state space (e.g. agent position in joint space)
             if OBS_STATE in batch:
                 for dim_idx, val in enumerate(batch[OBS_STATE][i]):
-                    rr.log(f"state/{dim_idx}", rr.Scalar(val.item()))
+                    rr.log(f"state/{dim_idx}", rr.Scalars(val.item()))
 
             if DONE in batch:
-                rr.log(DONE, rr.Scalar(batch[DONE][i].item()))
+                rr.log(DONE, rr.Scalars(batch[DONE][i].item()))
 
             if REWARD in batch:
-                rr.log(REWARD, rr.Scalar(batch[REWARD][i].item()))
+                rr.log(REWARD, rr.Scalars(batch[REWARD][i].item()))
 
             if "next.success" in batch:
-                rr.log("next.success", rr.Scalar(batch["next.success"][i].item()))
+                rr.log("next.success", rr.Scalars(batch["next.success"][i].item()))
 
     if mode == "local" and save:
         # save .rrd locally

src/lerobot/utils/visualization_utils.py

@@ -46,7 +46,7 @@ def log_rerun_data(
 
     This function iterates through the provided observation and action dictionaries and sends their contents
     to the Rerun viewer. It handles different data types appropriately:
-    - Scalar values (floats, ints) are logged as `rr.Scalar`.
+    - Scalars values (floats, ints) are logged as `rr.Scalars`.
     - 3D NumPy arrays that resemble images (e.g., with 1, 3, or 4 channels first) are transposed
       from CHW to HWC format and logged as `rr.Image`.
     - 1D NumPy arrays are logged as a series of individual scalars, with each element indexed.
@@ -65,7 +65,7 @@ def log_rerun_data(
             key = k if str(k).startswith(OBS_PREFIX) else f"{OBS_STR}.{k}"
 
             if _is_scalar(v):
-                rr.log(key, rr.Scalar(float(v)))
+                rr.log(key, rr.Scalars(float(v)))
             elif isinstance(v, np.ndarray):
                 arr = v
                 # Convert CHW -> HWC when needed
@@ -73,7 +73,7 @@ def log_rerun_data(
                     arr = np.transpose(arr, (1, 2, 0))
                 if arr.ndim == 1:
                     for i, vi in enumerate(arr):
-                        rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
                 else:
                     rr.log(key, rr.Image(arr), static=True)
 
@@ -84,13 +84,13 @@ def log_rerun_data(
             key = k if str(k).startswith("action.") else f"action.{k}"
 
             if _is_scalar(v):
-                rr.log(key, rr.Scalar(float(v)))
+                rr.log(key, rr.Scalars(float(v)))
             elif isinstance(v, np.ndarray):
                 if v.ndim == 1:
                     for i, vi in enumerate(v):
-                        rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))
                 else:
                     # Fall back to flattening higher-dimensional arrays
                     flat = v.flatten()
                     for i, vi in enumerate(flat):
-                        rr.log(f"{key}_{i}", rr.Scalar(float(vi)))
+                        rr.log(f"{key}_{i}", rr.Scalars(float(vi)))

tests/utils/test_visualization_utils.py

@@ -45,7 +45,7 @@ def mock_rerun(monkeypatch):
         calls.append((key, obj, kwargs))
 
     dummy_rr = SimpleNamespace(
-        Scalar=DummyScalar,
+        Scalars=DummyScalar,
         Image=DummyImage,
         log=dummy_log,
         init=lambda *a, **k: None,
@@ -109,9 +109,9 @@ def test_log_rerun_data_envtransition_scalars_and_image(mock_rerun):
     vu.log_rerun_data(observation=obs_data, action=action_data)
 
     # We expect:
-    # - observation.state.temperature -> Scalar
+    # - observation.state.temperature -> Scalars
     # - observation.camera -> Image (HWC) with static=True
-    # - action.throttle -> Scalar
+    # - action.throttle -> Scalars
     # - action.vector_0, action.vector_1 -> Scalars
     expected_keys = {
         f"{OBS_STATE}.temperature",