From 0a7b21cdd028fa7d263186a74ed73186171d0439 Mon Sep 17 00:00:00 2001
From: Pepijn <pepijn@huggingface.co>
Date: Wed, 15 Jul 2026 14:05:50 +0200
Subject: [PATCH] refactor(train): remove wandb example tables

---
 src/lerobot/common/wandb_utils.py    | 85 ----------------------------
 src/lerobot/configs/default.py       |  3 -
 src/lerobot/scripts/lerobot_train.py | 17 ------
 3 files changed, 105 deletions(-)

diff --git a/src/lerobot/common/wandb_utils.py b/src/lerobot/common/wandb_utils.py
index a1175e85f..c229b5eaa 100644
--- a/src/lerobot/common/wandb_utils.py
+++ b/src/lerobot/common/wandb_utils.py
@@ -19,8 +19,6 @@ import re
 from glob import glob
 from pathlib import Path
 
-import numpy as np
-import torch
 from huggingface_hub.constants import SAFETENSORS_SINGLE_FILE
 from termcolor import colored
 
@@ -209,86 +207,3 @@ class WandBLogger:
 
         wandb_video = self._wandb.Video(video_path, fps=self.env_fps, format="mp4")
         self._wandb.log({f"{mode}/video": wandb_video}, step=step)
-
-    def log_training_examples(
-        self,
-        batch: dict,
-        step: int,
-        *,
-        camera_keys: list[str],
-        n_samples: int = 4,
-        mode: str = "train",
-    ) -> None:
-        """Log a small W&B table with sampled images/text and action endpoints."""
-        if mode not in {"train", "eval"}:
-            raise ValueError(mode)
-
-        # Batch size — first tensor-like value wins.
-        bsz = next(
-            (int(v.shape[0]) for v in batch.values() if hasattr(v, "shape") and v.ndim > 0),
-            None,
-        )
-        if not bsz:
-            return
-        n = min(int(n_samples), bsz)
-
-        present_cameras = [c for c in camera_keys if c in batch]
-        text_keys = [k for k in ("task", "subtask", "memory", "instruction") if k in batch]
-
-        columns = ["sample"]
-        columns.extend(c.removeprefix("observation.images.") or c for c in present_cameras)
-        columns.extend(text_keys)
-        columns += ["gt_action_first", "gt_action_last"]
-
-        table = self._wandb.Table(columns=columns)
-
-        def _to_uint8_hwc(t: torch.Tensor) -> np.ndarray:
-            if t.ndim == 4:
-                t = t[0]
-            if t.ndim == 3 and t.shape[0] in (1, 3, 4) and t.shape[-1] not in (1, 3, 4):
-                t = t.permute(1, 2, 0)
-            arr = t.detach().cpu().float().numpy()
-            if arr.size and float(arr.max()) <= 1.5:
-                arr = arr * 255.0
-            return np.clip(arr, 0, 255).astype(np.uint8)
-
-        def _action_endpoints(a: torch.Tensor) -> tuple[str, str]:
-            arr = a.detach().cpu().float().numpy()
-            if arr.ndim == 2:
-                return str(np.round(arr[0], 3).tolist()), str(np.round(arr[-1], 3).tolist())
-            if arr.ndim == 1:
-                rounded = np.round(arr, 3).tolist()
-                return str(rounded), str(rounded)
-            text = str(arr.tolist())
-            return text, text
-
-        for i in range(n):
-            row: list = [i]
-            for cam in present_cameras:
-                try:
-                    row.append(self._wandb.Image(_to_uint8_hwc(batch[cam][i])))
-                except Exception as exc:  # noqa: BLE001
-                    logging.warning(
-                        "log_training_examples: camera %s sample %d failed (%s)",
-                        cam,
-                        i,
-                        exc,
-                    )
-                    row.append(None)
-            for tk in text_keys:
-                v = batch[tk]
-                if isinstance(v, list | tuple):
-                    row.append(str(v[i]) if i < len(v) else "")
-                else:
-                    row.append(str(v))
-            action = batch.get("action")
-            if isinstance(action, torch.Tensor) and action.ndim >= 1:
-                first, last = _action_endpoints(action[i])
-                row.append(first)
-                row.append(last)
-            else:
-                row.append("")
-                row.append("")
-            table.add_data(*row)
-
-        self._wandb.log({f"{mode}/examples": table}, step=step)
diff --git a/src/lerobot/configs/default.py b/src/lerobot/configs/default.py
index b51d1000e..38991a665 100644
--- a/src/lerobot/configs/default.py
+++ b/src/lerobot/configs/default.py
@@ -75,9 +75,6 @@ class WandBConfig:
     run_id: str | None = None
     mode: str | None = None  # Allowed values: 'online', 'offline' 'disabled'. Defaults to 'online'
     add_tags: bool = True  # If True, save configuration as tags in the WandB run.
-    # Periodic W&B table with sampled images/text and action endpoints. Set to 0 to disable.
-    log_examples_freq: int = 5000
-    log_examples_n: int = 4
 
 
 @dataclass
diff --git a/src/lerobot/scripts/lerobot_train.py b/src/lerobot/scripts/lerobot_train.py
index 34e7d2021..d9172b562 100644
--- a/src/lerobot/scripts/lerobot_train.py
+++ b/src/lerobot/scripts/lerobot_train.py
@@ -730,23 +730,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                 if wandb_logger:
                     wandb_logger.log_dict({"eval_loss": eval_loss}, step=step, mode="eval")
 
-        # Periodic W&B example table (camera images + text fields + action endpoints).
-        if (
-            wandb_logger is not None
-            and cfg.wandb.log_examples_freq > 0
-            and step % cfg.wandb.log_examples_freq == 0
-            and is_main_process
-        ):
-            try:
-                wandb_logger.log_training_examples(
-                    batch=batch,
-                    step=step,
-                    camera_keys=list(dataset.meta.camera_keys),
-                    n_samples=cfg.wandb.log_examples_n,
-                )
-            except Exception as exc:  # noqa: BLE001
-                logging.warning("wandb log_training_examples failed: %s", exc)
-
         if cfg.save_checkpoint and is_saving_step:
             # Under FSDP, gathering the full model + optimizer state dicts is a cross-rank collective,
             # so all ranks must participate; rank 0 then writes the materialized dicts. For DDP /