diff --git a/examples/tutorial.ipynb b/examples/tutorial.ipynb
index 053c118a..12d278ce 100644
--- a/examples/tutorial.ipynb
+++ b/examples/tutorial.ipynb
@@ -66,7 +66,7 @@
         "import ultralytics\n",
         "ultralytics.checks()"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -102,7 +102,7 @@
         "# Run inference on an image with YOLOv8n\n",
         "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
       ],
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -169,7 +169,7 @@
         "# Validate YOLOv8n on COCO8 val\n",
         "!yolo val model=yolov8n.pt data=coco8.yaml"
       ],
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -215,6 +215,25 @@
         "Train YOLOv8 on [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/), [Classify](https://docs.ultralytics.com/tasks/classify/) and [Pose](https://docs.ultralytics.com/tasks/pose/) datasets. See [YOLOv8 Train Docs](https://docs.ultralytics.com/modes/train/) for more information."
       ]
     },
+    {
+      "cell_type": "code",
+      "source": [
+        "#@title Select YOLOv8 🚀 logger {run: 'auto'}\n",
+        "logger = 'Comet' #@param ['Comet', 'TensorBoard']\n",
+        "\n",
+        "if logger == 'Comet':\n",
+        "  %pip install -q comet_ml\n",
+        "  import comet_ml; comet_ml.init()\n",
+        "elif logger == 'TensorBoard':\n",
+        "  %load_ext tensorboard\n",
+        "  %tensorboard --logdir ."
+      ],
+      "metadata": {
+        "id": "ktegpM42AooT"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {
@@ -228,7 +247,7 @@
         "# Train YOLOv8n on COCO8 for 3 epochs\n",
         "!yolo train model=yolov8n.pt data=coco8.yaml epochs=3 imgsz=640"
       ],
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -280,8 +299,6 @@
             "\n",
             "      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size\n",
             "        1/3     0.761G     0.9273      3.155      1.291         32        640: 100% 1/1 [00:01<00:00,  1.23s/it]\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/optim/lr_scheduler.py:139: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
-            "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n",
             "                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100% 1/1 [00:00<00:00,  2.21it/s]\n",
             "                   all          4         17      0.613      0.899      0.888      0.621\n",
             "\n",
@@ -359,7 +376,7 @@
         "id": "CYIjW4igCjqD",
         "outputId": "2b65e381-717b-4a6f-d6f5-5254c867f3a4"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py
index 812d3d53..14f6410c 100644
--- a/ultralytics/engine/trainer.py
+++ b/ultralytics/engine/trainer.py
@@ -10,6 +10,7 @@ import math
 import os
 import subprocess
 import time
+import warnings
 from copy import deepcopy
 from datetime import datetime, timedelta
 from pathlib import Path
@@ -378,7 +379,9 @@ class BaseTrainer:
 
             self.lr = {f'lr/pg{ir}': x['lr'] for ir, x in enumerate(self.optimizer.param_groups)}  # for loggers
 
-            self.scheduler.step()
+            with warnings.catch_warnings():
+                warnings.simplefilter('ignore')  # suppress 'Detected lr_scheduler.step() before optimizer.step()'
+                self.scheduler.step()
             self.run_callbacks('on_train_epoch_end')
 
             if RANK in (-1, 0):
diff --git a/ultralytics/utils/callbacks/tensorboard.py b/ultralytics/utils/callbacks/tensorboard.py
index c542c9e8..b9a1704d 100644
--- a/ultralytics/utils/callbacks/tensorboard.py
+++ b/ultralytics/utils/callbacks/tensorboard.py
@@ -32,9 +32,9 @@ def _log_tensorboard_graph(trainer):
         imgsz = trainer.args.imgsz
         imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz
         p = next(trainer.model.parameters())  # for device, type
-        im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype)  # input (WARNING: must be zeros, not empty)
-        with warnings.catch_warnings(category=UserWarning):
-            warnings.simplefilter('ignore')  # suppress jit trace warning
+        im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype)  # input image (must be zeros, not empty)
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore', category=UserWarning)  # suppress jit trace warning
             WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
     except Exception as e:
         LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')