diff --git a/docs/source/how-to-guides/feature-guides/global_local_modeling_fut_regr.ipynb b/docs/source/how-to-guides/feature-guides/global_local_modeling_fut_regr.ipynb
index 0e0b22391..02cf34719 100644
--- a/docs/source/how-to-guides/feature-guides/global_local_modeling_fut_regr.ipynb
+++ b/docs/source/how-to-guides/feature-guides/global_local_modeling_fut_regr.ipynb
@@ -750,9 +750,8 @@
    "source": [
     "try:\n",
     "    # it already installed dependencies\n",
-    "    from torchsummary import summary\n",
     "    from torchviz import make_dot\n",
-    "except:\n",
+    "except ImportError:\n",
     "    # install graphviz on system\n",
     "    import platform\n",
     "\n",
@@ -768,7 +767,6 @@
     "    !pip install torchviz\n",
     "    !pip install graphviz\n",
     "    # import\n",
-    "    from torchsummary import summary\n",
     "    from torchviz import make_dot"
    ]
   },
diff --git a/docs/source/how-to-guides/feature-guides/global_local_trend.ipynb b/docs/source/how-to-guides/feature-guides/global_local_trend.ipynb
index 968214d97..ca7fd3eff 100644
--- a/docs/source/how-to-guides/feature-guides/global_local_trend.ipynb
+++ b/docs/source/how-to-guides/feature-guides/global_local_trend.ipynb
@@ -1308,9 +1308,8 @@
    "source": [
     "try:\n",
     "    # it already installed dependencies\n",
-    "    from torchsummary import summary\n",
     "    from torchviz import make_dot\n",
-    "except:\n",
+    "except ImportError:\n",
     "    # install graphviz on system\n",
     "    import platform\n",
     "\n",
@@ -1326,7 +1325,6 @@
     "    !pip install torchviz\n",
     "    !pip install graphviz\n",
     "    # import\n",
-    "    from torchsummary import summary\n",
     "    from torchviz import make_dot"
    ]
   },
diff --git a/docs/source/how-to-guides/feature-guides/mlflow.ipynb b/docs/source/how-to-guides/feature-guides/mlflow.ipynb
index 5e3f72f6b..645bba40c 100644
--- a/docs/source/how-to-guides/feature-guides/mlflow.ipynb
+++ b/docs/source/how-to-guides/feature-guides/mlflow.ipynb
@@ -254,7 +254,6 @@
     "# Copy and paste url from command line to web browser\n",
     "\n",
     "import mlflow\n",
-    "import torchmetrics\n",
     "from mlflow.data.pandas_dataset import PandasDataset\n",
     "\n",
     "if local:\n",
@@ -272,7 +271,6 @@
     "    )\n",
     "\n",
     "    import mlflow.pytorch\n",
-    "    from mlflow.client import MlflowClient\n",
     "\n",
     "    model_name = \"NeuralProphet\"\n",
     "\n",
diff --git a/docs/source/how-to-guides/feature-guides/network_architecture_visualization.ipynb b/docs/source/how-to-guides/feature-guides/network_architecture_visualization.ipynb
index 70d99ce3f..65afb79c5 100644
--- a/docs/source/how-to-guides/feature-guides/network_architecture_visualization.ipynb
+++ b/docs/source/how-to-guides/feature-guides/network_architecture_visualization.ipynb
@@ -41,7 +41,7 @@
     "    # it already installed dependencies\n",
     "    from torchsummary import summary\n",
     "    from torchviz import make_dot\n",
-    "except:\n",
+    "except ImportError:\n",
     "    # install graphviz on system\n",
     "    import platform\n",
     "\n",
@@ -69,7 +69,7 @@
    "source": [
     "try:\n",
     "    from neuralprophet import NeuralProphet\n",
-    "except:\n",
+    "except ImportError:\n",
     "    # if NeuralProphet is not installed yet:\n",
     "    !pip install git+https://github.com/ourownstory/neural_prophet.git\n",
     "    from neuralprophet import NeuralProphet"
diff --git a/docs/source/how-to-guides/feature-guides/pl_profiling/fit-advanced.txt b/docs/source/how-to-guides/feature-guides/pl_profiling/fit-advanced.txt
new file mode 100644
index 000000000..f081912a8
--- /dev/null
+++ b/docs/source/how-to-guides/feature-guides/pl_profiling/fit-advanced.txt
@@ -0,0 +1,4747 @@
+FIT Profiler Report
+Profile stats for: [LightningModule]TimeNet.configure_callbacks
+         7 function calls in 0.000 seconds
+
+   Ordered by: cumulative time
+
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+        1    0.000    0.000    0.000    0.000 module.py:936(configure_callbacks)
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+        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
+
+
+
+Profile stats for: [LightningModule]TimeNet.setup
+         7 function calls in 0.000 seconds
+
+   Ordered by: cumulative time
+
+   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
+        1    0.000    0.000    0.000    0.000 contextlib.py:141(__exit__)
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+        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
+
+
+
+Profile stats for: [LightningModule]TimeNet.configure_optimizers
+         1165 function calls (1119 primitive calls) in 0.029 seconds
+
+   Ordered by: cumulative time
+
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diff --git a/docs/source/how-to-guides/feature-guides/profiling_and_logging.ipynb b/docs/source/how-to-guides/feature-guides/profiling_and_logging.ipynb
new file mode 100644
index 000000000..98457c2ce
--- /dev/null
+++ b/docs/source/how-to-guides/feature-guides/profiling_and_logging.ipynb
@@ -0,0 +1,308 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tutorial 11: Profiling and Logging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This tutorial will guide you through setting up profiling and logging in your NeuralProphet models using PyTorch's Profiler and TensorBoard Logger. Profiling helps you understand where your model spends time and memory, and logging keeps track of these metrics for later analysis."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Profiling"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the Simple Profiler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Simple Profiler is a lightweight and straightforward tool that tracks the execution time of different sections of your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytorch_lightning.profilers import SimpleProfiler\n",
+    "\n",
+    "# Configure Simple Profiler\n",
+    "trainer_config = {\"profiler\": SimpleProfiler(dirpath=\"./pl_profiling\", filename=\"simple\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integrating the Simple Profiler into the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from neuralprophet import NeuralProphet, set_log_level\n",
+    "\n",
+    "# Load the dataset from the CSV file using pandas\n",
+    "df = pd.read_csv(\"https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial01.csv\")\n",
+    "\n",
+    "# Disable logging messages unless there is an error\n",
+    "set_log_level(\"ERROR\")\n",
+    "\n",
+    "# Model and prediction\n",
+    "m = NeuralProphet(trainer_config=trainer_config)\n",
+    "m.fit(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Using the Advanced Profiler"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The Advanced Profiler offers more detailed insights into your model's performance, including function-level statistics and memory usage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytorch_lightning.profilers import AdvancedProfiler\n",
+    "\n",
+    "# Configure Advanced Profiler\n",
+    "trainer_config = {\"profiler\": AdvancedProfiler(dirpath=\"./pl_profiling\", filename=\"advanced\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integrating the Advanced Profiler into the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from neuralprophet import NeuralProphet, set_log_level\n",
+    "\n",
+    "# Load the dataset from the CSV file using pandas\n",
+    "df = pd.read_csv(\"https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial01.csv\")\n",
+    "\n",
+    "# Disable logging messages unless there is an error\n",
+    "set_log_level(\"ERROR\")\n",
+    "\n",
+    "# Model and prediction\n",
+    "m = NeuralProphet(trainer_config=trainer_config)\n",
+    "m.fit(df, learning_rate=0.1, epochs=10, batch_size=128, progress=False, minimal=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
+   "source": [
+    "You can check the profiling reports in the generated text files under ``` ./pl_profiling ```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Logging"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setting Up TensorBoard Logger"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pytorch_lightning.loggers import TensorBoardLogger\n",
+    "\n",
+    "# Configure TensorBoard logger\n",
+    "trainer_config = {\"logger\": TensorBoardLogger(\"tb_logs\", name=\"NeuralProphet\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Integrating Logging into the Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from neuralprophet import NeuralProphet\n",
+    "\n",
+    "# Load the dataset from the CSV file using pandas\n",
+    "df = pd.read_csv(\"https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial01.csv\")\n",
+    "\n",
+    "\n",
+    "# Model and prediction\n",
+    "m = NeuralProphet()\n",
+    "metrics = m.fit(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualizing Logs in TensorBoard"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "bat"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tensorboard --logdir tb_logs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Open the provided URL in your browser, and you'll be able to explore various metrics like training loss, validation loss, and more, all tracked during the training process."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  Integrating Profiling with TensorBoard Logging\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, make sure to install ```torch-tb-profiler```. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch\n",
+    "from pytorch_lightning.loggers import TensorBoardLogger\n",
+    "\n",
+    "trainer_config = {}\n",
+    "\n",
+    "# Configure TensorBoard logger\n",
+    "trainer_config[\"logger\"] = TensorBoardLogger(\"tb_logs\", name=\"NeuralProphet\")\n",
+    "\n",
+    "# Integrate profiler with logging\n",
+    "trainer_config[\"profiler\"] = torch.profiler.profile(\n",
+    "    on_trace_ready=torch.profiler.tensorboard_trace_handler(\"tb_logs/profiler0\"),\n",
+    "    record_shapes=True,\n",
+    "    profile_memory=True,\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from neuralprophet import NeuralProphet\n",
+    "from neuralprophet import set_random_seed\n",
+    "\n",
+    "set_random_seed(42)\n",
+    "\n",
+    "# Load the dataset from the CSV file using pandas\n",
+    "df = pd.read_csv(\"https://github.com/ourownstory/neuralprophet-data/raw/main/kaggle-energy/datasets/tutorial01.csv\")\n",
+    "\n",
+    "# Model and prediction\n",
+    "m = NeuralProphet()\n",
+    "\n",
+    "df_train, df_val = m.split_df(df, valid_p=0.2)\n",
+    "\n",
+    "# Set the deterministic flag to True\n",
+    "metrics = m.fit(df_train, validation_df=df_val, progress=None, deterministic=True)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.10.7 ('.venv': venv)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "92c9cff0281419e73896333b85e681aea9374fd743c51074843eeada7c3f6baf"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/how-to-guides/feature-guides/prophet_to_torch_prophet.ipynb b/docs/source/how-to-guides/feature-guides/prophet_to_torch_prophet.ipynb
index 20827a015..bec2c7ff4 100644
--- a/docs/source/how-to-guides/feature-guides/prophet_to_torch_prophet.ipynb
+++ b/docs/source/how-to-guides/feature-guides/prophet_to_torch_prophet.ipynb
@@ -240,11 +240,11 @@
     "# Set loggers to ERROR level\n",
     "import logging\n",
     "import warnings\n",
+    "from neuralprophet import set_log_level\n",
     "\n",
     "logging.getLogger(\"prophet\").setLevel(logging.ERROR)\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
-    "from neuralprophet import set_log_level\n",
     "\n",
     "set_log_level(\"ERROR\")"
    ]
diff --git a/docs/source/how-to-guides/feature-guides/uncertainty_quantification.ipynb b/docs/source/how-to-guides/feature-guides/uncertainty_quantification.ipynb
index f5826a066..e542daf56 100644
--- a/docs/source/how-to-guides/feature-guides/uncertainty_quantification.ipynb
+++ b/docs/source/how-to-guides/feature-guides/uncertainty_quantification.ipynb
@@ -48,9 +48,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
     "import pandas as pd\n",
-    "from neuralprophet import NeuralProphet, uncertainty_evaluate, set_log_level, set_random_seed\n",
+    "from neuralprophet import NeuralProphet, uncertainty_evaluate, set_random_seed\n",
     "\n",
     "data_location = \"http://raw.githubusercontent.com/ourownstory/neuralprophet-data/main/datasets/\"\n",
     "df = pd.read_csv(data_location + \"energy/SF_hospital_load.csv\")"
diff --git a/docs/source/how-to-guides/index.rst b/docs/source/how-to-guides/index.rst
index 428810e93..f4228d187 100644
--- a/docs/source/how-to-guides/index.rst
+++ b/docs/source/how-to-guides/index.rst
@@ -21,6 +21,8 @@ Feature guides
     MLflow Integration<feature-guides/mlflow>
     Live Plotting during Training<feature-guides/Live_plot_during_training>
     Network Architecture Visualization<feature-guides/network_architecture_visualization>
+    Profiling and Logging<feature-guides/profiling_and_logging>
+
 
 Application examples
 --------------------
diff --git a/tests/metrics/debug-energy-price-daily.ipynb b/tests/metrics/debug-energy-price-daily.ipynb
index e94b8d756..7ddb1071c 100644
--- a/tests/metrics/debug-energy-price-daily.ipynb
+++ b/tests/metrics/debug-energy-price-daily.ipynb
@@ -7,15 +7,12 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import pathlib\n",
-    "\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "import plotly.graph_objects as go\n",
     "from plotly.subplots import make_subplots\n",
     "from plotly_resampler import unregister_plotly_resampler\n",
     "\n",
-    "from neuralprophet import NeuralProphet, set_random_seed"
+    "from neuralprophet import NeuralProphet"
    ]
   },
   {
diff --git a/tests/metrics/debug-energy-price-hourly.ipynb b/tests/metrics/debug-energy-price-hourly.ipynb
index 14a09c93e..70ce9d1ea 100644
--- a/tests/metrics/debug-energy-price-hourly.ipynb
+++ b/tests/metrics/debug-energy-price-hourly.ipynb
@@ -7,7 +7,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import pathlib\n",
     "import torch\n",
     "\n",
     "import numpy as np\n",
@@ -16,7 +15,7 @@
     "from plotly.subplots import make_subplots\n",
     "from plotly_resampler import unregister_plotly_resampler\n",
     "\n",
-    "from neuralprophet import NeuralProphet, set_random_seed"
+    "from neuralprophet import NeuralProphet"
    ]
   },
   {
diff --git a/tests/metrics/debug-yosemite.ipynb b/tests/metrics/debug-yosemite.ipynb
index a7a2b0e56..9bd584dae 100644
--- a/tests/metrics/debug-yosemite.ipynb
+++ b/tests/metrics/debug-yosemite.ipynb
@@ -7,7 +7,6 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import pathlib\n",
     "import time\n",
     "\n",
     "import numpy as np\n",