Add Optuna jupyter example (#198)

.github/workflows/test-examples.yml

@@ -20,6 +20,7 @@ jobs:
           - integrations/llm/comet-llm/notebooks/CometLLM_hello_world.ipynb
           - integrations/model-evaluation/gradio/notebooks/Gradio_and_Comet.ipynb
           - integrations/model-evaluation/gradio/notebooks/Logging_Model_Inferences_with_Comet_and_Gradio.ipynb
+          - integrations/model-optimization/optuna/notebooks/Comet_with_optuna.ipynb
           - integrations/model-optimization/ray-tune/notebooks/Comet_and_Ray.ipynb
           - integrations/model-training/composer/notebooks/comet_composer.ipynb
           - integrations/model-training/fastai/notebooks/fastai_hello_world.ipynb

integrations/model-optimization/optuna/notebooks/Comet_with_optuna.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "a4f94350-269b-4e7e-a4ae-afb0804b201f",
+   "metadata": {},
+   "source": [
+    "# Comet with Optuna\n",
+    "\n",
+    "<img src=\"https://cdn.comet.ml/img/notebook_logo.png\">\n",
+    "<img src=\"https://optuna.org/assets/img/optuna-logo.png\">\n",
+    "\n",
+    "[Optuna](https://optuna.org/) is an open source hyperparameter optimization framework to automate hyperparameter search.\n",
+    "\n",
+    "Instrument Optuna with Comet to start managing experiments and track hyperparameters for faster and easier reproducibility and collaboration.\n",
+    "\n",
+    "Get a preview for what's to come. Check out a completed experiment created from this notebook [here](https://www.comet.com/examples/comet-example-optuna-notebook/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5989b8ec-d587-4658-8a70-e6dfa7391f64",
+   "metadata": {},
+   "source": [
+    "# Install dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8bef2b18-d046-404f-a55c-eacdcf17506c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%pip install \"comet_ml>=3.33.10\" \"optuna>=4.0.0\" \"optuna-integration>=4.0.0\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2a4b2cf0-a0a7-499a-b09c-92a00e43d7d6",
+   "metadata": {},
+   "source": [
+    "# Login to Comet"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f5f3da5e-b71a-4c44-a79e-5d1e0dec5837",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import comet_ml\n",
+    "\n",
+    "comet_ml.login()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "585d806c-8c8e-471a-bffb-5f5a6f2c15fd",
+   "metadata": {},
+   "source": [
+    "# Create the Study and Comet Callback"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28d1913f-5ee1-446d-a53b-74822a7ac041",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import optuna\n",
+    "from optuna_integration.comet import CometCallback\n",
+    "\n",
+    "study = optuna.create_study()\n",
+    "comet = CometCallback(\n",
+    "    study,\n",
+    "    project_name=\"comet-example-optuna-hello-world-notebook\",\n",
+    "    metric_names=[\"score\"],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd19a3c4-19ff-4dbf-bda7-e84d1cb98ee7",
+   "metadata": {},
+   "source": [
+    "# Create the objective function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbaef3ce-3568-424b-80e8-ab2594ebc804",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "@comet.track_in_comet()\n",
+    "def objective(trial):\n",
+    "    x = trial.suggest_float(\"x\", -10, 10)\n",
+    "    objective = (x - 2) ** 2\n",
+    "\n",
+    "    return objective"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4c2dede2-58ce-4a2e-9097-75b7c6694e0a",
+   "metadata": {},
+   "source": [
+    "# Start the optimization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7aa323f-8faf-41d6-b318-049add1f4422",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "study.optimize(objective, n_trials=20, callbacks=[comet])\n",
+    "\n",
+    "best_params = study.best_params\n",
+    "found_x = best_params[\"x\"]\n",
+    "print(\"Found x: {}, (x - 2)^2: {}\".format(found_x, (found_x - 2) ** 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7baf1d2f-561f-4524-b9d3-c08b572eedba",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}