Saturn Cloud template: Machine Learning & Classical AI Section

examples/machine-learning_ClassicAI/mlflow-server/README.md

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+# MLflow Tracking Server Template
+
+This template demonstrates how to use MLflow to track, log, and manage machine learning experiments in a single notebook. It trains a Random Forest model on the Diabetes dataset, logs parameters, metrics, and artifacts, and enables viewing and reloading runs locally or through a remote MLflow tracking server.
+
+You can deploy MLflow-tracked models via platforms like **Saturn Cloud**, refer to Saturn’s documentation for deployment guidance.
+
+---
+
+## References
+
+* [MLflow Documentation](https://mlflow.org/docs/latest/index.html)
+* [Saturn Cloud Docs](https://saturncloud.io/docs/)
+* [Scikit-learn RandomForestRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html)

examples/machine-learning_ClassicAI/mlflow-server/mlflow-tracking.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JwE9FH9oFOMb"
+   },
+   "source": [
+    "# MLflow Tracking Server\n",
+    "\n",
+    "**MLflow** is an open-source platform that simplifies the tracking, comparison, and deployment of machine learning experiments.\n",
+    "\n",
+    "In this sample example template, you’ll use MLflow to **track training runs**, **log parameters and metrics**, and **store models** for future reuse — all within a single notebook.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oV2djjnvFOMj"
+   },
+   "source": [
+    "Install **MLflow**, **Gradio**, and supporting libraries including **scikit‑learn**, **matplotlib**, and **pandas**.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "b5yEqAXTFOMk"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -q mlflow scikit-learn matplotlib pandas gradio\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IppJqmrgFOMm"
+   },
+   "source": [
+    "Import MLflow, perform a quick GPU check with PyTorch, and load helper libraries used throughout.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "F31VfPfuFOMn"
+   },
+   "outputs": [],
+   "source": [
+    "import mlflow, os, torch, pandas as pd, matplotlib.pyplot as plt, gradio as gr\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.datasets import load_diabetes\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "device = 'cuda' if torch.cuda.is_available() else 'cpu'\n",
+    "print(f'✅ Using device: {device}')\n",
+    "if device == 'cpu':\n",
+    "    print('⚠️ Running on CPU — switch to GPU for faster performance if available.')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fyS8Q66nFOMp"
+   },
+   "source": [
+    "By default, MLflow saves runs to the local **`mlruns/`** directory. You can switch to a **remote tracking server** later by setting a different tracking URI.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Le6Ec_F_FOMq"
+   },
+   "outputs": [],
+   "source": [
+    "mlflow.set_tracking_uri('file:///content/mlruns')\n",
+    "mlflow.set_experiment('mlflow_tracking_demo')\n",
+    "print('🎯 Tracking URI:', mlflow.get_tracking_uri())\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eTSpUqXLFOMs"
+   },
+   "source": [
+    "It fetches experiment metadata, parameters, and metrics from your local `mlruns/` directory (or a remote server if configured).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tNSyaQmHFOMu"
+   },
+   "outputs": [],
+   "source": [
+    "from mlflow.tracking import MlflowClient\n",
+    "\n",
+    "def show_mlflow_runs_table(experiment_name=\"mlflow_tracking_demo\"):\n",
+    "    \"\"\"Display all MLflow runs (similar to MLflow UI Table).\"\"\"\n",
+    "    client = MlflowClient()\n",
+    "    experiment = client.get_experiment_by_name(experiment_name)\n",
+    "\n",
+    "    if not experiment:\n",
+    "        return pd.DataFrame({\"Info\": [\"No experiment found. Run a training cell first.\"]})\n",
+    "    runs = client.search_runs([experiment.experiment_id])\n",
+    "    if not runs:\n",
+    "        return pd.DataFrame({\"Info\": [\"No runs logged yet.\"]})\n",
+    "\n",
+    "    rows = []\n",
+    "    for r in runs:\n",
+    "        row = {\n",
+    "            \"Run ID\": r.info.run_id,\n",
+    "            \"Status\": r.info.status,\n",
+    "            \"Start Time\": pd.to_datetime(r.info.start_time, unit=\"ms\"),\n",
+    "            \"End Time\": pd.to_datetime(r.info.end_time, unit=\"ms\"),\n",
+    "            \"Duration (s)\": round((r.info.end_time - r.info.start_time) / 1000, 2)\n",
+    "            if r.info.end_time else None,\n",
+    "        }\n",
+    "        row.update(r.data.params)\n",
+    "        row.update(r.data.metrics)\n",
+    "        rows.append(row)\n",
+    "\n",
+    "    df = pd.DataFrame(rows)\n",
+    "    main_cols = [\"Run ID\", \"Status\", \"Start Time\", \"End Time\", \"Duration (s)\"]\n",
+    "    other_cols = [c for c in df.columns if c not in main_cols]\n",
+    "    df = df[main_cols + other_cols]\n",
+    "    print(f\"✅ Showing {len(df)} runs from experiment '{experiment_name}'\")\n",
+    "    return df\n",
+    "\n",
+    "runs_df = show_mlflow_runs_table()\n",
+    "display(runs_df)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oMeP7H8qFOMw"
+   },
+   "source": [
+    "Let's train a small **Random Forest** on the Diabetes dataset and log parameters, metrics, and the model artefact to MLflow.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "tqqgobq6FOMy"
+   },
+   "outputs": [],
+   "source": [
+    "from mlflow.models.signature import infer_signature\n",
+    "\n",
+    "with mlflow.start_run() as run:\n",
+    "    db = load_diabetes()\n",
+    "    X_train, X_test, y_train, y_test = train_test_split(db.data, db.target, test_size=0.2, random_state=42)\n",
+    "\n",
+    "    model = RandomForestRegressor(n_estimators=100, max_depth=6, random_state=42)\n",
+    "    model.fit(X_train, y_train)\n",
+    "    preds = model.predict(X_test)\n",
+    "    signature = infer_signature(X_test, preds)\n",
+    "\n",
+    "    mlflow.log_params({'n_estimators': 100, 'max_depth': 6})\n",
+    "    mlflow.log_metric('mean_prediction', float(preds.mean()))\n",
+    "    mlflow.sklearn.log_model(model, 'model', signature=signature)\n",
+    "\n",
+    "    print(f'Run ID: {run.info.run_id}')\n",
+    "    print('✅ Training and logging complete!')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KwHrJpK9FOMz"
+   },
+   "source": [
+    "Use the run ID to load the stored model for inference.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iB6HPf3kFOM0"
+   },
+   "outputs": [],
+   "source": [
+    "run_id = run.info.run_id\n",
+    "loaded_model = mlflow.sklearn.load_model(f'runs:/{run_id}/model')\n",
+    "print('✅ Model loaded successfully!')\n",
+    "print('Sample predictions:', loaded_model.predict(X_test[:5]))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "7_ULM2cqFOM0"
+   },
+   "source": [
+    "So, you've configured MLflow tracking locally (can be configure for MLflow remote server too), logged parameters, metrics, and model artifacts.\n",
+    "\n",
+    "Additionally, you can reload a trained model from specific run using the `run Id`. Guide on deployment on saturn can be found in the [saturn documentation](https://saturncloud.io/docs)."
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "provenance": []
+  },
+  "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": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}