update realtime-inference example

Notebooks/2_ModelTrainingAndDeployment/model-training.ipynb

@@ -322,21 +322,21 @@
       "label": null,
       "name": "catalog_name",
       "options": {
-       "validationRegex": null,
-       "widgetDisplayType": "Text"
+       "widgetDisplayType": "Text",
+       "validationRegex": null
       },
       "parameterDataType": "String"
      },
      "widgetInfo": {
+      "widgetType": "text",
       "defaultValue": "pedroz_e2edata_dev",
       "label": null,
       "name": "catalog_name",
       "options": {
+       "widgetType": "text",
        "autoCreated": null,
-       "validationRegex": null,
-       "widgetType": "text"
-      },
-      "widgetType": "text"
+       "validationRegex": null
+      }
      }
     }
    }

Notebooks/3_Inference/realtime-inference.ipynb

@@ -1,70 +1,39 @@
 {
  "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This notebook is meant to show a simple example of how to use the realtime inference endpoint"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 0,
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
-     "nuid": "f558f7fa-8486-4a4d-bcc5-c9a71312e9e4",
+     "nuid": "c349d0aa-264d-41f5-837b-72703eb01f87",
      "showTitle": false,
      "tableResultSettingsMap": {},
      "title": ""
     }
    },
    "outputs": [],
    "source": [
-    "import os\n",
-    "import requests\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import json\n",
-    "from sklearn import datasets"
+    "# This notebook is meant to show a simple example of how to use the near real-time inference endpoint"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 0,
    "metadata": {
     "application/vnd.databricks.v1+cell": {
-     "cellMetadata": {
-      "byteLimit": 2048000,
-      "rowLimit": 10000
-     },
+     "cellMetadata": {},
      "inputWidgets": {},
-     "nuid": "1afd51a3-7462-4710-89c7-8244d7f2f11f",
+     "nuid": "62d91fa5-5a0d-4fbc-a81b-1850fd7b8d2f",
      "showTitle": false,
      "tableResultSettingsMap": {},
      "title": ""
     }
    },
    "outputs": [],
    "source": [
-    "def create_tf_serving_json(data):\n",
-    "    return {'inputs': {name: data[name].tolist() for name in data.keys()} if isinstance(data, dict) else data.tolist()}\n",
-    "\n",
-    "def score_model(dataset):\n",
-    "    url = 'https://adb-4181970831265458.18.azuredatabricks.net/serving-endpoints/pedroz_e2edata_dev-default-iris_model-endpoint/invocations'\n",
-    "    headers = {'Authorization': f'Bearer {os.environ.get(\"DATABRICKS_TOKEN\")}', 'Content-Type': 'application/json'}\n",
-    "    ds_dict = {'dataframe_split': dataset.to_dict(orient='split')} if isinstance(dataset, pd.DataFrame) else create_tf_serving_json(dataset)\n",
-    "    data_json = json.dumps(ds_dict, allow_nan=True)\n",
-    "    response = requests.request(method='POST', headers=headers, url=url, data=data_json)\n",
-    "    if response.status_code != 200:\n",
-    "        raise Exception(f'Request failed with status {response.status_code}, {response.text}')\n",
-    "    return response.json()"
+    "from databricks.sdk import WorkspaceClient"
    ]
   },
   {
@@ -77,30 +46,31 @@
       "rowLimit": 10000
      },
      "inputWidgets": {},
-     "nuid": "298490eb-582e-4ba3-8818-8617fc36c94a",
+     "nuid": "f82f4dff-f1f0-42da-b982-2a83442d3ba6",
      "showTitle": false,
      "tableResultSettingsMap": {},
      "title": ""
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/spark-d43833de-3f9d-486f-a93a-08/.ipykernel/2824/command-8394587244958649-400682212:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
-      "  df_samples.columns = df_samples.columns.str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n",
-      "/home/spark-d43833de-3f9d-486f-a93a-08/.ipykernel/2824/command-8394587244958649-400682212:4: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
-      "  df_samples.columns = df_samples.columns.str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# Pull the dataset for running the inference\n",
-    "iris_samples = datasets.load_iris(as_frame=True)\n",
-    "df_samples = pd.DataFrame(data = iris_samples['data'], columns = iris_samples['feature_names'])\n",
-    "df_samples.columns = df_samples.columns.str.replace(' ', '_').str.replace('(', '').str.replace(')', '')\n",
-    "df_samples = df_samples.head(1)"
+    "w = WorkspaceClient()\n",
+    "\n",
+    "sample_input = [\n",
+    "    {\n",
+    "        \"sepal_length_cm\": 5.1,\n",
+    "        \"sepal_width_cm\": 3.5,\n",
+    "        \"petal_length_cm\": 1.4,\n",
+    "        \"petal_width_cm\": 0.2\n",
+    "    }\n",
+    "]\n",
+    "\n",
+    "prediction = w.serving_endpoints.query(\n",
+    "    name=\"pedroz_e2edata_dev-default-iris_model-endpoint\",\n",
+    "    dataframe_records=sample_input\n",
+    ")\n",
+    "\n",
+    "display(prediction)"
    ]
   },
   {
@@ -113,26 +83,20 @@
       "rowLimit": 10000
      },
      "inputWidgets": {},
-     "nuid": "5f5aa914-0c0e-4387-83e6-0e9930a2099e",
+     "nuid": "6f25d6d3-1170-47a6-837e-69f90f17e1dd",
      "showTitle": false,
      "tableResultSettingsMap": {},
      "title": ""
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'predictions': [0]}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "score_model(df_samples)"
+    "print(\n",
+    "    'Input:',\n",
+    "    sample_input,\n",
+    "    '\\nOutput: ',\n",
+    "    prediction.predictions\n",
+    ")"
    ]
   }
  ],
@@ -142,7 +106,7 @@
    "dashboards": [],
    "environmentMetadata": {
     "base_environment": "",
-    "environment_version": "2"
+    "environment_version": "4"
    },
    "inputWidgetPreferences": null,
    "language": "python",