added example for Single Model Serving

Author: Milstein

5_rest_requests_single_model.ipynb

@@ -0,0 +1,206 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "55c8afde-9b18-4b6a-9ee5-33924bdb4f16",
+   "metadata": {},
+   "source": [
+    "# REST Inference for single-model server deployment \n",
+    "\n",
+    "_Note: Use this procedure for testing a model that you deployed on a single-model server. See `3_rest_requests_multi_model.ipynb` for testing a model that you deployed on a multi-model server._"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c004acc-13cd-4917-8480-592c7c2d623b",
+   "metadata": {},
+   "source": [
+    "## First, replace the placeholder with the *url* or *External* you got at the previous step from the Model Serving configuration\n",
+    "\n",
+    "The model name is already filled in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0de65d02-84a6-4cff-882e-551cdd42b486",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_name = \"fraud\"\n",
+    "infer_endpoint = 'change_me' # e.g. 'https://model-server.<your-namespace>.svc.cluster.local' or 'https://model-server-<your-namespace>.apps.shift.nerc.mghpcc.org'\n",
+    "infer_url = f\"{infer_endpoint}/v2/models/{model_name}/infer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d94f9ece-e9cf-44e2-a8a2-73160186aee8",
+   "metadata": {},
+   "source": [
+    "## Request Function\n",
+    "\n",
+    "Build and submit the REST request. \n",
+    "\n",
+    "Note: You submit the data in the same format that you used for an ONNX inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "54b9386f-683a-4880-b780-c40bec3ab9f8",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "\n",
+    "\n",
+    "def rest_request(data):\n",
+    "    json_data = {\n",
+    "        \"inputs\": [\n",
+    "            {\n",
+    "                \"name\": \"dense_input\",\n",
+    "                \"shape\": [1, 5],\n",
+    "                \"datatype\": \"FP32\",\n",
+    "                \"data\": data\n",
+    "            }\n",
+    "        ]\n",
+    "    }\n",
+    "\n",
+    "    response = requests.post(infer_url, json=json_data, verify=False)\n",
+    "    response_dict = response.json()\n",
+    "    return response_dict['outputs'][0]['data']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f871f12",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Load the scaler\n",
+    "import pickle\n",
+    "with open('artifact/scaler.pkl', 'rb') as handle:\n",
+    "    scaler = pickle.load(handle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "45ad16ac-23da-48bd-9796-f8e4cacae981",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [0.3111400080477545, 1.9459399775518593, 1.0, 0.0, 0.0]\n",
+    "prediction = rest_request(scaler.transform([data]).tolist()[0])\n",
+    "prediction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1d66e0f7-4d4e-4879-bdf1-36b712432fd9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshhold = 0.95\n",
+    "\n",
+    "if (prediction[0] > threshhold):\n",
+    "    print('fraud')\n",
+    "else:\n",
+    "    print('not fraud')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f7b17c0",
+   "metadata": {},
+   "source": [
+    "## Example 1: user buys a coffee\n",
+    "\n",
+    "In this example, the user is buying a coffee. The parameters given to the model are:\n",
+    "* same location as the last transaction (distance=0)\n",
+    "* same median price as the last transaction (ratio_to_median=1)\n",
+    "* using a pin number (pin=1)\n",
+    "* using the credit card chip (chip=1)\n",
+    "* not an online transaction (online=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0a68b67-b109-4a2f-b097-092f4a4d25ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [0.0, 1.0, 1.0, 1.0, 0.0]\n",
+    "prediction = rest_request(scaler.transform([data]).tolist()[0])\n",
+    "prediction\n",
+    "threshhold = 0.95\n",
+    "\n",
+    "if (prediction[0] > threshhold):\n",
+    "    print('The model predicts that this is fraud')\n",
+    "else:\n",
+    "    print('The model predicts that this is not fraud')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db10b280",
+   "metadata": {},
+   "source": [
+    "## Example 2: fraudulent transaction\n",
+    "\n",
+    "In this example, someone stole the user's credit card and is buying something online. The parameters given to the model are:\n",
+    "* very far away from the last transaction (distance=100)\n",
+    "* median price similar to the last transaction (ratio_to_median=1.2)\n",
+    "* not using a pin number (pin=0)\n",
+    "* not using the credit card chip (chip=0)\n",
+    "* is an online transaction (online=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "219b8927",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "data = [100, 1.2, 0.0, 0.0, 1.0]\n",
+    "prediction = rest_request(scaler.transform([data]).tolist()[0])\n",
+    "prediction\n",
+    "threshhold = 0.95\n",
+    "\n",
+    "if (prediction[0] > threshhold):\n",
+    "    print('The model predicts that this is fraud')\n",
+    "else:\n",
+    "    print('The model predicts that this is not fraud')"
+   ]
+  }
+ ],
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+  "kernelspec": {
+   "display_name": "Python 3.9",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
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+ },
+ "nbformat": 4,
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+}