notebooks/test_deployed_model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Deployed Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.datasets import mnist\n",
    "\n",
    "# import torch\n",
    "# from torchvision import datasets\n",
    "# from torchvision.transforms import ToTensor\n",
    "# from torch.utils.data import DataLoader\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## login into Openshift"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_url = \"https://api.smaug.na.operate-first.cloud:6443\"\n",
    "\n",
    "# Using token\n",
    "token = os.environ[\"CLUSTER_TOKEN\"]\n",
    "!oc login --token=$token --server=$cluster_url\n",
    "\n",
    "# # Using credentials\n",
    "# username = os.environ[\"USERNAME\"]\n",
    "# password = os.environ[\"PASSWORD\"]\n",
    "# !oc login -u $username -p $password --insecure-skip-tls-verify $cluster_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using project \"thoth-deployment-examples\" on server \"https://api.cnv.massopen.cloud:6443\".\n"
     ]
    }
   ],
   "source": [
    "!oc project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Now using project \"thoth-deployment-examples\" on server \"https://api.cnv.massopen.cloud:6443\".\n"
     ]
    }
   ],
   "source": [
    "!oc project thoth-deployment-examples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                                  READY     STATUS      RESTARTS   AGE\n",
      "elyra-aidevsecops-tutorial-2-deploy   0/1       Completed   0          12d\n",
      "elyra-aidevsecops-tutorial-2-wmml6    1/1       Running     0          12d\n"
     ]
    }
   ],
   "source": [
    "!oc get pods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                         HOST/PORT                                                                      PATH      SERVICES                     PORT      TERMINATION   WILDCARD\n",
      "elyra-aidevsecops-tutorial   elyra-aidevsecops-tutorial-thoth-deployment-examples.apps.cnv.massopen.cloud             elyra-aidevsecops-tutorial   <all>                   None\n"
     ]
    }
   ],
   "source": [
    "!oc get routes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test deployed Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The number in the image is 0 with probability 1.0\n"
     ]
    }
   ],
   "source": [
    "USE_PYTORCH = bool(int(os.getenv(\"TUTORIAL_USE_PYTORCH\", 0)))\n",
    "\n",
    "if USE_PYTORCH:\n",
    "    # Prepare MNIST data.\n",
    "    test_data = datasets.MNIST(\n",
    "        root=\"./data/raw/pytorch-mnist-dataset\",\n",
    "        train=False,\n",
    "        transform=ToTensor(),\n",
    "    )\n",
    "\n",
    "    loaders = {\n",
    "        \"test\": torch.utils.data.DataLoader(\n",
    "            test_data, batch_size=1, shuffle=True, num_workers=1\n",
    "        ),\n",
    "    }\n",
    "\n",
    "    test_data = loaders[\"test\"]\n",
    "\n",
    "    total_tests = int(os.getenv(\"TUTORIAL_MAX_REQUESTS_TEST\", len(test_data)))\n",
    "else:\n",
    "    _, (x_test, y_test) = mnist.load_data()\n",
    "\n",
    "    # Convert to float32.\n",
    "    x_test = np.array(x_test, np.float32)\n",
    "\n",
    "    # Normalize images value from [0, 255] to [0, 1].\n",
    "    x_test = x_test / 255.0\n",
    "\n",
    "    test_data = zip(x_test, y_test)\n",
    "\n",
    "    total_tests = int(os.getenv(\"TUTORIAL_MAX_REQUESTS_TEST\", len(x_test)))\n",
    "\n",
    "addr = os.getenv(\n",
    "    \"THOTH_AIDEVSECOPS_TUTORIAL_MODEL_URL\",\n",
    "    \"http://elyra-aidevsecops-tutorial-thoth-deployment-examples.apps.smaug.na.operate-first.cloud\",\n",
    ")\n",
    "test_url = addr + \"/predict\"\n",
    "\n",
    "# prepare headers for http request\n",
    "headers = {\"content-type\": \"application/json\"}\n",
    "\n",
    "# select one test image\n",
    "img = test_data[5890]\n",
    "data = json.dumps({\"inputs\": img.tolist()})\n",
    "\n",
    "# Check which image is sent\n",
    "plt.imshow(img, cmap=\"gray\")\n",
    "plt.show(block=False)\n",
    "# plt.pause(1)\n",
    "# plt.close()\n",
    "print(f\"Input image number: {test_data[5890]}\")\n",
    "\n",
    "# send http request with image and receive response\n",
    "response = requests.post(test_url, data=data, headers=headers)\n",
    "\n",
    "# decode response\n",
    "json_response = response.json()\n",
    "# {'prediction': 0, 'latency': 0.030432076993747614, 'probability': 1.0}\n",
    "\n",
    "prediction = json_response[\"prediction\"]\n",
    "latency = json_response[\"latency\"]\n",
    "probability = json_response[\"probability\"]\n",
    "\n",
    "print(f\"Model predicted {prediction} in {latency} s with probability {probability}\")"
   ]
  }
 ],
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