deploy-kfserving-recommender-service.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import json\n",
    "import time\n",
    "from kubernetes import client\n",
    "import pandas as pd\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "\n",
    "from kfserving import KFServingClient\n",
    "from kfserving import constants\n",
    "from kfserving import utils\n",
    "from kfserving import V1beta1InferenceService\n",
    "from kfserving import V1beta1InferenceServiceSpec\n",
    "from kfserving import V1beta1PredictorSpec\n",
    "from kfserving import V1beta1TFServingSpec\n",
    "from kfserving import V1beta1LoggerSpec\n",
    "\n",
    "from kubernetes.client import V1ResourceRequirements\n",
    "from kubernetes.client import V1ServiceAccount\n",
    "\n",
    "from kfserving import KFServingClient\n",
    "KFServing = KFServingClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NAMESPACE = 'kfserving-test'\n",
    "MODEL_NAME = 'product-recommender'\n",
    "KFSERVING_VERSION = 'v1beta1'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy Service Account with AWS Credentials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def creds():\n",
    "    KFServing.set_credentials(storage_type='S3',\n",
    "                              namespace=NAMESPACE,\n",
    "                              credentials_file=\"C:\\\\Users\\\\user\\\\.aws\\\\credentials\", # base64 coded AWS Credentials\n",
    "                              service_account='sa',\n",
    "                              s3_profile='default',\n",
    "                              s3_endpoint='s3-eu-central-1.amazonaws.com',\n",
    "                              s3_region='eu-central-1',\n",
    "                              s3_use_https='1',\n",
    "                              s3_verify_ssl='1')\n",
    "#creds()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy TensorFlow Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'api_version': 'serving.kubeflow.org/v1beta1',\n",
       " 'kind': 'InferenceService',\n",
       " 'metadata': {'annotations': None,\n",
       "              'cluster_name': None,\n",
       "              'creation_timestamp': None,\n",
       "              'deletion_grace_period_seconds': None,\n",
       "              'deletion_timestamp': None,\n",
       "              'finalizers': None,\n",
       "              'generate_name': None,\n",
       "              'generation': None,\n",
       "              'labels': None,\n",
       "              'managed_fields': None,\n",
       "              'name': 'product-recommender',\n",
       "              'namespace': 'kfserving-test',\n",
       "              'owner_references': None,\n",
       "              'resource_version': None,\n",
       "              'self_link': None,\n",
       "              'uid': None},\n",
       " 'spec': {'explainer': None,\n",
       "          'predictor': {'active_deadline_seconds': None,\n",
       "                        'affinity': None,\n",
       "                        'automount_service_account_token': None,\n",
       "                        'batcher': None,\n",
       "                        'canary_traffic_percent': None,\n",
       "                        'container_concurrency': None,\n",
       "                        'containers': None,\n",
       "                        'dns_config': None,\n",
       "                        'dns_policy': None,\n",
       "                        'enable_service_links': None,\n",
       "                        'ephemeral_containers': None,\n",
       "                        'host_aliases': None,\n",
       "                        'host_ipc': None,\n",
       "                        'host_network': None,\n",
       "                        'host_pid': None,\n",
       "                        'hostname': None,\n",
       "                        'image_pull_secrets': None,\n",
       "                        'init_containers': None,\n",
       "                        'logger': None,\n",
       "                        'max_replicas': None,\n",
       "                        'min_replicas': 1,\n",
       "                        'node_name': None,\n",
       "                        'node_selector': None,\n",
       "                        'onnx': None,\n",
       "                        'overhead': None,\n",
       "                        'pmml': None,\n",
       "                        'preemption_policy': None,\n",
       "                        'priority': None,\n",
       "                        'priority_class_name': None,\n",
       "                        'pytorch': None,\n",
       "                        'readiness_gates': None,\n",
       "                        'restart_policy': None,\n",
       "                        'runtime_class_name': None,\n",
       "                        'scheduler_name': None,\n",
       "                        'security_context': None,\n",
       "                        'service_account': None,\n",
       "                        'service_account_name': 'sa',\n",
       "                        'share_process_namespace': None,\n",
       "                        'sklearn': None,\n",
       "                        'subdomain': None,\n",
       "                        'tensorflow': {'args': None,\n",
       "                                       'command': None,\n",
       "                                       'env': None,\n",
       "                                       'env_from': None,\n",
       "                                       'image': None,\n",
       "                                       'image_pull_policy': None,\n",
       "                                       'lifecycle': None,\n",
       "                                       'liveness_probe': None,\n",
       "                                       'name': None,\n",
       "                                       'ports': None,\n",
       "                                       'protocol_version': None,\n",
       "                                       'readiness_probe': None,\n",
       "                                       'resources': None,\n",
       "                                       'runtime_version': '2.4.0',\n",
       "                                       'security_context': None,\n",
       "                                       'startup_probe': None,\n",
       "                                       'stdin': None,\n",
       "                                       'stdin_once': None,\n",
       "                                       'storage_uri': 's3://bucket-fex/0/f83dc82ab858401dac634a2ec3e35c82/artifacts/saved_model/tfmodel/',\n",
       "                                       'termination_message_path': None,\n",
       "                                       'termination_message_policy': None,\n",
       "                                       'tty': None,\n",
       "                                       'volume_devices': None,\n",
       "                                       'volume_mounts': None,\n",
       "                                       'working_dir': None},\n",
       "                        'termination_grace_period_seconds': None,\n",
       "                        'timeout': None,\n",
       "                        'tolerations': None,\n",
       "                        'topology_spread_constraints': None,\n",
       "                        'triton': None,\n",
       "                        'volumes': None,\n",
       "                        'xgboost': None},\n",
       "          'transformer': None},\n",
       " 'status': None}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api_version = constants.KFSERVING_GROUP + '/' + KFSERVING_VERSION\n",
    "\n",
    "isvc = V1beta1InferenceService(api_version=api_version,\n",
    "                               kind=constants.KFSERVING_KIND,\n",
    "                               metadata=client.V1ObjectMeta(\n",
    "                                   name=MODEL_NAME,\n",
    "                                   namespace=NAMESPACE,\n",
    "                                   # annotations= {\"prometheus.io/scrape\" : \"true\",\n",
    "                                   #              \"prometheus.io/port\" : \"8082\"}\n",
    "                               ),\n",
    "                               spec=V1beta1InferenceServiceSpec(\n",
    "                                   predictor=V1beta1PredictorSpec(\n",
    "                                       service_account_name='sa',  # service account for aws credentials\n",
    "                                       min_replicas=1,  # if 0: replica will scale down to 0 when there are no requests\n",
    "                                       # logger=V1beta1LoggerSpec(\n",
    "                                       #    mode='all',\n",
    "                                       #    url='http://cloudevent-service.kfserving-test.svc.cluster.local:3000'),\n",
    "                                       tensorflow=(V1beta1TFServingSpec(\n",
    "                                           runtime_version='2.4.0',  # TF Serving Version\n",
    "                                           storage_uri='s3://bucket-fex/0/f83dc82ab858401dac634a2ec3e35c82/artifacts/saved_model/tfmodel/')\n",
    "                                       )))\n",
    "                               )\n",
    "isvc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apiVersion': 'serving.kubeflow.org/v1beta1',\n",
       " 'kind': 'InferenceService',\n",
       " 'metadata': {'creationTimestamp': '2021-01-14T16:13:26Z',\n",
       "  'generation': 1,\n",
       "  'name': 'product-recommender',\n",
       "  'namespace': 'kfserving-test',\n",
       "  'resourceVersion': '664708',\n",
       "  'selfLink': '/apis/serving.kubeflow.org/v1beta1/namespaces/kfserving-test/inferenceservices/product-recommender',\n",
       "  'uid': 'ce788f85-a50b-46d8-b4ab-4a004db4cdd8'},\n",
       " 'spec': {'predictor': {'minReplicas': 1,\n",
       "   'serviceAccountName': 'sa',\n",
       "   'tensorflow': {'name': 'kfserving-container',\n",
       "    'resources': {'limits': {'cpu': '1', 'memory': '2Gi'},\n",
       "     'requests': {'cpu': '1', 'memory': '2Gi'}},\n",
       "    'runtimeVersion': '2.4.0',\n",
       "    'storageUri': 's3://bucket-fex/0/f83dc82ab858401dac634a2ec3e35c82/artifacts/saved_model/tfmodel/'}}}}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KFServing = KFServingClient()\n",
    "KFServing.create(isvc, version=KFSERVING_VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                 READY      PREDICTOR_CANARY_TRAFFIC  URL                                                              \n",
      "product-recommender  Unknown                                                                                               \n",
      "product-recommender  Unknown                                                                                               \n",
      "product-recommender  Unknown                                                                                               \n",
      "product-recommender  Unknown                                                                                               \n",
      "product-recommender  True                                 http://product-recommender.kfserving-test.example.com            \n"
     ]
    }
   ],
   "source": [
    "KFServing.get(MODEL_NAME, namespace=NAMESPACE, watch=True, timeout_seconds=120, version=KFSERVING_VERSION)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Canary to InferenceService by patching a new InferenceService"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apiVersion': 'serving.kubeflow.org/v1beta1',\n",
       " 'kind': 'InferenceService',\n",
       " 'metadata': {'creationTimestamp': '2021-01-14T16:00:02Z',\n",
       "  'finalizers': ['inferenceservice.finalizers'],\n",
       "  'generation': 3,\n",
       "  'name': 'product-recommender',\n",
       "  'namespace': 'kfserving-test',\n",
       "  'resourceVersion': '660026',\n",
       "  'selfLink': '/apis/serving.kubeflow.org/v1beta1/namespaces/kfserving-test/inferenceservices/product-recommender',\n",
       "  'uid': '01cb2e6d-d418-46d1-b74b-8f786d898ba8'},\n",
       " 'spec': {'predictor': {'canaryTrafficPercent': 10,\n",
       "   'minReplicas': 1,\n",
       "   'serviceAccountName': 'sa',\n",
       "   'tensorflow': {'name': 'kfserving-container',\n",
       "    'resources': {'limits': {'cpu': '1', 'memory': '2Gi'},\n",
       "     'requests': {'cpu': '1', 'memory': '2Gi'}},\n",
       "    'runtimeVersion': '2.4.0',\n",
       "    'storageUri': 's3://bucket-fex/0/00f118c323604546985c24871736d5d7/artifacts/saved_model/tfmodel/'}}},\n",
       " 'status': {'address': {'url': 'http://product-recommender.kfserving-test.svc.cluster.local/v1/models/product-recommender:predict'},\n",
       "  'components': {'predictor': {'address': {'url': 'http://product-recommender-predictor-default.kfserving-test.svc.cluster.local'},\n",
       "    'latestCreatedRevision': 'product-recommender-predictor-default-bsqsz',\n",
       "    'latestReadyRevision': 'product-recommender-predictor-default-bsqsz',\n",
       "    'latestRolledoutRevision': 'product-recommender-predictor-default-wjzk9',\n",
       "    'traffic': [{'latestRevision': True,\n",
       "      'percent': 20,\n",
       "      'revisionName': 'product-recommender-predictor-default-bsqsz',\n",
       "      'tag': 'latest',\n",
       "      'url': 'http://latest-product-recommender-predictor-default.kfserving-test.example.com'},\n",
       "     {'latestRevision': False,\n",
       "      'percent': 80,\n",
       "      'revisionName': 'product-recommender-predictor-default-wjzk9',\n",
       "      'tag': 'prev',\n",
       "      'url': 'http://prev-product-recommender-predictor-default.kfserving-test.example.com'}],\n",
       "    'url': 'http://product-recommender-predictor-default.kfserving-test.example.com'}},\n",
       "  'conditions': [{'lastTransitionTime': '2021-01-14T16:05:36Z',\n",
       "    'status': 'True',\n",
       "    'type': 'IngressReady'},\n",
       "   {'lastTransitionTime': '2021-01-14T16:05:36Z',\n",
       "    'severity': 'Info',\n",
       "    'status': 'True',\n",
       "    'type': 'PredictorConfigurationReady'},\n",
       "   {'lastTransitionTime': '2021-01-14T16:05:36Z',\n",
       "    'status': 'True',\n",
       "    'type': 'PredictorReady'},\n",
       "   {'lastTransitionTime': '2021-01-14T16:05:36Z',\n",
       "    'severity': 'Info',\n",
       "    'status': 'True',\n",
       "    'type': 'PredictorRouteReady'},\n",
       "   {'lastTransitionTime': '2021-01-14T16:05:36Z',\n",
       "    'status': 'True',\n",
       "    'type': 'Ready'}],\n",
       "  'url': 'http://product-recommender.kfserving-test.example.com'}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isvc = V1beta1InferenceService(api_version=api_version,\n",
    "                               kind=constants.KFSERVING_KIND,\n",
    "                               metadata=client.V1ObjectMeta(\n",
    "                                   name=MODEL_NAME,\n",
    "                                   namespace=NAMESPACE,\n",
    "                                   # annotations= {\"prometheus.io/scrape\" : \"true\",\n",
    "                                   #              \"prometheus.io/port\" : \"8082\"}\n",
    "                               ),\n",
    "                               spec=V1beta1InferenceServiceSpec(\n",
    "                                   predictor=V1beta1PredictorSpec(\n",
    "                                       canary_traffic_percent=10,\n",
    "                                       service_account_name='sa',  # service account for aws credentials\n",
    "                                       min_replicas=1,  # if 0: replica will scale down to 0 when there are no requests\n",
    "                                       # logger=V1beta1LoggerSpec(\n",
    "                                       #    mode='all',\n",
    "                                       #    url='http://cloudevent-service.kfserving-test.svc.cluster.local:3000'),\n",
    "                                       tensorflow=(V1beta1TFServingSpec(\n",
    "                                           runtime_version='2.4.0',  # TF Serving Version\n",
    "                                           storage_uri=\"s3://bucket-fex/0/00f118c323604546985c24871736d5d7/artifacts/saved_model/tfmodel/\")\n",
    "                                       )))\n",
    "                               )\n",
    "\n",
    "KFServing.patch(MODEL_NAME, isvc, namespace=NAMESPACE,\n",
    "                version=KFSERVING_VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NAME                 READY      PREDICTOR_CANARY_TRAFFIC  URL                                                              \n",
      "product-recommender  True                                 http://product-recommender.kfserving-test.example.com            \n"
     ]
    }
   ],
   "source": [
    "KFServing.get(MODEL_NAME, namespace=NAMESPACE, watch=True, timeout_seconds=120, version=KFSERVING_VERSION)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'apiVersion': 'serving.kubeflow.org/v1alpha2',\n",
       " 'kind': 'InferenceService',\n",
       " 'metadata': {'creationTimestamp': '2021-01-14T16:00:02Z',\n",
       "  'deletionGracePeriodSeconds': 0,\n",
       "  'deletionTimestamp': '2021-01-14T16:11:46Z',\n",
       "  'finalizers': ['inferenceservice.finalizers'],\n",
       "  'generation': 4,\n",
       "  'name': 'product-recommender',\n",
       "  'namespace': 'kfserving-test',\n",
       "  'resourceVersion': '663626',\n",
       "  'selfLink': '/apis/serving.kubeflow.org/v1alpha2/namespaces/kfserving-test/inferenceservices/product-recommender',\n",
       "  'uid': '01cb2e6d-d418-46d1-b74b-8f786d898ba8'},\n",
       " 'spec': {'canaryTrafficPercent': 10,\n",
       "  'default': {'predictor': {'minReplicas': 1,\n",
       "    'tensorflow': {'resources': {'limits': {'cpu': '1', 'memory': '2Gi'},\n",
       "      'requests': {'cpu': '1', 'memory': '2Gi'}},\n",
       "     'runtimeVersion': '2.4.0',\n",
       "     'storageUri': 's3://bucket-fex/0/00f118c323604546985c24871736d5d7/artifacts/saved_model/tfmodel/'}}}},\n",
       " 'status': {}}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KFServing.delete(MODEL_NAME, namespace=NAMESPACE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the Service\n",
    "\n",
    "To execute this cell, you need to have 'list_sessions_padded.npy', 'list_last_clicked.npy' and 'ID_Mapping.csv' files from the Notebook 'session_based_recommender_with_mlflow' in this directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(45916, 207, 52)\n",
      "(45916,)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Item_ID</th>\n",
       "      <th>Mapped_ID</th>\n",
       "      <th>category_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2402273</td>\n",
       "      <td>1</td>\n",
       "      <td>appliances.personal.massager</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>20100164</td>\n",
       "      <td>2</td>\n",
       "      <td>apparel.trousers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>21400264</td>\n",
       "      <td>3</td>\n",
       "      <td>electronics.clocks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1005239</td>\n",
       "      <td>4</td>\n",
       "      <td>construction.tools.light</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5100885</td>\n",
       "      <td>5</td>\n",
       "      <td>computers.notebook</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38510</th>\n",
       "      <td>38510</td>\n",
       "      <td>6902925</td>\n",
       "      <td>38511</td>\n",
       "      <td>electronics.telephone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38511</th>\n",
       "      <td>38511</td>\n",
       "      <td>2602249</td>\n",
       "      <td>38512</td>\n",
       "      <td>appliances.kitchen.refrigerators</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38512</th>\n",
       "      <td>38512</td>\n",
       "      <td>20100099</td>\n",
       "      <td>38513</td>\n",
       "      <td>apparel.trousers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38513</th>\n",
       "      <td>38513</td>\n",
       "      <td>21405267</td>\n",
       "      <td>38514</td>\n",
       "      <td>electronics.clocks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38514</th>\n",
       "      <td>38514</td>\n",
       "      <td>26405181</td>\n",
       "      <td>38515</td>\n",
       "      <td>computers.peripherals.printer</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>38515 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0   Item_ID  Mapped_ID                     category_code\n",
       "0               0   2402273          1      appliances.personal.massager\n",
       "1               1  20100164          2                  apparel.trousers\n",
       "2               2  21400264          3                electronics.clocks\n",
       "3               3   1005239          4          construction.tools.light\n",
       "4               4   5100885          5                computers.notebook\n",
       "...           ...       ...        ...                               ...\n",
       "38510       38510   6902925      38511             electronics.telephone\n",
       "38511       38511   2602249      38512  appliances.kitchen.refrigerators\n",
       "38512       38512  20100099      38513                  apparel.trousers\n",
       "38513       38513  21405267      38514                electronics.clocks\n",
       "38514       38514  26405181      38515     computers.peripherals.printer\n",
       "\n",
       "[38515 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "np.set_printoptions(precision=5)\n",
    "\n",
    "sessions_padded = np.load('list_sessions_padded.npy')\n",
    "print(sessions_padded.shape)\n",
    "last_clicked = np.load('list_last_clicked.npy')\n",
    "print(last_clicked.shape)\n",
    "n_output_features = int(last_clicked.max())\n",
    "n_unique_input_ids = int(sessions_padded[:, :, 0].max())\n",
    "window_length = sessions_padded.shape[1]\n",
    "n_input_features = sessions_padded.shape[2]\n",
    "\n",
    "id_mapping = pd.read_csv('ID_Mapping.csv')\n",
    "id_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04846978187561035\n",
      "Session:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_code</th>\n",
       "      <th>Item_ID</th>\n",
       "      <th>Item_ID_Mapped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801766</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801766</td>\n",
       "      <td>649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801806</td>\n",
       "      <td>1308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801806</td>\n",
       "      <td>1308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  category_code  Item_ID  Item_ID_Mapped\n",
       "0  appliances.personal.massager  1801766             649\n",
       "1  appliances.personal.massager  1801766             649\n",
       "2  appliances.personal.massager  1801806            1308\n",
       "3  appliances.personal.massager  1801806            1308"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction:\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_code</th>\n",
       "      <th>Item_ID</th>\n",
       "      <th>Item_ID_Mapped</th>\n",
       "      <th>probability</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801906</td>\n",
       "      <td>4134</td>\n",
       "      <td>0.227082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1802038</td>\n",
       "      <td>2743</td>\n",
       "      <td>0.178314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1802033</td>\n",
       "      <td>2781</td>\n",
       "      <td>0.130538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801841</td>\n",
       "      <td>18030</td>\n",
       "      <td>0.124758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>appliances.personal.massager</td>\n",
       "      <td>1801940</td>\n",
       "      <td>1369</td>\n",
       "      <td>0.122702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  category_code  Item_ID  Item_ID_Mapped  probability\n",
       "0  appliances.personal.massager  1801906            4134     0.227082\n",
       "1  appliances.personal.massager  1802038            2743     0.178314\n",
       "2  appliances.personal.massager  1802033            2781     0.130538\n",
       "3  appliances.personal.massager  1801841           18030     0.124758\n",
       "4  appliances.personal.massager  1801940            1369     0.122702"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ground Truth: 2743\n"
     ]
    }
   ],
   "source": [
    "def request_kf_serving(np_array, MODEL_NAME, NAMESPACE, INGRESS_HOST, INGRESS_PORT):\n",
    "    data = json.dumps({\"instances\": np_array.tolist()})\n",
    "    headers = {  # \"content-type\": \"application/json\",\n",
    "        'Host': '{}.{}.example.com'.format(MODEL_NAME, NAMESPACE)}\n",
    "    json_response = requests.post(\n",
    "        'http://{}:{}/v1/models/{}:predict'.format(INGRESS_HOST, INGRESS_PORT, MODEL_NAME), data=data, headers=headers)\n",
    "\n",
    "    try:\n",
    "        predictions = json.loads(json_response.text)['predictions']\n",
    "    except Exception as e:\n",
    "        raise e\n",
    "    return np.array(predictions).astype(np.float32)\n",
    "\n",
    "# When using Kubernetes Port Forwarding of the ingressgateway: kubectl port-forward --namespace istio-system svc/istio-ingressgateway 8080:80\n",
    "#INGRESS_HOST = 'localhost'\n",
    "#INGRESS_PORT = 8080\n",
    "\n",
    "\n",
    "INGRESS_HOST_LIST = !kubectl get po - l istio = ingressgateway - n istio-system - o jsonpath = {.items[0].status.hostIP}\n",
    "INGRESS_HOST = INGRESS_HOST_LIST[0]  # eg. '192.168.52.86'\n",
    "# print(INGRESS_HOST)\n",
    "INGRESS_PORT = 30083\n",
    "\n",
    "example = 6  # 5,6,7, 13\n",
    "start = time.time()\n",
    "\n",
    "pred = request_kf_serving(sessions_padded[example][np.newaxis, :, :],\n",
    "                          MODEL_NAME, NAMESPACE, INGRESS_HOST, INGRESS_PORT)\n",
    "end = time.time()\n",
    "print(end - start)\n",
    "\n",
    "top = pred.argsort()[0][::-1][:5]\n",
    "print(\"Session:\")\n",
    "session = pd.DataFrame()\n",
    "session['category_code'] = [id_mapping['category_code']\n",
    "                            [int(i)-1] for i in sessions_padded[example, :, 0] if i > 0]\n",
    "session['Item_ID'] = [id_mapping['Item_ID']\n",
    "                      [int(i)-1] for i in sessions_padded[example, :, 0] if i > 0]\n",
    "session['Item_ID_Mapped'] = [int(i)\n",
    "                             for i in sessions_padded[example, :, 0] if i > 0]\n",
    "session\n",
    "\n",
    "print(\"Prediction:\")\n",
    "prediction = pd.DataFrame()\n",
    "prediction['category_code'] = [\n",
    "    id_mapping['category_code'][int(i)-1] for i in top if i > 0]\n",
    "prediction['Item_ID'] = [id_mapping['Item_ID'][int(i)-1] for i in top if i > 0]\n",
    "prediction['Item_ID_Mapped'] = [int(i) for i in top if i > 0]\n",
    "prediction['probability'] = pred[0, top]\n",
    "prediction\n",
    "print(\"Ground Truth:\", last_clicked[example])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test autoscaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import threading\n",
    "\n",
    "while True:\n",
    "    for _ in range(10):\n",
    "        x = threading.Thread(target=request_kf_serving, args=(np.ones((1, 207, 52), dtype=np.float32),\n",
    "                                                              MODEL_NAME, NAMESPACE, INGRESS_HOST, INGRESS_PORT))\n",
    "        x.start()\n",
    "    time.sleep(0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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