main-generic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MIT License\n",
    "\n",
    "# Copyright (c) 2021 HZ-MS-CSA\n",
    "\n",
    "# Permission is hereby granted, free of charge, to any person obtaining a copy\n",
    "# of this software and associated documentation files (the \"Software\"), to deal\n",
    "# in the Software without restriction, including without limitation the rights\n",
    "# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n",
    "# copies of the Software, and to permit persons to whom the Software is\n",
    "# furnished to do so, subject to the following conditions:\n",
    "\n",
    "# The above copyright notice and this permission notice shall be included in all\n",
    "# copies or substantial portions of the Software.\n",
    "\n",
    "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
    "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
    "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n",
    "# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
    "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n",
    "# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n",
    "# SOFTWARE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Scoring"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.649626Z",
     "start_time": "2021-03-18T17:17:02.169967Z"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import pandas as pd\n",
    "import os\n",
    "import pickle\n",
    "import joblib\n",
    "import os\n",
    "from azureml.core.workspace import Workspace\n",
    "from azureml.core.model import Model\n",
    "from azureml.core.authentication import ServicePrincipalAuthentication\n",
    "from flask import Flask\n",
    "from flask_restful import Resource, Api, reqparse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.754817Z",
     "start_time": "2021-03-18T17:17:09.657026Z"
    }
   },
   "outputs": [],
   "source": [
    "# Use Service Principal\n",
    "\n",
    "sp = ServicePrincipalAuthentication(tenant_id=os.environ['TENANT_ID'], # tenantID\n",
    "                                    service_principal_id=os.environ['CLIENT_ID'], # clientId\n",
    "                                    service_principal_password=os.environ['CLIENT_SECRET']) # clientSecret\n",
    "\n",
    "ws = Workspace.get(name=os.environ['WORKSPACE_NAME'],\n",
    "                   auth=sp,\n",
    "                   subscription_id=os.environ['SUBSCRIPTION_ID'],\n",
    "                  resource_group=os.environ['RESOURCE_GROUP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.757276Z",
     "start_time": "2021-03-18T17:17:02.196Z"
    }
   },
   "outputs": [],
   "source": [
    "def score(raw_data, model_name):\n",
    "    # Get predictions and explanations for each data point\n",
    "    data = pd.read_json(raw_data)\n",
    "    \n",
    "    model_path = Model.get_model_path(model_name, version=None, _workspace=ws)\n",
    "\n",
    "    model = joblib.load(model_path)\n",
    "\n",
    "    # Make prediction\n",
    "    predictions = model.predict(data)\n",
    "\n",
    "    # You can return any data type as long as it is JSON-serializable\n",
    "    return {'results': predictions.tolist()}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.758179Z",
     "start_time": "2021-03-18T17:17:02.197Z"
    }
   },
   "outputs": [],
   "source": [
    "app = Flask(__name__)\n",
    "api = Api(app)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.758883Z",
     "start_time": "2021-03-18T17:17:02.198Z"
    }
   },
   "outputs": [],
   "source": [
    "parser = reqparse.RequestParser()\n",
    "parser.add_argument('data', location='json')\n",
    "parser.add_argument('model_name', required=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:17:09.759701Z",
     "start_time": "2021-03-18T17:17:02.199Z"
    }
   },
   "outputs": [],
   "source": [
    "class Score(Resource):\n",
    "    def get(self):\n",
    "        data = 'TEST_MESSAGE'\n",
    "        return {'data': data}, 200  # return data and 200 OK code\n",
    "    \n",
    "    def post(self):\n",
    "        args = parser.parse_args()\n",
    "        data = args['data']\n",
    "        model_name = args['model_name']\n",
    "        results = score(data, model_name)\n",
    "        return results, 200  # return data with 200 OK\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-18T17:16:57.011084Z",
     "start_time": "2021-03-18T17:16:56.832203Z"
    }
   },
   "outputs": [],
   "source": [
    "class HealthCheck(Resource):\n",
    "    def get(self):\n",
    "        details = json.dumps(ws.get_details())\n",
    "        return {'HealthStatus':'Okay', 'WorkspaceDetails': details}, 200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-17T23:10:31.661106Z",
     "start_time": "2021-03-17T23:10:31.656981Z"
    }
   },
   "outputs": [],
   "source": [
    "api.add_resource(Score, '/score')\n",
    "api.add_resource(HealthCheck, '/healthcheck')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-17T23:13:38.807321Z",
     "start_time": "2021-03-17T23:10:32.470881Z"
    }
   },
   "outputs": [],
   "source": [
    "if __name__ == '__main__':\n",
    "    app.run(host='0.0.0.0', port=5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "hzfairlearn",
   "language": "python",
   "name": "hzfairlearn"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}