code_sample/mobilenet/tf2_mobilenet_deploy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## TensorFlow 2.2 MobileNet 模型部署"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install tensorflow==2.2.0rc2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.0-rc2\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras as keras\n",
    "\n",
    "keras.backend.clear_session()  # For easy reset of notebook state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Error in loading the saved optimizer state. As a result, your model is starting with a freshly initialized optimizer.\n"
     ]
    }
   ],
   "source": [
    "# model_path = \"saved_model/mobilenet\"\n",
    "model_path = \"mobilenet_dog.h5\"\n",
    "\n",
    "model = keras.models.load_model(model_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建工作区（Workspace）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "notebook-gpu\n",
      "django-test\n",
      "japaneast\n",
      "b8cd4096-cfec-4e20-8b90-eddc24402703\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep='\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering model tf2-mobilenet\n",
      "Name: tf2-mobilenet\n",
      "Version: 4\n"
     ]
    }
   ],
   "source": [
    "from azureml.core import Model\n",
    "from azureml.core.resource_configuration import ResourceConfiguration\n",
    "\n",
    "model = Model.register(workspace=ws,\n",
    "                       model_name='tf2-mobilenet',\n",
    "                       model_path=model_path,\n",
    "                       model_framework=Model.Framework.TENSORFLOW,  # Framework used to create the model.\n",
    "                       resource_configuration=ResourceConfiguration(cpu=1, memory_in_gb=4))\n",
    "\n",
    "print('Name:', model.name)\n",
    "print('Version:', model.version)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.environment import Environment\n",
    "from azureml.core.model import InferenceConfig\n",
    "\n",
    "myenv = Environment(name = 'tf2-env')\n",
    "inference_config = InferenceConfig(runtime= \"python\", \n",
    "                                   entry_script=\"deploy/score.py\",\n",
    "                                   conda_file=\"deploy/infer_env.yml\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running......\n",
      "Succeeded\n",
      "AKS service creation operation finished, operation \"Succeeded\"\n",
      "Healthy\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.compute import AksCompute, ComputeTarget\n",
    "from azureml.core.webservice import Webservice, AksWebservice\n",
    "from azureml.exceptions import WebserviceException\n",
    "\n",
    "\n",
    "aks_target = AksCompute(ws, \"infer-aks\")\n",
    "service_name='tf2-mobilenet'\n",
    "\n",
    "try:\n",
    "    Webservice(ws, service_name).delete()\n",
    "except WebserviceException:\n",
    "    pass\n",
    "\n",
    "\n",
    "deployment_config = AksWebservice.deploy_configuration()\n",
    "\n",
    "\n",
    "service = Model.deploy(workspace=ws, \n",
    "                       name=service_name, \n",
    "                       models=[model], \n",
    "                       inference_config=inference_config, \n",
    "                       deployment_config=deployment_config, \n",
    "                       deployment_target=aks_target)\n",
    "\n",
    "service.wait_for_deployment(True)\n",
    "print(service.state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7oOBKj6LhXfhnxiruMacaATcYXh88e0e\n",
      "POST to url http://52.243.41.251:80/api/v1/service/tf2-mobilenet/score\n",
      "\"{\\\"result\\\": [[0.0, 1.0]]}\"\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "import json\n",
    "\n",
    "# Retrieve the API keys. Two keys were generated.\n",
    "key1, Key2 = service.get_keys()\n",
    "print(key1)\n",
    "\n",
    "data = json.dumps({'data': \"http://www.mgous.com/img/p/1905/28144928349/1_620_462.jpg\"})\n",
    "data = bytes(data, encoding='utf8')\n",
    "\n",
    "headers = {'Content-Type':'application/json', 'Authorization': 'Bearer ' + key1}\n",
    "\n",
    "resp = requests.post(service.scoring_uri, data=data, headers=headers)\n",
    "\n",
    "print(\"POST to url\", service.scoring_uri)\n",
    "print(resp.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "service.delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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