kubernetes_deploy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SDK version: 1.0.85\n"
     ]
    }
   ],
   "source": [
    "from azureml.core.model import Model\n",
    "import azureml.core\n",
    "\n",
    "print(\"SDK version:\", azureml.core.VERSION)\n",
    "\n",
    "from azureml.core import Workspace\n",
    "ws = Workspace.from_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Registering model Gen_Model\n"
     ]
    }
   ],
   "source": [
    "model = Model.register(model_path = \"./deploy_files\",\n",
    "                       model_name = \"Gen_Model\",\n",
    "                       description = \"Generative Model\",\n",
    "                       workspace = ws)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from azureml.core.model import InferenceConfig\n",
    "from azureml.core.environment import Environment\n",
    "from azureml.core.conda_dependencies import CondaDependencies\n",
    "\n",
    "# Create the environment\n",
    "myenv = Environment(name=\"myenv\")\n",
    "conda_dep = CondaDependencies().create(python_version='3.5')\n",
    "conda_dep.add_pip_package(\"azureml-defaults\")\n",
    "conda_dep.add_pip_package(\"torch\")\n",
    "conda_dep.add_pip_package(\"torchvision\")\n",
    "conda_dep.add_pip_package(\"tqdm\")\n",
    "# conda_dep.add_channel('rdkit')\n",
    "# conda_dep.add_channel('conda-forge')\n",
    "conda_dep.add_conda_package('rdkit')\n",
    "# conda_dep.add_conda_package('rdkit')\n",
    "myenv.python.conda_dependencies=conda_dep\n",
    "\n",
    "inference_config = InferenceConfig(entry_script=\"./score.py\", environment=myenv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CREATING THE AKS TARGET - DO NOT USE AGAIN\n",
    "\n",
    "# from azureml.core.compute import AksCompute, ComputeTarget\n",
    "\n",
    "# prov_config = AksCompute.provisioning_configuration(cluster_purpose = AksCompute.ClusterPurpose.DEV_TEST)\n",
    "\n",
    "# aks_name = 'aks-test'\n",
    "# aks_target = ComputeTarget.create(workspace = ws,\n",
    "#                                      name = aks_name,\n",
    "#                                      provisioning_configuration = prov_config)\n",
    "\n",
    "# aks_target.wait_for_completion(show_output = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR - Error, there is already a service with name myservice2 found in workspace ml-main\n",
      "\n"
     ]
    },
    {
     "ename": "WebserviceException",
     "evalue": "WebserviceException:\n\tMessage: Error, there is already a service with name myservice2 found in workspace ml-main\n\tInnerException None\n\tErrorResponse \n{\n    \"error\": {\n        \"message\": \"Error, there is already a service with name myservice2 found in workspace ml-main\"\n    }\n}",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mWebserviceException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/core/webservice/webservice.py\u001b[0m in \u001b[0;36m_check_for_existing_webservice\u001b[0;34m(workspace, name, overwrite)\u001b[0m\n\u001b[1;32m    494\u001b[0m                 raise WebserviceException('Error, there is already a service with name {} found in '\n\u001b[0;32m--> 495\u001b[0;31m                                           'workspace {}'.format(name, workspace._workspace_name))\n\u001b[0m\u001b[1;32m    496\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mWebserviceException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mWebserviceException\u001b[0m: WebserviceException:\n\tMessage: Error, there is already a service with name myservice2 found in workspace ml-main\n\tInnerException None\n\tErrorResponse \n{\n    \"error\": {\n        \"message\": \"Error, there is already a service with name myservice2 found in workspace ml-main\"\n    }\n}",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mWebserviceException\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-e58409d6e869>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mdeployment_config\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAksWebservice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeploy_configuration\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcpu_cores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmemory_gb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mservice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeploy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"myservice2\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minference_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdeployment_config\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maks_target\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mservice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait_for_deployment\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mshow_output\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mservice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/core/model.py\u001b[0m in \u001b[0;36mdeploy\u001b[0;34m(workspace, name, models, inference_config, deployment_config, deployment_target, overwrite)\u001b[0m\n\u001b[1;32m   1569\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1570\u001b[0m         \u001b[0mwebservice_name_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1571\u001b[0;31m         \u001b[0mWebservice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_for_existing_webservice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mworkspace\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moverwrite\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1572\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1573\u001b[0m         \u001b[0;31m# Local webservice.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda/envs/azureml_py36/lib/python3.6/site-packages/azureml/core/webservice/webservice.py\u001b[0m in \u001b[0;36m_check_for_existing_webservice\u001b[0;34m(workspace, name, overwrite)\u001b[0m\n\u001b[1;32m    496\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mWebserviceException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    497\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m'WebserviceNotFound'\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 498\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mWebserviceException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodule_logger\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    499\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    500\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mWebserviceException\u001b[0m: WebserviceException:\n\tMessage: Error, there is already a service with name myservice2 found in workspace ml-main\n\tInnerException None\n\tErrorResponse \n{\n    \"error\": {\n        \"message\": \"Error, there is already a service with name myservice2 found in workspace ml-main\"\n    }\n}"
     ]
    }
   ],
   "source": [
    "from azureml.core.webservice import AksWebservice, Webservice\n",
    "from azureml.core.model import Model\n",
    "\n",
    "aks_target = AksCompute(ws,\"aks-test\")\n",
    "\n",
    "deployment_config = AksWebservice.deploy_configuration(cpu_cores = 1, memory_gb = 1)\n",
    "service = Model.deploy(ws, \"myservice\", [model], inference_config, deployment_config, aks_target)\n",
    "service.wait_for_deployment(show_output = True)\n",
    "print(service.state)\n",
    "print(service.get_logs())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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