03-devops-pipeline-build.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "import shutil\n",
    "import os\n",
    "from glob import glob\n",
    "import re\n",
    "import mlflow.azureml\n",
    "from mlflow.keras import load_model\n",
    "from azureml.core import Workspace\n",
    "from azureml.core.webservice import Webservice\n",
    "from azureml.core.authentication import ServicePrincipalAuthentication"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get all enviroment variables\n",
    "account_name = os.getenv(\"account_name\")\n",
    "account_key = os.getenv(\"account_key\")\n",
    "workspace_name = os.getenv('workspace_name')\n",
    "subscription_id = os.getenv('subscription_id')\n",
    "resource_group = os.getenv('resource_group')\n",
    "location = os.getenv('location')\n",
    "tenant_id = os.getenv('tenant_id')\n",
    "service_principal_id = os.getenv('service_principal_id')\n",
    "service_principal_password = os.getenv('service_principal_password')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get latest model, the one with highest version indicated by v<versionNumber> in the end of the model name\n",
    "models = glob('models/*')\n",
    "models_versions = [(float(x[x.rfind('v')+1:-4]), x) for x in models]\n",
    "lastVersion = max(models_versions)[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_local_dir = 'model'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# unpack the model\n",
    "shutil.unpack_archive(lastVersion, model_local_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# test if model is loaded properly\n",
    "load_model(model_local_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename as not all symbols are allowed by azure\n",
    "fileName = lastVersion[lastVersion.find('/')+1:-4]\n",
    "fileName = re.sub(r'[^-\\.a-z0-9]', '-', fileName.lower())\n",
    "image_name = re.sub(r'\\W+', '', fileName) + '-image'\n",
    "model_name  = re.sub(r'\\W+', '', fileName) + '-model'\n",
    "deploy_name = re.sub(r'\\W+', '', fileName) + '-deploy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create authorization object\n",
    "auth = ServicePrincipalAuthentication(\n",
    "    tenant_id=tenant_id,\n",
    "    service_principal_id=service_principal_id,\n",
    "    service_principal_password= service_principal_password)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create/get workspace\n",
    "azure_workspace = Workspace.create(\n",
    "    name=workspace_name,\n",
    "    subscription_id=subscription_id,\n",
    "    resource_group=resource_group,\n",
    "    location=location,\n",
    "    create_resource_group=True,\n",
    "    exist_ok=True,\n",
    "    auth = auth)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the image and save in azure container registry\n",
    "azure_image, azure_model = mlflow.azureml.build_image(\n",
    "    model_uri=model_local_dir,\n",
    "    workspace=azure_workspace,\n",
    "    synchronous=True,\n",
    "    image_name = image_name,\n",
    "    model_name = model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# deploy container instance from the image\n",
    "webservice = Webservice.deploy_from_image(\n",
    "    image = azure_image, \n",
    "    workspace=azure_workspace, \n",
    "    name=deploy_name)\n",
    "webservice.wait_for_deployment()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the scoring endpoint in the file, just for convince of not scrolling all the way down in logs\n",
    "with open('scoring_path', 'w') as f: \n",
    "    f.write(webservice.scoring_uri) "
   ]
  }
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