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   "source": [
    "# Packaging and Deploying our ML model to our edge devices - Using IoT jobs\n",
    "\n",
    "In this exercise, we will:\n",
    "\n",
    " - Invoke SageMaker NEO to compile the trained model \n",
    " - Create a deployment package with a signed model + the runtime used by SageMaker Edge Agent to load and invoke the optimized model\n",
    " - Deploy the package using IoT Jobs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import time\n",
    "import boto3\n",
    "import json\n",
    "import uuid\n",
    "\n",
    "s3_client = boto3.client('s3')\n",
    "sm_client = boto3.client('sagemaker')\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "role = sagemaker.get_execution_role()\n",
    "bucket_name = 'sagemaker-wind-turbine-farm-%s' % project_id\n",
    "\n",
    "prefix='wind_turbine_anomaly'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compiling our ML model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Invoking SageMaker NEO to compile the trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "compilation_job_name = 'wind-turbine-anomaly-%d' % int(time.time()*1000)\n",
    "sm_client.create_compilation_job(\n",
    "    CompilationJobName=compilation_job_name,\n",
    "    RoleArn=role,\n",
    "    InputConfig={\n",
    "        'S3Uri': '%s%s/output/model.tar.gz' % (estimator_output_path, training_job_name),\n",
    "        'DataInputConfig': '{\"input0\":[1,%d,10,10]}' % n_features,\n",
    "        'Framework': 'PYTORCH'\n",
    "    },\n",
    "    OutputConfig={\n",
    "        'S3OutputLocation': 's3://%s/wind_turbine/optimized/' % sagemaker_session.default_bucket(),        \n",
    "        'TargetPlatform': { 'Os': 'LINUX', 'Arch': 'X86_64' }\n",
    "    },\n",
    "    StoppingCondition={ 'MaxRuntimeInSeconds': 900 }\n",
    ")\n",
    "while True:\n",
    "    resp = sm_client.describe_compilation_job(CompilationJobName=compilation_job_name)    \n",
    "    if resp['CompilationJobStatus'] in ['STARTING', 'INPROGRESS']:\n",
    "        print('Running...')\n",
    "    else:\n",
    "        print(resp['CompilationJobStatus'], compilation_job_name)\n",
    "        break\n",
    "    time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building the Deployment Package SageMaker Edge Manager\n",
    "It will sign the model and create a deployment package with:\n",
    " - The optimized model\n",
    " - Model Metadata\n",
    " - SageMaker NEO runtime (dlr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_version = '1.0'\n",
    "model_name = 'WindTurbineAnomalyDetection'\n",
    "edge_packaging_job_name='wind-turbine-anomaly-%d' % int(time.time()*1000)\n",
    "\n",
    "resp = sm_client.create_edge_packaging_job(\n",
    "    EdgePackagingJobName=edge_packaging_job_name,\n",
    "    CompilationJobName=compilation_job_name,\n",
    "    ModelName=model_name,\n",
    "    ModelVersion=model_version,\n",
    "    RoleArn=role,\n",
    "    OutputConfig={\n",
    "        'S3OutputLocation': 's3://%s/%s/model/' % (bucket_name, prefix)\n",
    "    }\n",
    ")\n",
    "while True:\n",
    "    resp = sm_client.describe_edge_packaging_job(EdgePackagingJobName=edge_packaging_job_name)    \n",
    "    if resp['EdgePackagingJobStatus'] in ['STARTING', 'INPROGRESS']:\n",
    "        print('Running...')\n",
    "    else:\n",
    "        print(resp['EdgePackagingJobStatus'], compilation_job_name)\n",
    "        break\n",
    "    time.sleep(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy the package\n",
    "Using IoT Jobs, we will notify the Python application in the edge devices. The application will:\n",
    " - download the deployment package\n",
    " - unpack it\n",
    " - load the new mode (unload previous versions if any)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iot_client = boto3.client('iot')\n",
    "sts_client = boto3.client('sts')\n",
    "\n",
    "region_name = sagemaker_session.boto_session.region_name\n",
    "account_id = sts_client.get_caller_identity()[\"Account\"]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "resp = iot_client.create_job(\n",
    "    jobId=str(uuid.uuid4()),\n",
    "    targets=[\n",
    "        'arn:aws:iot:%s:%s:thinggroup/WindTurbineFarm-%s' % (region_name, account_id, project_id),        \n",
    "    ],\n",
    "    document=json.dumps({\n",
    "        'type': 'new_model',\n",
    "        'model_version': model_version,\n",
    "        'model_name': model_name,\n",
    "        'model_package_bucket': bucket_name,\n",
    "        'model_package_key': \"%s/model/%s-%s.tar.gz\" % (prefix, model_name, model_version)        \n",
    "    }),\n",
    "    targetSelection='SNAPSHOT'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let us restore variables stored in the previous exercise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%store -r project_id\n",
    "%store -r estimator_output_path\n",
    "%store -r training_job_name\n",
    "%store -r n_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alright! Now, the deployment process will start on the connected edge devices! \n",
    "You can start the Exercise #4: Run a simulated fleet of wind turbines and edge devices. Predict anomalies\n",
    " \n",
    " > [Exercise 04](../../04-Run-Fleet/greengrass-v2/04-run-fleet-iot-jobs.ipynb)\n"
   ]
  }
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