aws_marketplace/using_model_packages/amazon_demo_product/Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# AWS Marketplace Product Usage Demonstration - Model Packages\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "This notebook's CI test result for us-west-2 is as follows. CI test results in other regions can be found at the end of the notebook. \n",
    "\n",
    "![This us-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-2/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Using Model Package ARN with Amazon SageMaker APIs\n",
    "\n",
    "This sample notebook demonstrates two new functionalities added to Amazon SageMaker:\n",
    "1. Using a Model Package ARN for inference via Batch Transform jobs / Live Endpoints\n",
    "2. Using a Marketplace Model Package ARN - we will use [Scikit Decision Trees - Pretrained Model](https://aws.amazon.com/marketplace/pp/prodview-7qop4x5ahrdhe?qid=1543169069960&sr=0-2&ref_=srh_res_product_title)\n",
    "\n",
    "\n",
    "## Overall flow diagram\n",
    "<img src=\"images/ModelPackageE2EFlow.jpg\">\n",
    "\n",
    "## Compatibility\n",
    "This notebook is compatible only with [Scikit Decision Trees - Pretrained Model](https://aws.amazon.com/marketplace/pp/prodview-7qop4x5ahrdhe?qid=1543169069960&sr=0-2&ref_=srh_res_product_title) sample model that is published to AWS Marketplace\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker as sage\n",
    "from sagemaker import get_execution_role\n",
    "from sagemaker.serializers import CSVSerializer\n",
    "\n",
    "role = get_execution_role()\n",
    "\n",
    "# S3 prefixes\n",
    "common_prefix = \"DEMO-scikit-byo-iris\"\n",
    "batch_inference_input_prefix = common_prefix + \"/batch-inference-input-data\"\n",
    "from sagemaker.predictor import Predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the session\n",
    "\n",
    "The session remembers our connection parameters to Amazon SageMaker. We'll use it to perform all of our Amazon SageMaker operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker_session = sage.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Model\n",
    "\n",
    "Now we use the above Model Package to create a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from src.scikit_product_arns import ScikitArnProvider\n",
    "\n",
    "modelpackage_arn = ScikitArnProvider.get_model_package_arn(sagemaker_session.boto_region_name)\n",
    "print(\"Using model package arn \" + modelpackage_arn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker import ModelPackage\n",
    "\n",
    "model = ModelPackage(\n",
    "    role=role, model_package_arn=modelpackage_arn, sagemaker_session=sagemaker_session\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Transform Job\n",
    "\n",
    "Now let's use the model built to run a batch inference job and verify it works.\n",
    "\n",
    "### Batch Transform Input Preparation\n",
    "\n",
    "The snippet below is removing the \"label\" column (column indexed at 0) and retaining the rest to be batch transform's input. \n",
    "\n",
    "NOTE: This is the same training data, which is a no-no from a statistical/ML science perspective. But the aim of this notebook is to demonstrate how things work end-to-end."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "## Remove first column that contains the label\n",
    "shape = pd.read_csv(\"data/training/iris.csv\", header=None).drop([0], axis=1)\n",
    "\n",
    "TRANSFORM_WORKDIR = \"data/transform\"\n",
    "shape.to_csv(TRANSFORM_WORKDIR + \"/batchtransform_test.csv\", index=False, header=False)\n",
    "\n",
    "transform_input = (\n",
    "    sagemaker_session.upload_data(TRANSFORM_WORKDIR, key_prefix=batch_inference_input_prefix)\n",
    "    + \"/batchtransform_test.csv\"\n",
    ")\n",
    "print(\"Transform input uploaded to \" + transform_input)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import uuid\n",
    "\n",
    "transformer = model.transformer(1, \"ml.m4.xlarge\")\n",
    "transformer.transform(transform_input, content_type=\"text/csv\")\n",
    "transformer.wait()\n",
    "\n",
    "print(\"Batch Transform output saved to \" + transformer.output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect the Batch Transform Output in S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from urllib.parse import urlparse\n",
    "\n",
    "parsed_url = urlparse(transformer.output_path)\n",
    "bucket_name = parsed_url.netloc\n",
    "file_key = \"{}/{}.out\".format(parsed_url.path[1:], \"batchtransform_test.csv\")\n",
    "\n",
    "s3_client = sagemaker_session.boto_session.client(\"s3\")\n",
    "\n",
    "response = s3_client.get_object(Bucket=sagemaker_session.default_bucket(), Key=file_key)\n",
    "response_bytes = response[\"Body\"].read().decode(\"utf-8\")\n",
    "print(response_bytes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Live Inference Endpoint\n",
    "\n",
    "Now we demonstrate the creation of an endpoint for live inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint_name = \"scikit-model\"\n",
    "predictor = model.deploy(1, \"ml.m4.xlarge\", endpoint_name=endpoint_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Choose some data and use it for a prediction\n",
    "\n",
    "In order to do some predictions, we'll extract some of the data we used for training and do predictions against it. This is, of course, bad statistical practice, but a good way to see how the mechanism works.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TRAINING_WORKDIR = \"data/training\"\n",
    "shape = pd.read_csv(TRAINING_WORKDIR + \"/iris.csv\", header=None)\n",
    "\n",
    "import itertools\n",
    "\n",
    "a = [50 * i for i in range(3)]\n",
    "b = [40 + i for i in range(10)]\n",
    "indices = [i + j for i, j in itertools.product(a, b)]\n",
    "\n",
    "test_data = shape.iloc[indices[:-1]]\n",
    "test_X = test_data.iloc[:, 1:]\n",
    "test_y = test_data.iloc[:, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = Predictor(\n",
    "    endpoint_name=endpoint_name, sagemaker_session=None, serializer=CSVSerializer()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(predictor.predict(test_X.values).decode(\"utf-8\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleanup endpoint\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.sagemaker_session.delete_endpoint(endpoint_name)\n",
    "model.sagemaker_session.delete_endpoint_config(endpoint_name)\n",
    "model.delete_model()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notebook CI Test Results\n",
    "\n",
    "This notebook was tested in multiple regions. The test results are as follows, except for us-west-2 which is shown at the top of the notebook.\n",
    "\n",
    "![This us-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-east-2/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/us-west-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ca-central-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/sa-east-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-2/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-west-3/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-central-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/eu-north-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-southeast-2/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-northeast-2/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n",
    "\n",
    "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://prod.us-west-2.tcx-beacon.docs.aws.dev/sagemaker-nb/ap-south-1/aws_marketplace|using_model_packages|amazon_demo_product|Using_ModelPackage_Arn_From_AWS_Marketplace.ipynb)\n"
   ]
  }
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