{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Integrate Instant Recommendations With Your Existing Python SDK Model Workflows\n"
   ]
  },
  {
   "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://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-2/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* SDK https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.model.Model.deploy\n",
    "* boto3 https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/sagemaker.html#client\n",
    "\n",
    "In this notebook we will demonstrate the following Python SDK workflow:\n",
    "1. Download the Model & Payload\n",
    "2. Create the Python SDK model\n",
    "3. Retrieve the Deployment Recommendation ID\n",
    "4. (Optional) Display the recommendations\n",
    "5. Deploy using a Deployment Recommendation ID\n",
    "6. Perform inference off of the deployed model\n",
    "7. Clean up\n",
    "\n",
    "When demonstrating this Python SDK workflow, we will be using an SKLearn model.\n",
    "\n",
    "**This sample is provided for demonstration purposes, make sure to conduct appropriate testing if deriving this code for your own use-cases!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 0: Make sure that you're using the latest Python SDK and Boto3 (Feel free to skip if already on latest versions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install --upgrade pip botocore boto3 sagemaker"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up Client and Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "import boto3\n",
    "import time\n",
    "\n",
    "region = boto3.Session().region_name\n",
    "role = sagemaker.get_execution_role()\n",
    "sm_client = boto3.client(\"sagemaker\", region_name=region)\n",
    "sagemaker_session = sagemaker.Session()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Download the Model & Payload\n",
    "\n",
    "In this example, we are using a pre-trained scikit-learn model, trained on the California Housing dataset, present in Scikit-Learn: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_california_housing.html. The California Housing dataset was originally published in:\n",
    "\n",
    "> Pace, R. Kelley, and Ronald Barry. \"Sparse spatial auto-regressions.\" Statistics & Probability Letters 33.3 (1997): 291-297."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "export_dir = \"./model/\"\n",
    "\n",
    "if not os.path.exists(export_dir):\n",
    "    os.makedirs(export_dir)\n",
    "    print(\"Directory \", export_dir, \" Created \")\n",
    "else:\n",
    "    print(\"Directory \", export_dir, \" already exists\")\n",
    "\n",
    "model_archive_name = \"sk-model.tar.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3 = boto3.client(\"s3\")\n",
    "s3.download_file(\n",
    "    f\"sagemaker-example-files-prod-{region}\",\n",
    "    \"models/california-housing/model.joblib\",\n",
    "    export_dir + \"model.joblib\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Tar the model and code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!tar -cvpzf {model_archive_name} -C ./model \"model.joblib\" -C ../code \"inference.py\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download the payload "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "payload_location = \"./sample-payload/\"\n",
    "\n",
    "if not os.path.exists(payload_location):\n",
    "    os.makedirs(payload_location)\n",
    "    print(\"Directory \", payload_location, \" Created \")\n",
    "else:\n",
    "    print(\"Directory \", payload_location, \" already exists\")\n",
    "\n",
    "payload_archive_name = \"sk_payload.tar.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd\n",
    "\n",
    "data = fetch_california_housing()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    data.data, data.target, test_size=0.25, random_state=42\n",
    ")\n",
    "\n",
    "# we don't train a model, so we will need only the testing data\n",
    "testX = pd.DataFrame(X_test, columns=data.feature_names)\n",
    "# Save testing data to CSV\n",
    "testX[data.feature_names].head(10).to_csv(\n",
    "    os.path.join(payload_location, \"test_data.csv\"), header=False, index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tar the payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cd ./sample-payload/ && tar czvf ../{payload_archive_name} *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Upload Your model and payload to S3\n",
    "\n",
    "We will be uploading the pretrained model and corresponding test set as `sk-model.tar.gz` and as `sk_payload.tar.gz` to S3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prefix = \"sagemaker/scikit-learn-inference-recommender\"\n",
    "\n",
    "model_url = sagemaker_session.upload_data(model_archive_name, key_prefix=prefix)\n",
    "sample_payload_url = sagemaker_session.upload_data(payload_archive_name, key_prefix=prefix)\n",
    "\n",
    "print(\"model uploaded to: {}\".format(model_url))\n",
    "print(\"sample payload uploaded to: {}\".format(sample_payload_url))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Create the Python SDK model\n",
    "\n",
    "In this example we will be using a Python SDK `SKLearnModel` model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.model import Model\n",
    "from sagemaker import image_uris\n",
    "\n",
    "model = Model(\n",
    "    model_data=model_url,\n",
    "    role=role,\n",
    "    image_uri=image_uris.retrieve(\n",
    "        framework=\"sklearn\", region=region, version=\"1.0-1\", image_scope=\"inference\"\n",
    "    ),\n",
    "    entry_point=\"./code/inference.py\",\n",
    "    sagemaker_session=sagemaker_session,\n",
    ")\n",
    "\n",
    "model.create()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Retrieve the Deployment Recommendation ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datetime import datetime, timedelta\n",
    "\n",
    "model_name = model.name\n",
    "\n",
    "completed = timed_out = False\n",
    "\n",
    "start = datetime.today()\n",
    "max_duration = start + timedelta(minutes=1)\n",
    "\n",
    "while not completed and not timed_out:\n",
    "    describe_model_response = sm_client.describe_model(ModelName=model_name)\n",
    "    deployment_recommendation = describe_model_response.get(\"DeploymentRecommendation\")\n",
    "\n",
    "    completed = (\n",
    "        deployment_recommendation is not None\n",
    "        and \"COMPLETED\" == deployment_recommendation.get(\"RecommendationStatus\")\n",
    "    )\n",
    "\n",
    "    time.sleep(0.5)\n",
    "    timed_out = datetime.today() > max_duration\n",
    "\n",
    "    print(datetime.today(), \"DeploymentRecommendation:\", deployment_recommendation)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## (Optional) Step 4: Display the recommendations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_colwidth\", None)\n",
    "df = pd.DataFrame(\n",
    "    deployment_recommendation.get(\"RealTimeInferenceRecommendations\"),\n",
    "    columns=[\"RecommendationId\", \"InstanceType\", \"Environment\"],\n",
    ")\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5: Deploy using a Deployment Recommendation ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import uuid\n",
    "from sagemaker.sklearn.model import SKLearnPredictor\n",
    "\n",
    "recommendation_id = deployment_recommendation.get(\"RealTimeInferenceRecommendations\")[0].get(\n",
    "    \"RecommendationId\"\n",
    ")\n",
    "\n",
    "endpoint_name = \"notebook-test-\" + str(uuid.uuid4())\n",
    "\n",
    "model.predictor_cls = SKLearnPredictor\n",
    "\n",
    "predictor = model.deploy(\n",
    "    inference_recommendation_id=recommendation_id,\n",
    "    initial_instance_count=1,\n",
    "    endpoint_name=endpoint_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6: Produce Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "payload = pd.read_csv(\"./sample-payload/test_data.csv\")\n",
    "\n",
    "inference = predictor.predict(payload)\n",
    "\n",
    "print(inference)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7: Cleanup\n",
    "\n",
    "Cleanup the resources if needed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.delete_model()\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "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://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This us-east-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-east-2/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This us-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/us-west-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ca-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ca-central-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This sa-east-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/sa-east-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This eu-west-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-1/sagemaker-inference-recommender|python|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This eu-west-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-2/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This eu-west-3 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-west-3/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This eu-central-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-central-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This eu-north-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/eu-north-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ap-southeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-southeast-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ap-southeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-southeast-2/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ap-northeast-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-northeast-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ap-northeast-2 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-northeast-2/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n",
    "\n",
    "![This ap-south-1 badge failed to load. Check your device's internet connectivity, otherwise the service is currently unavailable](https://h75twx4l60.execute-api.us-west-2.amazonaws.com/sagemaker-nb/ap-south-1/sagemaker-inference-recommender|python-sdk|instant-recommendations-with-python-sdk.ipynb)\n"
   ]
  }
 ],
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