sagemaker-customer-segmentation-model-package.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup the environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import sagemaker as sage\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "role = get_execution_role()\n",
    "sess = sage.Session()\n",
    "model_package_arn='<REPLACE WITH MODEL ARN>'\n",
    "s3_base_path='s3://<REPLACE WITH BUCKET NAME>'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Realtime Inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from sagemaker import ModelPackage\n",
    "model_name='customer-segmentation'\n",
    "model = ModelPackage(name=model_name,\n",
    "                     role=role,\n",
    "                     model_package_arn=model_package_arn,\n",
    "                     sagemaker_session=sagemaker_session)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint_name = 'customer-segmentation'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "predictor = model.deploy(1, 'ml.m5.large', endpoint_name=endpoint_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect to the endpoint\n",
    "predictor = sage.predictor.RealTimePredictor(\n",
    "    endpoint_name,\n",
    "    sagemaker_session=sagemaker_session\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# open s3 file\n",
    "import io\n",
    "import boto3\n",
    "res = \"\"\n",
    "s3_client = boto3.client('s3')\n",
    "s3_response_object = s3_client.get_object(Bucket='<REPLACE WITH BUCKET NAME>', Key='input/input_data.csv')\n",
    "object_content = s3_response_object['Body'].read()\n",
    "\n",
    "res = predictor.predict(object_content)\n",
    "print(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete endpoint\n",
    "predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Batch Transform Job "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_output_path=s3_base_path + '/output/'\n",
    "transformer = sage.transformer.Transformer(\n",
    "    base_transform_job_name='customer-segmentation-batch-transform',\n",
    "    model_name=model_name,\n",
    "    instance_count=1,\n",
    "    instance_type='ml.m5.large',\n",
    "    output_path=s3_output_path,\n",
    "    sagemaker_session=sagemaker_session\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "transformer.transform(\n",
    "    s3_base_path + '/input/'\n",
    ")\n",
    "# wait until prediction job is completed\n",
    "transformer.wait()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Inspect the Batch Transform Output in S3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_client = sage.s3.S3Downloader\n",
    "s3_client.read_file(s3_base_path + '/output/result.csv')"
   ]
  }
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