{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Host a custom built BYOC inference image   \n",
    "- Choose Python3 Data Science kernel in SageMaker Studio \n",
    "- Create a dummy model.tar.gz with nothing in it, and upload it to S3(Make sure SageMaker role has access to the bucket)\n",
    "- Use the script in the folder to built a docker image and push it to Amazon ECR\n",
    "- Run following code to create a SageMaker model with the dummy model file\n",
    "- A SageMaker endpoint will be created based on the MXNet model file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "from sagemaker.mxnet import MXNetModel\n",
    "from time import gmtime, strftime\n",
    "bucket_name = 'S3_BUCKET_NAME'\n",
    "s3_prefix = 'MODEL_FILE_KEY_PREFIX'\n",
    "model = MXNetModel(\n",
    "    entry_point='entrypoint.py',\n",
    "    model_data='s3://{}/{}/model.tar.gz'.format(bucket_name, s3_prefix),\n",
    "    framework_version='1.6.0',\n",
    "    py_version='py3',\n",
    "    source_dir='inference_src',#'source_directory'\n",
    "    image_uri='ACCOUNT.dkr.ecr.AWS_REGION.amazonaws.com/IMAGE_NAME',\n",
    "    role=sagemaker.get_execution_role(),\n",
    "    name='audio-embedding-model'+ strftime('%Y-%m-%d-%H-%M-%S', gmtime())\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ep_name = 'audio-embedding-endpoint'+ strftime('%Y-%m-%d-%H-%M-%S', gmtime())\n",
    "predictor = model.deploy(initial_instance_count=1, \n",
    "                        instance_type='ml.m5.xlarge',\n",
    "                        endpoint_name=ep_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test the endpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import json\n",
    "client = boto3.client('sagemaker-runtime')\n",
    "test_image = 'test_audio.mp3'\n",
    "img = open(test_image, 'rb').read()\n",
    "response = client.invoke_endpoint(\n",
    "    EndpointName=ep_name,\n",
    "    Body=img,\n",
    "    ContentType='application/x-image')\n",
    "\n",
    "result = response[\"Body\"].read().decode(\"utf-8\")\n",
    "detections = json.loads(result)\n",
    "print (detections)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "boto3.client('sagemaker').delete_endpoint(EndpointName=ep_name)"
   ]
  }
 ],
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