lab/01_CreateAlgorithmContainer/02_Testing our local model server.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First, let's  test the ping method (GET /ping)\n",
    "This method will be used by Sagemaker for health check our model. It must return a code **200**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from urllib import request\n",
    "\n",
    "base_url='http://localhost:8080'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "resp = request.urlopen(\"%s/ping\" % base_url)\n",
    "print(\"Response code: %d\" % resp.getcode() )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Then we can the predictions (POST /invocations)\n",
    "This method will be used by Sagemaker for the predictions. Here we're simulating the header parameter related to the CustomAttributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "from sagemaker.serializers import CSVSerializer\n",
    "csv_serializer = CSVSerializer()\n",
    "payloads = [\n",
    "    [4.6, 3.1, 1.5, 0.2], # 0\n",
    "    [7.7, 2.6, 6.9, 2.3], # 2\n",
    "    [6.1, 2.8, 4.7, 1.2]  # 1\n",
    "]\n",
    "\n",
    "def predict(payload):\n",
    "    headers = {\n",
    "        'Content-type': 'text/csv',\n",
    "        'Accept': 'text/csv'\n",
    "    }\n",
    "    \n",
    "    req = request.Request(\"%s/invocations\" % base_url, data=csv_serializer.serialize(payload).encode('utf-8'), headers=headers)\n",
    "    resp = request.urlopen(req)\n",
    "    print(\"Response code: %d, Prediction: %s\\n\" % (resp.getcode(), resp.read()))\n",
    "    for i in resp.headers:\n",
    "        print(i, resp.headers[i])\n",
    "\n",
    "for p in payloads:\n",
    "    predict(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Now, you can go back to the previous Jupyter, stop it and continue executing it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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