deploy your model on sagemaker and check if it works

deploy_model_Sage.ipynb

{
 "cells": [  
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor = estimator.deploy(initial_instance_count=1, \n",
    "                             instance_type='ml.p2.xlarge',\n",
    "                             endpoint_name='founder-classifier-endpoint')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n",
    "\n",
    "#lets test if the endpoint works by throwing it some image\n",
    "location='./data/bing/Jeff Bezos/faces_28x28/Jeff_Bezos_100.jpg'\n",
    "image=cv2.imread(location, cv2.IMREAD_GRAYSCALE)\n",
    "image=np.array(image, dtype='float64').reshape([-1,28,28,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictor.predict({'x': image})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "sagemaker.Session().delete_endpoint(predictor.endpoint)"
   ]
  }
 ],
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