project 4-v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deploy and monitor a machine learning workflow for Image Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up this notebook\n",
    "\n",
    "Notes about the instance size and kernel setup: this notebook has been tested on\n",
    "\n",
    "1. The `Python 3 (Data Science)` kernel\n",
    "2. The `ml.t3.medium` Sagemaker notebook instance\n",
    "\n",
    "## Data Staging\n",
    "\n",
    "We'll use a sample dataset called CIFAR to simulate the challenges Scones Unlimited are facing in Image Classification. In order to start working with CIFAR we'll need to:\n",
    "\n",
    "1. Extract the data from a hosting service\n",
    "2. Transform it into a usable shape and format\n",
    "3. Load it into a production system\n",
    "\n",
    "In other words, we're going to do some simple ETL!\n",
    "\n",
    "### 1. Extract the data from the hosting service\n",
    "\n",
    "In the cell below, define a function `extract_cifar_data` that extracts python version of the CIFAR-100 dataset. The CIFAR dataaset is open source and generously hosted by the University of Toronto at: https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "CIFAR_URL=\"https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "def extract_cifar_data(url=\"https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz\", filename=\"cifar.tar.gz\"):\n",
    "    \"\"\"A function for extracting the CIFAR-100 dataset and storing it as a gzipped file\n",
    "    \n",
    "    Arguments:\n",
    "    url      -- the URL where the dataset is hosted\n",
    "    filename -- the full path where the dataset will be written\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    # Todo: request the data from the data url (done)\n",
    "    # Hint: use `requests.get` method\n",
    "    r = requests.get(url)\n",
    "    with open(filename, \"wb\") as file_context:\n",
    "        file_context.write(r.content)\n",
    "    return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's test it out! Run the following cell and check whether a new file `cifar.tar.gz` is created in the file explorer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "extract_cifar_data(CIFAR_URL)     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Transform the data into a usable shape and format\n",
    "\n",
    "Clearly, distributing the data as a gzipped archive makes sense for the hosting service! It saves on bandwidth, storage, and it's a widely-used archive format. In fact, it's so widely used that the Python community ships a utility for working with them, `tarfile`, as part of its Standard Library. Execute the following cell to decompress your extracted dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tarfile\n",
    "\n",
    "with tarfile.open(\"cifar.tar.gz\", \"r:gz\") as tar:\n",
    "    tar.extractall()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A new folder `cifar-100-python` should be created, containing `meta`, `test`, and `train` files. These files are `pickles` and the [CIFAR homepage](https://www.cs.toronto.edu/~kriz/cifar.html) provides a simple script that can be used to load them. We've adapted the script below for you to run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "with open(\"./cifar-100-python/meta\", \"rb\") as f:\n",
    "    dataset_meta = pickle.load(f, encoding='bytes')\n",
    "\n",
    "with open(\"./cifar-100-python/test\", \"rb\") as f:\n",
    "    dataset_test = pickle.load(f, encoding='bytes')\n",
    "\n",
    "with open(\"./cifar-100-python/train\", \"rb\") as f:\n",
    "    dataset_train = pickle.load(f, encoding='bytes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Feel free to explore the datasets\n",
    "dataset_train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_test.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'fine_label_names', b'coarse_label_names'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_meta.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As documented on the homepage, `b'data'` contains rows of 3073 unsigned integers, representing three channels (red, green, and blue) for one 32x32 pixel image per row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3072"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "32*32*3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a simple gut-check, let's transform one of our images. Each 1024 items in a row is a channel (red, green, then blue). Each 32 items in the channel are a row in the 32x32 image. Using python, we can stack these channels into a 32x32x3 array, and save it as a PNG file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Each 1024 in a row is a channel (red, green, then blue)\n",
    "row = dataset_train[b'data'][0]\n",
    "red, green, blue = row[0:1024], row[1024:2048], row[2048:]\n",
    "\n",
    "# Each 32 items in the channel are a row in the 32x32 image\n",
    "red = red.reshape(32,32)\n",
    "green = green.reshape(32,32)\n",
    "blue = blue.reshape(32,32)\n",
    "\n",
    "# Combine the channels into a 32x32x3 image!\n",
    "combined = np.dstack((red,green,blue))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a more concise version, consider the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All in one:\n",
    "test_image = np.dstack((\n",
    "    row[0:1024].reshape(32,32),\n",
    "    row[1024:2048].reshape(32,32),\n",
    "    row[2048:].reshape(32,32)\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(test_image);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like a cow! Let's check the label. `dataset_meta` contains label names in order, and `dataset_train` has a list of labels for each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train[b'fine_labels'][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our image has a label of `19`, so let's see what the 19th item is in the list of label names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'cattle'\n"
     ]
    }
   ],
   "source": [
    "print(dataset_meta[b'fine_label_names'][19])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok! 'cattle' sounds about right. By the way, using the previous two lines we can do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'cattle'\n"
     ]
    }
   ],
   "source": [
    "n = 0\n",
    "print(dataset_meta[b'fine_label_names'][dataset_train[b'fine_labels'][n]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we know how to check labels, is there a way that we can also check file names? `dataset_train` also contains a `b'filenames'` key. Let's see what we have here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'bos_taurus_s_000507.png'\n"
     ]
    }
   ],
   "source": [
    "print(dataset_train[b'filenames'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\"Taurus\" is the name of a subspecies of cattle, so this looks like a pretty reasonable filename. To save an image we can also do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.imsave(\"file.png\", test_image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your new PNG file should now appear in the file explorer -- go ahead and pop it open to see!\n",
    "\n",
    "Now that you know how to reshape the images, save them as files, and capture their filenames and labels, let's just capture all the bicycles and motorcycles and save them. Scones Unlimited can use a model that tells these apart to route delivery drivers automatically.\n",
    "\n",
    "In the following cell, identify the label numbers for Bicycles and Motorcycles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'fine_label_names', b'coarse_label_names'])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_meta.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'apple'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'aquarium_fish'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'baby'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'bear'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'beaver'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             labels\n",
       "0          b'apple'\n",
       "1  b'aquarium_fish'\n",
       "2           b'baby'\n",
       "3           b'bear'\n",
       "4         b'beaver'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Retrieving labels name\n",
    "df_meta = pd.DataFrame({\n",
    "    \"labels\": dataset_meta[b\"fine_label_names\"],\n",
    "})\n",
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 100 entries, 0 to 99\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   labels  100 non-null    object\n",
      "dtypes: object(1)\n",
      "memory usage: 928.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_meta.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>aquarium_fish</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>baby</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>beaver</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          labels\n",
       "0          apple\n",
       "1  aquarium_fish\n",
       "2           baby\n",
       "3           bear\n",
       "4         beaver"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decode df_meta.labels so they are regular strings\n",
    "df_meta[\"labels\"] = df_meta[\"labels\"].apply(\n",
    "    lambda x: x.decode(\"utf-8\")\n",
    ")\n",
    "\n",
    "df_meta.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bicycle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>motorcycle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        labels\n",
       "8      bicycle\n",
       "48  motorcycle"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### Todo: Filter the dataset_train and dataset_meta objects to find the \n",
    "# label numbers for Bicycle and Motorcycle\n",
    "\n",
    "labels_df = df_meta[ (df_meta[\"labels\"].str.lower()==\"motorcycle\") | \n",
    "                 (df_meta[\"labels\"].str.lower()==\"bicycle\")  ]\n",
    "\n",
    "labels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([8, 48], dtype='int64')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[8, 48]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get labels for vehicles of interest\n",
    "labels_idx = labels_df.index.to_list()\n",
    "labels_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Good job! We only need objects with label 8 and 48 -- this drastically simplifies our handling of the data! Below we construct a dataframe for you, and you can safely drop the rows that don't contain observations about bicycles and motorcycles. Fill in the missing lines below to drop all other rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([b'filenames', b'batch_label', b'fine_labels', b'coarse_labels', b'data'])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'bos_taurus_s_000507.png'</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>[255, 255, 255, 255, 255, 255, 255, 255, 255, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'stegosaurus_s_000125.png'</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>[255, 253, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'mcintosh_s_000643.png'</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[250, 248, 247, 248, 249, 249, 248, 248, 247, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'altar_boy_s_001435.png'</td>\n",
       "      <td>11</td>\n",
       "      <td>3</td>\n",
       "      <td>[124, 131, 135, 138, 140, 144, 148, 152, 156, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'cichlid_s_000031.png'</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>[43, 32, 87, 127, 55, 48, 51, 87, 121, 113, 13...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     filenames  labels  row  \\\n",
       "0   b'bos_taurus_s_000507.png'      19    0   \n",
       "1  b'stegosaurus_s_000125.png'      29    1   \n",
       "2     b'mcintosh_s_000643.png'       0    2   \n",
       "3    b'altar_boy_s_001435.png'      11    3   \n",
       "4      b'cichlid_s_000031.png'       1    4   \n",
       "\n",
       "                                      flatten_pixels  \n",
       "0  [255, 255, 255, 255, 255, 255, 255, 255, 255, ...  \n",
       "1  [255, 253, 253, 253, 253, 253, 253, 253, 253, ...  \n",
       "2  [250, 248, 247, 248, 249, 249, 248, 248, 247, ...  \n",
       "3  [124, 131, 135, 138, 140, 144, 148, 152, 156, ...  \n",
       "4  [43, 32, 87, 127, 55, 48, 51, 87, 121, 113, 13...  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Construct the train dataframe\n",
    "df_train = pd.DataFrame({\n",
    "    \"filenames\": dataset_train[b'filenames'],\n",
    "    \"labels\": dataset_train[b'fine_labels'],\n",
    "    \"row\": range(len(dataset_train[b'filenames'])),\n",
    "    \n",
    "    # \"batch_label\": json.dumps(dataset_train[b\"batch_label\"]),\n",
    "    # \"coarse_labels\": json.dumps(dataset_train[b\"coarse_labels\"]),\n",
    "    # \"flatten_pixels\": json.dumps(dataset_train[b\"data\"]),\n",
    "    \n",
    "    # \"batch_label\": dataset_train[b\"batch_label\"],\n",
    "    # \"coarse_labels\": dataset_train[b\"coarse_labels\"],\n",
    "    \"flatten_pixels\": dataset_train[b\"data\"].tolist(),\n",
    "    \n",
    "})\n",
    "\n",
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50000, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 50000 entries, 0 to 49999\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   filenames       50000 non-null  object\n",
      " 1   labels          50000 non-null  int64 \n",
      " 2   row             50000 non-null  int64 \n",
      " 3   flatten_pixels  50000 non-null  object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"labels\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 8, 48]), (1000, 4))"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Keeping only relevant labels\n",
    "df_train = df_train[ df_train[\"labels\"].isin(labels_idx) ]\n",
    "df_train[\"labels\"].unique(), df_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     500\n",
       "48    500\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train[\"labels\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25035</th>\n",
       "      <td>b'velocipede_s_001358.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>25035</td>\n",
       "      <td>[84, 91, 98, 107, 112, 117, 121, 127, 133, 136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35368</th>\n",
       "      <td>b'bicycle_s_000039.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>35368</td>\n",
       "      <td>[255, 252, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35589</th>\n",
       "      <td>b'motorcycle_s_002291.png'</td>\n",
       "      <td>48</td>\n",
       "      <td>35589</td>\n",
       "      <td>[185, 183, 184, 183, 182, 181, 182, 184, 182, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32078</th>\n",
       "      <td>b'moped_s_000135.png'</td>\n",
       "      <td>48</td>\n",
       "      <td>32078</td>\n",
       "      <td>[61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19343</th>\n",
       "      <td>b'trail_bike_s_000016.png'</td>\n",
       "      <td>48</td>\n",
       "      <td>19343</td>\n",
       "      <td>[134, 132, 164, 207, 181, 227, 255, 255, 255, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        filenames  labels    row  \\\n",
       "25035  b'velocipede_s_001358.png'       8  25035   \n",
       "35368     b'bicycle_s_000039.png'       8  35368   \n",
       "35589  b'motorcycle_s_002291.png'      48  35589   \n",
       "32078       b'moped_s_000135.png'      48  32078   \n",
       "19343  b'trail_bike_s_000016.png'      48  19343   \n",
       "\n",
       "                                          flatten_pixels  \n",
       "25035  [84, 91, 98, 107, 112, 117, 121, 127, 133, 136...  \n",
       "35368  [255, 252, 253, 253, 253, 253, 253, 253, 253, ...  \n",
       "35589  [185, 183, 184, 183, 182, 181, 182, 184, 182, ...  \n",
       "32078  [61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...  \n",
       "19343  [134, 132, 164, 207, 181, 227, 255, 255, 255, ...  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.sample(5,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>flatten_pixels</th>\n",
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       "  </thead>\n",
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       "      <th>25035</th>\n",
       "      <td>velocipede_s_001358.png</td>\n",
       "      <td>8</td>\n",
       "      <td>25035</td>\n",
       "      <td>[84, 91, 98, 107, 112, 117, 121, 127, 133, 136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35368</th>\n",
       "      <td>bicycle_s_000039.png</td>\n",
       "      <td>8</td>\n",
       "      <td>35368</td>\n",
       "      <td>[255, 252, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35589</th>\n",
       "      <td>motorcycle_s_002291.png</td>\n",
       "      <td>48</td>\n",
       "      <td>35589</td>\n",
       "      <td>[185, 183, 184, 183, 182, 181, 182, 184, 182, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32078</th>\n",
       "      <td>moped_s_000135.png</td>\n",
       "      <td>48</td>\n",
       "      <td>32078</td>\n",
       "      <td>[61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19343</th>\n",
       "      <td>trail_bike_s_000016.png</td>\n",
       "      <td>48</td>\n",
       "      <td>19343</td>\n",
       "      <td>[134, 132, 164, 207, 181, 227, 255, 255, 255, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                     filenames  labels    row  \\\n",
       "25035  velocipede_s_001358.png       8  25035   \n",
       "35368     bicycle_s_000039.png       8  35368   \n",
       "35589  motorcycle_s_002291.png      48  35589   \n",
       "32078       moped_s_000135.png      48  32078   \n",
       "19343  trail_bike_s_000016.png      48  19343   \n",
       "\n",
       "                                          flatten_pixels  \n",
       "25035  [84, 91, 98, 107, 112, 117, 121, 127, 133, 136...  \n",
       "35368  [255, 252, 253, 253, 253, 253, 253, 253, 253, ...  \n",
       "35589  [185, 183, 184, 183, 182, 181, 182, 184, 182, ...  \n",
       "32078  [61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...  \n",
       "19343  [134, 132, 164, 207, 181, 227, 255, 255, 255, ...  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decode df_train.filenames so they are regular strings\n",
    "df_train[\"filenames\"] = df_train[\"filenames\"].apply(\n",
    "    lambda x: x.decode(\"utf-8\")\n",
    ")\n",
    "\n",
    "df_train.sample(5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "      <th>labels</th>\n",
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       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'volcano_s_000012.png'</td>\n",
       "      <td>49</td>\n",
       "      <td>0</td>\n",
       "      <td>[199, 196, 195, 195, 196, 197, 198, 198, 199, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'woods_s_000412.png'</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>[113, 88, 72, 63, 72, 100, 131, 149, 139, 132,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'seal_s_001803.png'</td>\n",
       "      <td>72</td>\n",
       "      <td>2</td>\n",
       "      <td>[61, 61, 67, 62, 64, 63, 70, 73, 78, 88, 85, 7...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'mushroom_s_001755.png'</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>[93, 88, 104, 90, 74, 70, 70, 75, 77, 60, 61, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'adriatic_sea_s_000653.png'</td>\n",
       "      <td>71</td>\n",
       "      <td>4</td>\n",
       "      <td>[80, 80, 82, 86, 92, 97, 96, 97, 98, 99, 100, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      filenames  labels  row  \\\n",
       "0       b'volcano_s_000012.png'      49    0   \n",
       "1         b'woods_s_000412.png'      33    1   \n",
       "2          b'seal_s_001803.png'      72    2   \n",
       "3      b'mushroom_s_001755.png'      51    3   \n",
       "4  b'adriatic_sea_s_000653.png'      71    4   \n",
       "\n",
       "                                      flatten_pixels  \n",
       "0  [199, 196, 195, 195, 196, 197, 198, 198, 199, ...  \n",
       "1  [113, 88, 72, 63, 72, 100, 131, 149, 139, 132,...  \n",
       "2  [61, 61, 67, 62, 64, 63, 70, 73, 78, 88, 85, 7...  \n",
       "3  [93, 88, 104, 90, 74, 70, 70, 75, 77, 60, 61, ...  \n",
       "4  [80, 80, 82, 86, 92, 97, 96, 97, 98, 99, 100, ...  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create test df\n",
    "df_test = pd.DataFrame({\n",
    "    \"filenames\": dataset_test[b'filenames'],\n",
    "    \"labels\": dataset_test[b'fine_labels'],\n",
    "    \"row\": range(len(dataset_test[b'filenames'])),\n",
    "    \n",
    "    \"flatten_pixels\": dataset_test[b\"data\"].tolist(),\n",
    "})\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000, 4)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 8, 48]), (200, 4))"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Only keep relevant samples in df_test\n",
    "df_test = df_test[ df_test[\"labels\"].isin(labels_idx) ]\n",
    "df_test[\"labels\"].unique() ,df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>b'safety_bike_s_000390.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>[22, 34, 38, 33, 26, 9, 10, 9, 7, 19, 45, 57, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>b'bike_s_000658.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>[216, 217, 215, 215, 214, 211, 208, 205, 201, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>b'velocipede_s_001744.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>116</td>\n",
       "      <td>[255, 251, 251, 251, 251, 249, 240, 235, 237, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>b'bike_s_000643.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>161</td>\n",
       "      <td>[74, 105, 118, 140, 166, 158, 163, 170, 165, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>b'ordinary_bicycle_s_000437.png'</td>\n",
       "      <td>8</td>\n",
       "      <td>319</td>\n",
       "      <td>[195, 193, 193, 193, 193, 193, 193, 193, 193, ...</td>\n",
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      ],
      "text/plain": [
       "                            filenames  labels  row  \\\n",
       "27        b'safety_bike_s_000390.png'       8   27   \n",
       "28               b'bike_s_000658.png'       8   28   \n",
       "116        b'velocipede_s_001744.png'       8  116   \n",
       "161              b'bike_s_000643.png'       8  161   \n",
       "319  b'ordinary_bicycle_s_000437.png'       8  319   \n",
       "\n",
       "                                        flatten_pixels  \n",
       "27   [22, 34, 38, 33, 26, 9, 10, 9, 7, 19, 45, 57, ...  \n",
       "28   [216, 217, 215, 215, 214, 211, 208, 205, 201, ...  \n",
       "116  [255, 251, 251, 251, 251, 249, 240, 235, 237, ...  \n",
       "161  [74, 105, 118, 140, 166, 158, 163, 170, 165, 1...  \n",
       "319  [195, 193, 193, 193, 193, 193, 193, 193, 193, ...  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     100\n",
       "48    100\n",
       "Name: labels, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test[\"labels\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>safety_bike_s_000390.png</td>\n",
       "      <td>8</td>\n",
       "      <td>27</td>\n",
       "      <td>[22, 34, 38, 33, 26, 9, 10, 9, 7, 19, 45, 57, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>bike_s_000658.png</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>[216, 217, 215, 215, 214, 211, 208, 205, 201, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>velocipede_s_001744.png</td>\n",
       "      <td>8</td>\n",
       "      <td>116</td>\n",
       "      <td>[255, 251, 251, 251, 251, 249, 240, 235, 237, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>bike_s_000643.png</td>\n",
       "      <td>8</td>\n",
       "      <td>161</td>\n",
       "      <td>[74, 105, 118, 140, 166, 158, 163, 170, 165, 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>ordinary_bicycle_s_000437.png</td>\n",
       "      <td>8</td>\n",
       "      <td>319</td>\n",
       "      <td>[195, 193, 193, 193, 193, 193, 193, 193, 193, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                         filenames  labels  row  \\\n",
       "27        safety_bike_s_000390.png       8   27   \n",
       "28               bike_s_000658.png       8   28   \n",
       "116        velocipede_s_001744.png       8  116   \n",
       "161              bike_s_000643.png       8  161   \n",
       "319  ordinary_bicycle_s_000437.png       8  319   \n",
       "\n",
       "                                        flatten_pixels  \n",
       "27   [22, 34, 38, 33, 26, 9, 10, 9, 7, 19, 45, 57, ...  \n",
       "28   [216, 217, 215, 215, 214, 211, 208, 205, 201, ...  \n",
       "116  [255, 251, 251, 251, 251, 249, 240, 235, 237, ...  \n",
       "161  [74, 105, 118, 140, 166, 158, 163, 170, 165, 1...  \n",
       "319  [195, 193, 193, 193, 193, 193, 193, 193, 193, ...  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Decode df_test.filenames so they are regular strings\n",
    "df_test[\"filenames\"] = df_test[\"filenames\"].apply(\n",
    "    lambda x: x.decode(\"utf-8\")\n",
    ")\n",
    "\n",
    "df_test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the images pixels structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([255, 255, 255, ...,  10,  59,  79], dtype=uint8)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset_train[b'data'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(dataset_train[b'data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #Construct the dataframe\n",
    "# df_train = pd.DataFrame({\n",
    "#     \"filenames\": dataset_train[b'filenames'],\n",
    "#     \"labels\": dataset_train[b'fine_labels'],\n",
    "#     \"row\": range(len(dataset_train[b'filenames']))\n",
    "# })\n",
    "\n",
    "# # Drop all rows from df_train where label is not 8 or 48\n",
    "# df_train = #TODO: Fill in\n",
    "\n",
    "# # Decode df_train.filenames so they are regular strings\n",
    "# df_train[\"filenames\"] = df_train[\"filenames\"].apply(\n",
    "#     lambda x: x.decode(\"utf-8\")\n",
    "# )\n",
    "\n",
    "\n",
    "# df_test = pd.DataFrame({\n",
    "#     \"filenames\": dataset_test[b'filenames'],\n",
    "#     \"labels\": dataset_test[b'fine_labels'],\n",
    "#     \"row\": range(len(dataset_test[b'filenames']))\n",
    "# })\n",
    "\n",
    "# # Drop all rows from df_test where label is not 8 or 48\n",
    "# df_test = #TODO: Fill in\n",
    "\n",
    "# # Decode df_test.filenames so they are regular strings\n",
    "# df_test[\"filenames\"] = df_test[\"filenames\"].apply(\n",
    "#     lambda x: x.decode(\"utf-8\")\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the data is filtered for just our classes, we can save all our images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mkdir: cannot create directory ‘./train’: File exists\n",
      "mkdir: cannot create directory ‘./test’: File exists\n"
     ]
    }
   ],
   "source": [
    "!mkdir ./train\n",
    "!mkdir ./test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous sections we introduced you to several key snippets of code:\n",
    "\n",
    "1. Grabbing the image data:\n",
    "\n",
    "```python\n",
    "dataset_train[b'data'][0]\n",
    "```\n",
    "\n",
    "2. A simple idiom for stacking the image data into the right shape\n",
    "\n",
    "```python\n",
    "import numpy as np\n",
    "np.dstack((\n",
    "    row[0:1024].reshape(32,32),\n",
    "    row[1024:2048].reshape(32,32),\n",
    "    row[2048:].reshape(32,32)\n",
    "))\n",
    "```\n",
    "\n",
    "3. A simple `matplotlib` utility for saving images\n",
    "\n",
    "```python\n",
    "plt.imsave(path+row['filenames'], target)\n",
    "```\n",
    "\n",
    "Compose these together into a function that saves all the images into the `./test` and `./train` directories. Use the comments in the body of the `save_images` function below to guide your construction of the function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Each 1024 in a row is a channel (red, green, then blue)\n",
    "# row = dataset_train[b'data'][0]\n",
    "# red, green, blue = row[0:1024], row[1024:2048], row[2048:]\n",
    "\n",
    "# # Each 32 items in the channel are a row in the 32x32 image\n",
    "# red = red.reshape(32,32)\n",
    "# green = green.reshape(32,32)\n",
    "# blue = blue.reshape(32,32)\n",
    "\n",
    "# # Combine the channels into a 32x32x3 image!\n",
    "# combined = np.dstack((red,green,blue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_random_image(df:pd.DataFrame):\n",
    "    indexes = df.index.to_list()\n",
    "    \n",
    "    choosen_idx = random.choice(indexes)\n",
    "\n",
    "    # Each 1024 in a row is a channel (red, green, then blue)\n",
    "    flatten_pixels = np.array(df.loc[choosen_idx,\"flatten_pixels\"]) #dataset_train[b'data'][0]\n",
    "    red, green, blue = flatten_pixels[0:1024], flatten_pixels[1024:2048], flatten_pixels[2048:]\n",
    "\n",
    "    # Each 32 items in the channel are a row in the 32x32 image\n",
    "    red = red.reshape(32,32)\n",
    "    green = green.reshape(32,32)\n",
    "    blue = blue.reshape(32,32)\n",
    "\n",
    "    # Combine the channels into a 32x32x3 image!\n",
    "    image = np.dstack((red,green,blue))\n",
    "\n",
    "    plt.title(df.loc[choosen_idx,\"filenames\"])\n",
    "    plt.imshow(image);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_image(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_image(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def save_images(df:pd.DataFrame, images:np.ndarray, save_path:str):\n",
    "#     for index, row in df.iterrows():\n",
    "        \n",
    "#         # The code may take time, log in the console to keep track of things\n",
    "#         if index==0 or index%100==0:\n",
    "#             print(f\"Currently processing sample {index} over {df.shape[0]} (note: indexes not reset)...\")\n",
    "        \n",
    "#         # Each 1024 in a row is a channel (red, green, then blue)\n",
    "#         flatten_pixels = images[index] #df.loc[index, b\"flatten_pixels\"]\n",
    "#         red_channel, green_channel, blue_channel = flatten_pixels[0:1024], flatten_pixels[1024:2048], flatten_pixels[2048:]\n",
    "    \n",
    "#         # Each 32 items in the channel are a row in the 32x32 image\n",
    "#         red_channel = red_channel.reshape(32,32)\n",
    "#         green_channel = red_channel.reshape(32,32)\n",
    "#         blue_channel = red_channel.reshape(32,32)\n",
    "        \n",
    "#         # Combine the channels into a 32x32x3 image!\n",
    "#         image = np.dstack((red,green,blue))\n",
    "        \n",
    "#         image_path = os.path.join(save_path, df.loc[index, 'filenames'] )\n",
    "        \n",
    "#         # plt.imshow(image)\n",
    "        \n",
    "#         plt.imsave(image_path, image)\n",
    "    \n",
    "#     print(f\"All images processed in {save_path}\")\n",
    "    \n",
    "# #     #Grab the image data in row-major form\n",
    "# #     img = \n",
    "    \n",
    "# #     # Consolidated stacking/reshaping from earlier\n",
    "# #     target = \n",
    "    \n",
    "# #     # Save the image\n",
    "    \n",
    "    \n",
    "#     # Return any signal data you want for debugging (done)\n",
    "#     return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def save_images(df:pd.DataFrame, save_path:str):\n",
    "    for index, row in df.iterrows():\n",
    "        \n",
    "        # The code may take time, log in the console to keep track of things\n",
    "        if index==0 or index%100==0:\n",
    "            print(f\"Currently processing sample {index} over {df.shape[0]} (note: indexes not reset)...\")\n",
    "        \n",
    "        # Each 1024 in a row is a channel (red, green, then blue)\n",
    "        flatten_pixels = np.array(df.loc[index,\"flatten_pixels\"]).astype(np.uint8) #df.loc[index, b\"flatten_pixels\"]\n",
    "        red_channel, green_channel, blue_channel = flatten_pixels[0:1024], flatten_pixels[1024:2048], flatten_pixels[2048:]\n",
    "    \n",
    "        # Each 32 items in the channel are a row in the 32x32 image\n",
    "        red_channel = red_channel.reshape(32,32)\n",
    "        green_channel = green_channel.reshape(32,32)\n",
    "        blue_channel = blue_channel.reshape(32,32)\n",
    "        \n",
    "        # Combine the channels into a 32x32x3 image!\n",
    "        image = np.dstack((red_channel,green_channel,blue_channel))\n",
    "        \n",
    "        image_path = os.path.join(save_path, df.loc[index, 'filenames'] )\n",
    "        \n",
    "        plt.imsave(image_path, image)\n",
    "    \n",
    "    print(f\"All images processed in {save_path}\")\n",
    "    \n",
    "#     #Grab the image data in row-major form\n",
    "#     img = \n",
    "    \n",
    "#     # Consolidated stacking/reshaping from earlier\n",
    "#     target = \n",
    "    \n",
    "#     # Save the image\n",
    "    \n",
    "    \n",
    "    # Return any signal data you want for debugging (done)\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((1000, 4), (200, 4))"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.shape, df_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25035</th>\n",
       "      <td>velocipede_s_001358.png</td>\n",
       "      <td>8</td>\n",
       "      <td>25035</td>\n",
       "      <td>[84, 91, 98, 107, 112, 117, 121, 127, 133, 136...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35368</th>\n",
       "      <td>bicycle_s_000039.png</td>\n",
       "      <td>8</td>\n",
       "      <td>35368</td>\n",
       "      <td>[255, 252, 253, 253, 253, 253, 253, 253, 253, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35589</th>\n",
       "      <td>motorcycle_s_002291.png</td>\n",
       "      <td>48</td>\n",
       "      <td>35589</td>\n",
       "      <td>[185, 183, 184, 183, 182, 181, 182, 184, 182, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32078</th>\n",
       "      <td>moped_s_000135.png</td>\n",
       "      <td>48</td>\n",
       "      <td>32078</td>\n",
       "      <td>[61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19343</th>\n",
       "      <td>trail_bike_s_000016.png</td>\n",
       "      <td>48</td>\n",
       "      <td>19343</td>\n",
       "      <td>[134, 132, 164, 207, 181, 227, 255, 255, 255, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     filenames  labels    row  \\\n",
       "25035  velocipede_s_001358.png       8  25035   \n",
       "35368     bicycle_s_000039.png       8  35368   \n",
       "35589  motorcycle_s_002291.png      48  35589   \n",
       "32078       moped_s_000135.png      48  32078   \n",
       "19343  trail_bike_s_000016.png      48  19343   \n",
       "\n",
       "                                          flatten_pixels  \n",
       "25035  [84, 91, 98, 107, 112, 117, 121, 127, 133, 136...  \n",
       "35368  [255, 252, 253, 253, 253, 253, 253, 253, 253, ...  \n",
       "35589  [185, 183, 184, 183, 182, 181, 182, 184, 182, ...  \n",
       "32078  [61, 70, 44, 61, 117, 127, 122, 121, 142, 137,...  \n",
       "19343  [134, 132, 164, 207, 181, 227, 255, 255, 255, ...  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.sample(5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5162</th>\n",
       "      <td>motorcycle_s_000685.png</td>\n",
       "      <td>48</td>\n",
       "      <td>5162</td>\n",
       "      <td>[166, 167, 171, 174, 176, 178, 180, 181, 182, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>790</th>\n",
       "      <td>motorcycle_s_000963.png</td>\n",
       "      <td>48</td>\n",
       "      <td>790</td>\n",
       "      <td>[253, 250, 250, 250, 250, 250, 250, 250, 250, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1469</th>\n",
       "      <td>velocipede_s_000292.png</td>\n",
       "      <td>8</td>\n",
       "      <td>1469</td>\n",
       "      <td>[184, 186, 190, 193, 196, 197, 199, 200, 200, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7806</th>\n",
       "      <td>minibike_s_000254.png</td>\n",
       "      <td>48</td>\n",
       "      <td>7806</td>\n",
       "      <td>[209, 205, 205, 206, 207, 207, 207, 206, 205, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6553</th>\n",
       "      <td>velocipede_s_001633.png</td>\n",
       "      <td>8</td>\n",
       "      <td>6553</td>\n",
       "      <td>[159, 168, 180, 192, 203, 212, 219, 224, 228, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    filenames  labels   row  \\\n",
       "5162  motorcycle_s_000685.png      48  5162   \n",
       "790   motorcycle_s_000963.png      48   790   \n",
       "1469  velocipede_s_000292.png       8  1469   \n",
       "7806    minibike_s_000254.png      48  7806   \n",
       "6553  velocipede_s_001633.png       8  6553   \n",
       "\n",
       "                                         flatten_pixels  \n",
       "5162  [166, 167, 171, 174, 176, 178, 180, 181, 182, ...  \n",
       "790   [253, 250, 250, 250, 250, 250, 250, 250, 250, ...  \n",
       "1469  [184, 186, 190, 193, 196, 197, 199, 200, 200, ...  \n",
       "7806  [209, 205, 205, 206, 207, 207, 207, 206, 205, ...  \n",
       "6553  [159, 168, 180, 192, 203, 212, 219, 224, 228, ...  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.sample(5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently processing sample 4600 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 4900 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 10000 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 15600 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 16800 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 28500 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 31900 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 34500 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 36200 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 41800 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 44000 over 1000 (note: indexes not reset)...\n",
      "Currently processing sample 44300 over 1000 (note: indexes not reset)...\n",
      "All images processed in ./train\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save train image to disk\n",
    "save_images(df_train, \"./train\") #save_images(df_train, dataset_train[b\"data\"], \"./train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Currently processing sample 8300 over 200 (note: indexes not reset)...\n",
      "All images processed in ./test\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# save train image to disk\n",
    "save_images(df_test, \"./test\")  # save_images(df_test, dataset_test[b\"data\"], \"./test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.image as mpimg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_random_image_in_folder(folder:str):\n",
    "    \n",
    "    # Get one random image path\n",
    "    random_image = random.sample(os.listdir(folder), 1)\n",
    "    \n",
    "    # Read image\n",
    "    image = mpimg.imread(os.path.join(folder, random_image[0]))\n",
    "    plt.imshow(image)\n",
    "    plt.title(random_image)\n",
    "    plt.axis(\"off\");\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_image_in_folder(\"./train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_image_in_folder(\"./train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_random_image_in_folder(\"./test\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 3. Load the data\n",
    "\n",
    "Now we can load the data into S3.\n",
    "\n",
    "Using the sagemaker SDK grab the current region, execution role, and bucket."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:155: UserWarning: A NumPy version >=1.18.5 and <1.25.0 is required for this version of SciPy (detected version 1.25.1\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sagemaker.session.Session at 0x7f376c0046d0>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sagemaker\n",
    "from sagemaker.session import Session\n",
    "from sagemaker import get_execution_role\n",
    "\n",
    "session = sagemaker.Session()\n",
    "session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default Bucket: sagemaker-us-west-2-659095202386\n"
     ]
    }
   ],
   "source": [
    "bucket= session.default_bucket()\n",
    "print(\"Default Bucket: {}\".format(bucket))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AWS Region: us-west-2\n"
     ]
    }
   ],
   "source": [
    "region = session.boto_region_name\n",
    "print(\"AWS Region: {}\".format(region))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RoleArn: arn:aws:iam::659095202386:role/service-role/AmazonSageMaker-ExecutionRole-20230822T154211\n"
     ]
    }
   ],
   "source": [
    "role = get_execution_role()\n",
    "print(\"RoleArn: {}\".format(role))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With this data we can easily sync your data up into S3!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'udacity-project-4'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prefix = \"udacity-project-4\"\n",
    "prefix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "upload: train/bicycle_s_000021.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000021.png\n",
      "upload: train/bicycle_s_000038.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000038.png\n",
      "upload: train/bicycle_s_000017.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000017.png\n",
      "upload: train/bicycle_s_000071.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000071.png\n",
      "upload: train/bicycle_s_000035.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000035.png\n",
      "upload: train/bicycle_s_000124.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000124.png\n",
      "upload: train/bicycle_s_000039.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000039.png\n",
      "upload: train/bicycle_s_000149.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000149.png\n",
      "upload: train/bicycle_s_000043.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000043.png\n",
      "upload: train/bicycle_s_000156.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000156.png\n",
      "upload: train/bicycle_s_000159.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/bicycle_s_000159.png\n",
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      "upload: train/cycle_s_003006.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003006.png\n",
      "upload: train/cycle_s_002978.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_002978.png\n",
      "upload: train/dirt_bike_s_000005.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000005.png\n",
      "upload: train/cycle_s_003162.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003162.png\n",
      "upload: train/cycle_s_003147.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003147.png\n",
      "upload: train/cycle_s_002844.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_002844.png\n",
      "upload: train/dirt_bike_s_000003.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000003.png\n",
      "upload: train/dirt_bike_s_000017.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000017.png\n",
      "upload: train/cycle_s_003148.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003148.png\n",
      "upload: train/dirt_bike_s_000119.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000119.png\n",
      "upload: train/minibike_s_000011.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000011.png\n",
      "upload: train/dirt_bike_s_000030.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000030.png\n",
      "upload: train/cycle_s_003122.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003122.png\n",
      "upload: train/dirt_bike_s_000059.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000059.png\n",
      "upload: train/minibike_s_000022.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000022.png\n",
      "upload: train/dirt_bike_s_000134.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000134.png\n",
      "upload: train/cycle_s_003043.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/cycle_s_003043.png\n",
      "upload: train/minibike_s_000010.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000010.png\n",
      "upload: train/dirt_bike_s_000124.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/dirt_bike_s_000124.png\n",
      "upload: train/minibike_s_000036.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000036.png\n",
      "upload: train/minibike_s_000099.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000099.png\n",
      "upload: train/minibike_s_000035.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000035.png\n",
      "upload: train/minibike_s_000020.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000020.png\n",
      "upload: train/minibike_s_000110.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000110.png\n",
      "upload: train/minibike_s_000130.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000130.png\n",
      "upload: train/minibike_s_000108.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000108.png\n",
      "upload: train/minibike_s_000117.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000117.png\n",
      "upload: train/minibike_s_000116.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000116.png\n",
      "upload: train/minibike_s_000121.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000121.png\n",
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      "upload: train/minibike_s_000146.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000146.png\n",
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      "upload: train/minibike_s_000102.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000102.png\n",
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      "upload: train/minibike_s_000218.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000218.png\n",
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      "upload: train/minibike_s_000401.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000401.png\n",
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      "upload: train/minibike_s_000434.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000434.png\n",
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      "upload: train/minibike_s_000485.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000485.png\n",
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      "upload: train/minibike_s_000565.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000565.png\n",
      "upload: train/minibike_s_000566.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/minibike_s_000566.png\n",
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      "upload: train/motorcycle_s_000167.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000167.png\n",
      "upload: train/motorcycle_s_000216.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000216.png\n",
      "upload: train/motorcycle_s_000217.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000217.png\n",
      "upload: train/motorcycle_s_000219.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000219.png\n",
      "upload: train/motorcycle_s_000223.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000223.png\n",
      "upload: train/motorcycle_s_000242.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000242.png\n",
      "upload: train/motorcycle_s_000136.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000136.png\n",
      "upload: train/motorcycle_s_000220.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000220.png\n",
      "upload: train/motorcycle_s_000221.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000221.png\n",
      "upload: train/motorcycle_s_000245.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000245.png\n",
      "upload: train/motorcycle_s_000253.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000253.png\n",
      "upload: train/motorcycle_s_000249.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000249.png\n",
      "upload: train/motorcycle_s_000246.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000246.png\n",
      "upload: train/motorcycle_s_000290.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000290.png\n",
      "upload: train/motorcycle_s_000262.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000262.png\n",
      "upload: train/motorcycle_s_000277.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000277.png\n",
      "upload: train/motorcycle_s_000284.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000284.png\n",
      "upload: train/motorcycle_s_000252.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000252.png\n",
      "upload: train/motorcycle_s_000304.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000304.png\n",
      "upload: train/motorcycle_s_000256.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000256.png\n",
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      "upload: train/motorcycle_s_000300.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000300.png\n",
      "upload: train/motorcycle_s_000319.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000319.png\n",
      "upload: train/motorcycle_s_000312.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000312.png\n",
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      "upload: train/motorcycle_s_000320.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000320.png\n",
      "upload: train/motorcycle_s_000325.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000325.png\n",
      "upload: train/motorcycle_s_000332.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000332.png\n",
      "upload: train/motorcycle_s_000422.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000422.png\n",
      "upload: train/motorcycle_s_000407.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000407.png\n",
      "upload: train/motorcycle_s_000418.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000418.png\n",
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      "upload: train/motorcycle_s_000430.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000430.png\n",
      "upload: train/motorcycle_s_000431.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000431.png\n",
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      "upload: train/motorcycle_s_000340.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000340.png\n",
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      "upload: train/motorcycle_s_000456.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000456.png\n",
      "upload: train/motorcycle_s_000508.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000508.png\n",
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      "upload: train/motorcycle_s_000521.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000521.png\n",
      "upload: train/motorcycle_s_000451.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000451.png\n",
      "upload: train/motorcycle_s_000353.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000353.png\n",
      "upload: train/motorcycle_s_000486.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000486.png\n",
      "upload: train/motorcycle_s_000496.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000496.png\n",
      "upload: train/motorcycle_s_000543.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000543.png\n",
      "upload: train/motorcycle_s_000593.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000593.png\n",
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      "upload: train/motorcycle_s_000585.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000585.png\n",
      "upload: train/motorcycle_s_000459.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000459.png\n",
      "upload: train/motorcycle_s_000580.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000580.png\n",
      "upload: train/motorcycle_s_000629.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000629.png\n",
      "upload: train/motorcycle_s_000542.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000542.png\n",
      "upload: train/motorcycle_s_000654.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000654.png\n",
      "upload: train/motorcycle_s_000622.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000622.png\n",
      "upload: train/motorcycle_s_000605.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000605.png\n",
      "upload: train/motorcycle_s_000669.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000669.png\n",
      "upload: train/motorcycle_s_000545.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000545.png\n",
      "upload: train/motorcycle_s_000617.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000617.png\n",
      "upload: train/motorcycle_s_000695.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000695.png\n",
      "upload: train/motorcycle_s_000713.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000713.png\n",
      "upload: train/motorcycle_s_000700.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000700.png\n",
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      "upload: train/motorcycle_s_000724.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000724.png\n",
      "upload: train/motorcycle_s_000732.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000732.png\n",
      "upload: train/motorcycle_s_000741.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000741.png\n",
      "upload: train/motorcycle_s_000698.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000698.png\n",
      "upload: train/motorcycle_s_000772.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000772.png\n",
      "upload: train/motorcycle_s_000687.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000687.png\n",
      "upload: train/motorcycle_s_000699.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000699.png\n",
      "upload: train/motorcycle_s_000714.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000714.png\n",
      "upload: train/motorcycle_s_000734.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000734.png\n",
      "upload: train/motorcycle_s_000792.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000792.png\n",
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      "upload: train/motorcycle_s_000790.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000790.png\n",
      "upload: train/motorcycle_s_000894.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000894.png\n",
      "upload: train/motorcycle_s_000917.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000917.png\n",
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      "upload: train/motorcycle_s_000834.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000834.png\n",
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      "upload: train/motorcycle_s_000933.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000933.png\n",
      "upload: train/motorcycle_s_000938.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000938.png\n",
      "upload: train/motorcycle_s_000941.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000941.png\n",
      "upload: train/motorcycle_s_000922.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000922.png\n",
      "upload: train/motorcycle_s_000991.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000991.png\n",
      "upload: train/motorcycle_s_001012.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001012.png\n",
      "upload: train/motorcycle_s_001016.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001016.png\n",
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      "upload: train/motorcycle_s_001063.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001063.png\n",
      "upload: train/motorcycle_s_001064.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001064.png\n",
      "upload: train/motorcycle_s_001119.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001119.png\n",
      "upload: train/motorcycle_s_001027.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001027.png\n",
      "upload: train/motorcycle_s_000989.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000989.png\n",
      "upload: train/motorcycle_s_001176.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001176.png\n",
      "upload: train/motorcycle_s_001126.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001126.png\n",
      "upload: train/motorcycle_s_001106.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001106.png\n",
      "upload: train/motorcycle_s_000979.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_000979.png\n",
      "upload: train/motorcycle_s_001118.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001118.png\n",
      "upload: train/motorcycle_s_001033.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001033.png\n",
      "upload: train/motorcycle_s_001182.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001182.png\n",
      "upload: train/motorcycle_s_001180.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001180.png\n",
      "upload: train/motorcycle_s_001205.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001205.png\n",
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      "upload: train/motorcycle_s_001209.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001209.png\n",
      "upload: train/motorcycle_s_001167.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001167.png\n",
      "upload: train/motorcycle_s_001196.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001196.png\n",
      "upload: train/motorcycle_s_001181.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001181.png\n",
      "upload: train/motorcycle_s_001183.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001183.png\n",
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      "upload: train/motorcycle_s_001238.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001238.png\n",
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      "upload: train/motorcycle_s_001213.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001213.png\n",
      "upload: train/motorcycle_s_001237.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001237.png\n",
      "upload: train/motorcycle_s_001273.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001273.png\n",
      "upload: train/motorcycle_s_001287.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001287.png\n",
      "upload: train/motorcycle_s_001247.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001247.png\n",
      "upload: train/motorcycle_s_001246.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001246.png\n",
      "upload: train/motorcycle_s_001263.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001263.png\n",
      "upload: train/motorcycle_s_001319.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001319.png\n",
      "upload: train/motorcycle_s_001326.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001326.png\n",
      "upload: train/motorcycle_s_001242.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001242.png\n",
      "upload: train/motorcycle_s_001297.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001297.png\n",
      "upload: train/motorcycle_s_001303.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001303.png\n",
      "upload: train/motorcycle_s_001315.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001315.png\n",
      "upload: train/motorcycle_s_001363.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001363.png\n",
      "upload: train/motorcycle_s_001368.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001368.png\n",
      "upload: train/motorcycle_s_001384.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001384.png\n",
      "upload: train/motorcycle_s_001392.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001392.png\n",
      "upload: train/motorcycle_s_001400.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001400.png\n",
      "upload: train/motorcycle_s_001360.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001360.png\n",
      "upload: train/motorcycle_s_001236.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001236.png\n",
      "upload: train/motorcycle_s_001402.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001402.png\n",
      "upload: train/motorcycle_s_001397.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001397.png\n",
      "upload: train/motorcycle_s_001409.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001409.png\n",
      "upload: train/motorcycle_s_001337.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001337.png\n",
      "upload: train/motorcycle_s_001407.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001407.png\n",
      "upload: train/motorcycle_s_001413.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001413.png\n",
      "upload: train/motorcycle_s_001370.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001370.png\n",
      "upload: train/motorcycle_s_001438.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001438.png\n",
      "upload: train/motorcycle_s_001453.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001453.png\n",
      "upload: train/motorcycle_s_001507.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001507.png\n",
      "upload: train/motorcycle_s_001527.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001527.png\n",
      "upload: train/motorcycle_s_001492.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001492.png\n",
      "upload: train/motorcycle_s_001439.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001439.png\n",
      "upload: train/motorcycle_s_001536.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001536.png\n",
      "upload: train/motorcycle_s_001584.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001584.png\n",
      "upload: train/motorcycle_s_001520.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001520.png\n",
      "upload: train/motorcycle_s_001435.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001435.png\n",
      "upload: train/motorcycle_s_001563.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001563.png\n",
      "upload: train/motorcycle_s_001610.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001610.png\n",
      "upload: train/motorcycle_s_001565.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001565.png\n",
      "upload: train/motorcycle_s_001549.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001549.png\n",
      "upload: train/motorcycle_s_001587.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001587.png\n",
      "upload: train/motorcycle_s_001585.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001585.png\n",
      "upload: train/motorcycle_s_001611.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001611.png\n",
      "upload: train/motorcycle_s_001623.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001623.png\n",
      "upload: train/motorcycle_s_001641.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001641.png\n",
      "upload: train/motorcycle_s_001622.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001622.png\n",
      "upload: train/motorcycle_s_001686.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001686.png\n",
      "upload: train/motorcycle_s_001693.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001693.png\n",
      "upload: train/motorcycle_s_001580.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001580.png\n",
      "upload: train/motorcycle_s_001690.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001690.png\n",
      "upload: train/motorcycle_s_001695.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001695.png\n",
      "upload: train/motorcycle_s_001706.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001706.png\n",
      "upload: train/motorcycle_s_001699.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001699.png\n",
      "upload: train/motorcycle_s_001753.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001753.png\n",
      "upload: train/motorcycle_s_001709.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001709.png\n",
      "upload: train/motorcycle_s_001715.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001715.png\n",
      "upload: train/motorcycle_s_001696.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001696.png\n",
      "upload: train/motorcycle_s_001710.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001710.png\n",
      "upload: train/motorcycle_s_001711.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001711.png\n",
      "upload: train/motorcycle_s_001714.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001714.png\n",
      "upload: train/motorcycle_s_001767.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001767.png\n",
      "upload: train/motorcycle_s_001765.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001765.png\n",
      "upload: train/motorcycle_s_001707.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001707.png\n",
      "upload: train/motorcycle_s_001790.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001790.png\n",
      "upload: train/motorcycle_s_001862.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001862.png\n",
      "upload: train/motorcycle_s_001784.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001784.png\n",
      "upload: train/motorcycle_s_001896.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001896.png\n",
      "upload: train/motorcycle_s_001856.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001856.png\n",
      "upload: train/motorcycle_s_001792.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001792.png\n",
      "upload: train/motorcycle_s_001778.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001778.png\n",
      "upload: train/motorcycle_s_001905.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001905.png\n",
      "upload: train/motorcycle_s_001848.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001848.png\n",
      "upload: train/motorcycle_s_002030.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002030.png\n",
      "upload: train/motorcycle_s_002031.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002031.png\n",
      "upload: train/motorcycle_s_001878.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001878.png\n",
      "upload: train/motorcycle_s_002065.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002065.png\n",
      "upload: train/motorcycle_s_002066.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002066.png\n",
      "upload: train/motorcycle_s_001973.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001973.png\n",
      "upload: train/motorcycle_s_002067.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002067.png\n",
      "upload: train/motorcycle_s_002140.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002140.png\n",
      "upload: train/motorcycle_s_002121.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002121.png\n",
      "upload: train/motorcycle_s_001920.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001920.png\n",
      "upload: train/motorcycle_s_002153.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002153.png\n",
      "upload: train/motorcycle_s_002192.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002192.png\n",
      "upload: train/motorcycle_s_002193.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002193.png\n",
      "upload: train/motorcycle_s_002105.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002105.png\n",
      "upload: train/motorcycle_s_002214.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002214.png\n",
      "upload: train/motorcycle_s_002162.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002162.png\n",
      "upload: train/motorcycle_s_002183.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002183.png\n",
      "upload: train/motorcycle_s_002215.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002215.png\n",
      "upload: train/motorcycle_s_002221.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002221.png\n",
      "upload: train/motorcycle_s_001853.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_001853.png\n",
      "upload: train/motorcycle_s_002197.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002197.png\n",
      "upload: train/motorcycle_s_002225.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002225.png\n",
      "upload: train/motorcycle_s_002236.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002236.png\n",
      "upload: train/motorcycle_s_002291.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002291.png\n",
      "upload: train/motorcycle_s_002234.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002234.png\n",
      "upload: train/motorcycle_s_002298.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002298.png\n",
      "upload: train/motorcycle_s_002143.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002143.png\n",
      "upload: train/motorcycle_s_002271.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002271.png\n",
      "upload: train/motorcycle_s_002275.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002275.png\n",
      "upload: train/motorcycle_s_002222.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002222.png\n",
      "upload: train/ordinary_bicycle_s_000008.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000008.png\n",
      "upload: train/motorcycle_s_002317.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002317.png\n",
      "upload: train/motorcycle_s_002316.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002316.png\n",
      "upload: train/ordinary_bicycle_s_000011.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000011.png\n",
      "upload: train/ordinary_bicycle_s_000016.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000016.png\n",
      "upload: train/motorcycle_s_002254.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002254.png\n",
      "upload: train/ordinary_bicycle_s_000022.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000022.png\n",
      "upload: train/motorcycle_s_002237.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/motorcycle_s_002237.png\n",
      "upload: train/ordinary_bicycle_s_000035.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000035.png\n",
      "upload: train/ordinary_bicycle_s_000036.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000036.png\n",
      "upload: train/ordinary_bicycle_s_000029.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000029.png\n",
      "upload: train/ordinary_bicycle_s_000049.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000049.png\n",
      "upload: train/ordinary_bicycle_s_000066.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000066.png\n",
      "upload: train/ordinary_bicycle_s_000056.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000056.png\n",
      "upload: train/ordinary_bicycle_s_000031.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000031.png\n",
      "upload: train/ordinary_bicycle_s_000098.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000098.png\n",
      "upload: train/ordinary_bicycle_s_000023.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000023.png\n",
      "upload: train/ordinary_bicycle_s_000096.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000096.png\n",
      "upload: train/ordinary_bicycle_s_000097.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000097.png\n",
      "upload: train/ordinary_bicycle_s_000060.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000060.png\n",
      "upload: train/ordinary_bicycle_s_000101.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000101.png\n",
      "upload: train/ordinary_bicycle_s_000099.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000099.png\n",
      "upload: train/ordinary_bicycle_s_000100.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000100.png\n",
      "upload: train/ordinary_bicycle_s_000095.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000095.png\n",
      "upload: train/ordinary_bicycle_s_000107.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000107.png\n",
      "upload: train/ordinary_bicycle_s_000102.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000102.png\n",
      "upload: train/ordinary_bicycle_s_000112.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000112.png\n",
      "upload: train/ordinary_bicycle_s_000161.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000161.png\n",
      "upload: train/ordinary_bicycle_s_000110.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000110.png\n",
      "upload: train/ordinary_bicycle_s_000125.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000125.png\n",
      "upload: train/ordinary_bicycle_s_000167.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000167.png\n",
      "upload: train/ordinary_bicycle_s_000104.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000104.png\n",
      "upload: train/ordinary_bicycle_s_000201.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000201.png\n",
      "upload: train/ordinary_bicycle_s_000155.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000155.png\n",
      "upload: train/ordinary_bicycle_s_000205.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000205.png\n",
      "upload: train/ordinary_bicycle_s_000218.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000218.png\n",
      "upload: train/ordinary_bicycle_s_000157.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000157.png\n",
      "upload: train/ordinary_bicycle_s_000274.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000274.png\n",
      "upload: train/ordinary_bicycle_s_000269.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000269.png\n",
      "upload: train/ordinary_bicycle_s_000216.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000216.png\n",
      "upload: train/ordinary_bicycle_s_000286.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000286.png\n",
      "upload: train/ordinary_bicycle_s_000154.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000154.png\n",
      "upload: train/ordinary_bicycle_s_000277.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000277.png\n",
      "upload: train/ordinary_bicycle_s_000280.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000280.png\n",
      "upload: train/ordinary_bicycle_s_000265.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000265.png\n",
      "upload: train/ordinary_bicycle_s_000308.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000308.png\n",
      "upload: train/ordinary_bicycle_s_000297.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000297.png\n",
      "upload: train/ordinary_bicycle_s_000347.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000347.png\n",
      "upload: train/ordinary_bicycle_s_000387.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000387.png\n",
      "upload: train/ordinary_bicycle_s_000426.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000426.png\n",
      "upload: train/ordinary_bicycle_s_000324.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000324.png\n",
      "upload: train/ordinary_bicycle_s_000451.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000451.png\n",
      "upload: train/ordinary_bicycle_s_000419.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000419.png\n",
      "upload: train/safety_bicycle_s_000092.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000092.png\n",
      "upload: train/ordinary_bicycle_s_000433.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000433.png\n",
      "upload: train/ordinary_bicycle_s_000432.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000432.png\n",
      "upload: train/ordinary_bicycle_s_000355.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000355.png\n",
      "upload: train/safety_bicycle_s_000019.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000019.png\n",
      "upload: train/safety_bicycle_s_000125.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000125.png\n",
      "upload: train/ordinary_bicycle_s_000298.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/ordinary_bicycle_s_000298.png\n",
      "upload: train/safety_bicycle_s_000079.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000079.png\n",
      "upload: train/safety_bicycle_s_000196.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000196.png\n",
      "upload: train/safety_bicycle_s_000167.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000167.png\n",
      "upload: train/safety_bicycle_s_000162.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000162.png\n",
      "upload: train/safety_bicycle_s_000232.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000232.png\n",
      "upload: train/safety_bicycle_s_000239.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000239.png\n",
      "upload: train/safety_bicycle_s_000255.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000255.png\n",
      "upload: train/safety_bicycle_s_000233.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000233.png\n",
      "upload: train/safety_bicycle_s_000324.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000324.png\n",
      "upload: train/safety_bicycle_s_000348.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000348.png\n",
      "upload: train/safety_bicycle_s_000296.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000296.png\n",
      "upload: train/safety_bicycle_s_000207.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000207.png\n",
      "upload: train/safety_bicycle_s_000140.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000140.png\n",
      "upload: train/safety_bicycle_s_000193.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000193.png\n",
      "upload: train/safety_bicycle_s_000425.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000425.png\n",
      "upload: train/safety_bicycle_s_000322.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000322.png\n",
      "upload: train/safety_bicycle_s_000359.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000359.png\n",
      "upload: train/safety_bicycle_s_000427.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000427.png\n",
      "upload: train/safety_bicycle_s_000500.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000500.png\n",
      "upload: train/safety_bicycle_s_000303.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000303.png\n",
      "upload: train/safety_bicycle_s_000533.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000533.png\n",
      "upload: train/safety_bicycle_s_000373.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000373.png\n",
      "upload: train/safety_bicycle_s_000261.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000261.png\n",
      "upload: train/safety_bicycle_s_000568.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000568.png\n",
      "upload: train/safety_bicycle_s_000660.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000660.png\n",
      "upload: train/safety_bicycle_s_000789.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000789.png\n",
      "upload: train/safety_bicycle_s_001049.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001049.png\n",
      "upload: train/safety_bicycle_s_001026.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001026.png\n",
      "upload: train/safety_bicycle_s_000760.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000760.png\n",
      "upload: train/safety_bicycle_s_001028.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001028.png\n",
      "upload: train/safety_bicycle_s_000655.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000655.png\n",
      "upload: train/safety_bicycle_s_000860.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000860.png\n",
      "upload: train/safety_bicycle_s_001029.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001029.png\n",
      "upload: train/safety_bicycle_s_001109.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001109.png\n",
      "upload: train/safety_bicycle_s_001085.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001085.png\n",
      "upload: train/safety_bicycle_s_001113.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001113.png\n",
      "upload: train/safety_bicycle_s_000532.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000532.png\n",
      "upload: train/safety_bicycle_s_001202.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001202.png\n",
      "upload: train/safety_bicycle_s_001303.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001303.png\n",
      "upload: train/safety_bicycle_s_001243.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001243.png\n",
      "upload: train/safety_bicycle_s_001254.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001254.png\n",
      "upload: train/safety_bicycle_s_001320.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001320.png\n",
      "upload: train/safety_bicycle_s_001240.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001240.png\n",
      "upload: train/safety_bicycle_s_001381.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001381.png\n",
      "upload: train/safety_bicycle_s_001063.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001063.png\n",
      "upload: train/safety_bicycle_s_001327.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001327.png\n",
      "upload: train/safety_bicycle_s_000728.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_000728.png\n",
      "upload: train/safety_bicycle_s_001253.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001253.png\n",
      "upload: train/safety_bicycle_s_001639.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001639.png\n",
      "upload: train/safety_bicycle_s_001706.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001706.png\n",
      "upload: train/safety_bicycle_s_001608.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001608.png\n",
      "upload: train/safety_bike_s_000015.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000015.png\n",
      "upload: train/safety_bike_s_000009.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000009.png\n",
      "upload: train/safety_bicycle_s_001651.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001651.png\n",
      "upload: train/safety_bicycle_s_001705.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001705.png\n",
      "upload: train/safety_bicycle_s_001699.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001699.png\n",
      "upload: train/safety_bicycle_s_001570.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001570.png\n",
      "upload: train/safety_bike_s_000054.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000054.png\n",
      "upload: train/safety_bicycle_s_001659.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bicycle_s_001659.png\n",
      "upload: train/safety_bike_s_000158.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000158.png\n",
      "upload: train/safety_bike_s_000104.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000104.png\n",
      "upload: train/safety_bike_s_000178.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000178.png\n",
      "upload: train/safety_bike_s_000160.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000160.png\n",
      "upload: train/safety_bike_s_000155.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000155.png\n",
      "upload: train/safety_bike_s_000100.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000100.png\n",
      "upload: train/safety_bike_s_000165.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000165.png\n",
      "upload: train/safety_bike_s_000058.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000058.png\n",
      "upload: train/safety_bike_s_000244.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000244.png\n",
      "upload: train/safety_bike_s_000079.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000079.png\n",
      "upload: train/safety_bike_s_000263.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000263.png\n",
      "upload: train/safety_bike_s_000198.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000198.png\n",
      "upload: train/safety_bike_s_000277.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000277.png\n",
      "upload: train/safety_bike_s_000328.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000328.png\n",
      "upload: train/safety_bike_s_000245.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000245.png\n",
      "upload: train/safety_bike_s_000381.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000381.png\n",
      "upload: train/safety_bike_s_000173.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000173.png\n",
      "upload: train/safety_bike_s_000311.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000311.png\n",
      "upload: train/safety_bike_s_000830.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000830.png\n",
      "upload: train/safety_bike_s_000482.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000482.png\n",
      "upload: train/safety_bike_s_000643.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000643.png\n",
      "upload: train/safety_bike_s_000461.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000461.png\n",
      "upload: train/safety_bike_s_000867.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000867.png\n",
      "upload: train/safety_bike_s_000567.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000567.png\n",
      "upload: train/safety_bike_s_000950.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000950.png\n",
      "upload: train/safety_bike_s_000934.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000934.png\n",
      "upload: train/safety_bike_s_000963.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000963.png\n",
      "upload: train/safety_bike_s_000984.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000984.png\n",
      "upload: train/safety_bike_s_000921.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000921.png\n",
      "upload: train/safety_bike_s_000848.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000848.png\n",
      "upload: train/safety_bike_s_000914.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000914.png\n",
      "upload: train/safety_bike_s_001012.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001012.png\n",
      "upload: train/safety_bike_s_001338.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001338.png\n",
      "upload: train/safety_bike_s_001289.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001289.png\n",
      "upload: train/safety_bike_s_001148.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001148.png\n",
      "upload: train/safety_bike_s_001371.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001371.png\n",
      "upload: train/safety_bike_s_000989.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_000989.png\n",
      "upload: train/safety_bike_s_001010.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001010.png\n",
      "upload: train/safety_bike_s_001257.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001257.png\n",
      "upload: train/safety_bike_s_001465.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001465.png\n",
      "upload: train/safety_bike_s_001355.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001355.png\n",
      "upload: train/safety_bike_s_001474.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001474.png\n",
      "upload: train/safety_bike_s_001640.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001640.png\n",
      "upload: train/safety_bike_s_001560.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001560.png\n",
      "upload: train/safety_bike_s_001715.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001715.png\n",
      "upload: train/trail_bike_s_000016.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/trail_bike_s_000016.png\n",
      "upload: train/safety_bike_s_001552.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001552.png\n",
      "upload: train/safety_bike_s_001659.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001659.png\n",
      "upload: train/safety_bike_s_001472.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001472.png\n",
      "upload: train/safety_bike_s_001608.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001608.png\n",
      "upload: train/safety_bike_s_001590.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/train/safety_bike_s_001590.png\n",
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      "upload: test/velocipede_s_001277.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/test/velocipede_s_001277.png\n",
      "upload: test/velocipede_s_001379.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/test/velocipede_s_001379.png\n",
      "upload: test/velocipede_s_001633.png to s3://sagemaker-us-west-2-659095202386/udacity-project-4/test/velocipede_s_001633.png\n",
      "Data sync up into s3\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"DEFAULT_S3_BUCKET\"] = bucket\n",
    "os.environ[\"PREFIX\"] = prefix\n",
    "\n",
    "!aws s3 sync ./train s3://${DEFAULT_S3_BUCKET}/${PREFIX}/train/\n",
    "!aws s3 sync ./test s3://${DEFAULT_S3_BUCKET}/${PREFIX}/test/\n",
    "\n",
    "print(\"Data sync up into s3\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And that's it! You can check the bucket and verify that the items were uploaded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Training\n",
    "\n",
    "For Image Classification, Sagemaker [also expects metadata](https://docs.aws.amazon.com/sagemaker/latest/dg/image-classification.html) e.g. in the form of TSV files with labels and filepaths. We can generate these using our Pandas DataFrames from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>bicycle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>motorcycle</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        labels\n",
       "8      bicycle\n",
       "48  motorcycle"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>filenames</th>\n",
       "      <th>labels</th>\n",
       "      <th>row</th>\n",
       "      <th>flatten_pixels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>bike_s_000682.png</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "      <td>[35, 27, 31, 77, 136, 168, 103, 101, 85, 63, 6...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>bike_s_000127.png</td>\n",
       "      <td>8</td>\n",
       "      <td>30</td>\n",
       "      <td>[88, 104, 112, 81, 40, 39, 41, 47, 87, 119, 16...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>cycle_s_002598.png</td>\n",
       "      <td>8</td>\n",
       "      <td>130</td>\n",
       "      <td>[89, 88, 89, 88, 89, 90, 90, 90, 90, 90, 90, 9...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>minibike_s_000824.png</td>\n",
       "      <td>48</td>\n",
       "      <td>152</td>\n",
       "      <td>[158, 160, 163, 168, 173, 177, 178, 180, 181, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>motorcycle_s_001856.png</td>\n",
       "      <td>48</td>\n",
       "      <td>195</td>\n",
       "      <td>[255, 255, 255, 255, 255, 255, 248, 220, 201, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   filenames  labels  row  \\\n",
       "16         bike_s_000682.png       8   16   \n",
       "30         bike_s_000127.png       8   30   \n",
       "130       cycle_s_002598.png       8  130   \n",
       "152    minibike_s_000824.png      48  152   \n",
       "195  motorcycle_s_001856.png      48  195   \n",
       "\n",
       "                                        flatten_pixels  \n",
       "16   [35, 27, 31, 77, 136, 168, 103, 101, 85, 63, 6...  \n",
       "30   [88, 104, 112, 81, 40, 39, 41, 47, 87, 119, 16...  \n",
       "130  [89, 88, 89, 88, 89, 90, 90, 90, 90, 90, 90, 9...  \n",
       "152  [158, 160, 163, 168, 173, 177, 178, 180, 181, ...  \n",
       "195  [255, 255, 255, 255, 255, 255, 248, 220, 201, ...  "
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def to_metadata_file(df:pd.DataFrame, prefix:str):\n",
    "    \"\"\"\n",
    "        Save dataframe on disk, after :\n",
    "            - adding each file path on s3 in a new column\n",
    "            - updating the label to reflect the project goal: 0 (instead of 8, for bicycle), 1 (instead of 48, for motorcycle)\n",
    "    \"\"\"\n",
    "    \n",
    "    # adding a new column for image path on s3 \n",
    "    df[\"s3_path\"] = df[\"filenames\"]\n",
    "    \n",
    "    # replacing current label (8 and 48, for bicycle and motorcycle) by new ones (0 and 1, for binary classification)\n",
    "    df[\"labels\"] = df[\"labels\"].apply(lambda x: 0 if x==8 else 1) #8: label for bicycle\n",
    "    \n",
    "    return df[[\"row\", \"labels\", \"s3_path\"]].to_csv(\n",
    "        f\"{prefix}.lst\", sep=\"\\t\", index=False, header=False\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "to_metadata_file(df_train.copy(), \"train\")\n",
    "to_metadata_file(df_test.copy(), \"test\")\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 166084\n",
      "-rw-r--r-- 1 root root     36954 Aug 23 12:44  starter.ipynb\n",
      "drwxr-xr-x 9 root root      6144 Aug 23 12:44  ..\n",
      "drwx------ 2 root root      6144 Aug 28 15:42  cifar-100-python\n",
      "drwxr-xr-x 2 root root      6144 Sep  5 15:30  captured_data\n",
      "-rw-r--r-- 1 root root    398306 Sep  5 16:10 'project 4.ipynb'\n",
      "drwxr-xr-x 2 root root      6144 Sep  6 09:26  .ipynb_checkpoints\n",
      "drwxr-xr-x 2 root root     43008 Sep  7 10:35  train\n",
      "drwxr-xr-x 2 root root     14336 Sep  7 10:35  test\n",
      "-rw-r--r-- 1 root root    501695 Sep  7 12:04 'project 4-v2.ipynb'\n",
      "drwxr-xr-x 7 root root      6144 Sep  7 12:04  .\n",
      "-rw-r--r-- 1 root root 169001437 Sep  7 12:04  cifar.tar.gz\n",
      "-rw-r--r-- 1 root root      2901 Sep  7 12:04  file.png\n",
      "-rw-r--r-- 1 root root     30604 Sep  7 12:05  train.lst\n",
      "-rw-r--r-- 1 root root      5920 Sep  7 12:05  test.lst\n"
     ]
    }
   ],
   "source": [
    "!ls -lrta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also upload our manifest files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "\n",
    "# Upload files\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(f\"{prefix}/train.lst\").upload_file('./train.lst')\n",
    "boto3.Session().resource('s3').Bucket(bucket).Object(f\"{prefix}/test.lst\").upload_file('./test.lst')\n",
    "\n",
    "print(\"Done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the `bucket` and `region` info we can get the latest prebuilt container to run our training job, and define an output location on our s3 bucket for the model. Use the `image_uris` function from the SageMaker SDK to retrieve the latest `image-classification` image below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Defaulting to the only supported framework/algorithm version: 1. Ignoring framework/algorithm version: latest.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'433757028032.dkr.ecr.us-west-2.amazonaws.com/image-classification:1'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use the image_uris function to retrieve the latest 'image-classification' image \n",
    "algo_image = sagemaker.image_uris.retrieve(\"image-classification\", region, \"latest\")\n",
    "algo_image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-west-2-659095202386/udacity-project-4/models/image_model'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3_output_location = f\"s3://{bucket}/{prefix}/models/image_model\"\n",
    "s3_output_location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're ready to create an estimator! Create an estimator `img_classifier_model` that uses one instance of `ml.p3.2xlarge`. Ensure that y ou use the output location we defined above - we'll be referring to that later!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sagemaker.estimator.Estimator at 0x7f3767095db0>"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_classifier_model=sagemaker.estimator.Estimator(\n",
    "    algo_image,\n",
    "    role,\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.p3.2xlarge\",\n",
    "    output_path=s3_output_location,\n",
    "    sagemaker_session=session\n",
    ")\n",
    "\n",
    "img_classifier_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also set a few key hyperparameters and define the inputs for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sagemaker.estimator.Estimator"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# https://docs.aws.amazon.com/sagemaker/latest/dg/IC-Hyperparameter.html\n",
    "\n",
    "img_classifier_model.set_hyperparameters(\n",
    "    image_shape= '3,32,32' , # The input image dimensions, which is the same size as the input layer of the network. The format is defined as 'num_channels, height, width'\n",
    "    num_classes= 2, \n",
    "    num_training_samples= df_train.shape[0],\n",
    "    \n",
    "    # augmentation_type=\"crop_color_transform\",\n",
    "    # use_pretrained_model=1,\n",
    "    # early_stopping=True,\n",
    "    # top_k=5\n",
    ")\n",
    "\n",
    "type(img_classifier_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `image-classification` image uses four input channels with very specific input parameters. For convenience, we've provided them below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train': <sagemaker.inputs.TrainingInput at 0x7f3767097b20>,\n",
       " 'validation': <sagemaker.inputs.TrainingInput at 0x7f3767094400>,\n",
       " 'train_lst': <sagemaker.inputs.TrainingInput at 0x7f37670959f0>,\n",
       " 'validation_lst': <sagemaker.inputs.TrainingInput at 0x7f3767096560>}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sagemaker.debugger import Rule, rule_configs\n",
    "from sagemaker.session import TrainingInput\n",
    "\n",
    "model_inputs = {\n",
    "        \"train\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/{prefix}/train/\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"validation\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/{prefix}/test/\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"train_lst\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/{prefix}/train.lst\",\n",
    "            content_type=\"application/x-image\"\n",
    "        ),\n",
    "        \"validation_lst\": sagemaker.inputs.TrainingInput(\n",
    "            s3_data=f\"s3://{bucket}/{prefix}/test.lst\",\n",
    "            content_type=\"application/x-image\"\n",
    "        )\n",
    "}\n",
    "\n",
    "model_inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great, now we can train the model using the model_inputs. In the cell below, call the `fit` method on our model,:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating training-job with name: image-classification-2023-09-07-12-05-50-906\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-09-07 12:05:51 Starting - Starting the training job......\n",
      "2023-09-07 12:06:25 Starting - Preparing the instances for training......\n",
      "2023-09-07 12:07:29 Downloading - Downloading input data...\n",
      "2023-09-07 12:07:54 Training - Downloading the training image............\n",
      "2023-09-07 12:09:55 Training - Training image download completed. Training in progress..\u001b[34mDocker entrypoint called with argument(s): train\u001b[0m\n",
      "\u001b[34mRunning default environment configuration script\u001b[0m\n",
      "\u001b[34mNvidia gpu devices, drivers and cuda toolkit versions (only available on hosts with GPU):\u001b[0m\n",
      "\u001b[34mThu Sep  7 12:10:19 2023       \u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34m| NVIDIA-SMI 535.54.03              Driver Version: 535.54.03    CUDA Version: 12.2     |\u001b[0m\n",
      "\u001b[34m|-----------------------------------------+----------------------+----------------------+\u001b[0m\n",
      "\u001b[34m| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\u001b[0m\n",
      "\u001b[34m| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\u001b[0m\n",
      "\u001b[34m|                                         |                      |               MIG M. |\u001b[0m\n",
      "\u001b[34m|=========================================+======================+======================|\u001b[0m\n",
      "\u001b[34m|   0  Tesla V100-SXM2-16GB           On  | 00000000:00:1E.0 Off |                    0 |\u001b[0m\n",
      "\u001b[34m| N/A   30C    P0              23W / 300W |      0MiB / 16384MiB |      0%      Default |\u001b[0m\n",
      "\u001b[34m|                                         |                      |                  N/A |\u001b[0m\n",
      "\u001b[34m+-----------------------------------------+----------------------+----------------------+\n",
      "                                                                                         \u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34m| Processes:                                                                            |\u001b[0m\n",
      "\u001b[34m|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\u001b[0m\n",
      "\u001b[34m|        ID   ID                                                             Usage      |\u001b[0m\n",
      "\u001b[34m|=======================================================================================|\u001b[0m\n",
      "\u001b[34m|  No running processes found                                                           |\u001b[0m\n",
      "\u001b[34m+---------------------------------------------------------------------------------------+\u001b[0m\n",
      "\u001b[34mChecking for nvidia driver and cuda compatibility.\u001b[0m\n",
      "\u001b[34mCUDA Compatibility driver provided.\u001b[0m\n",
      "\u001b[34mProceeding with compatibility check between driver, cuda-toolkit and cuda-compat.\u001b[0m\n",
      "\u001b[34mDetected cuda-toolkit version: 11.1.\u001b[0m\n",
      "\u001b[34mDetected cuda-compat version: 455.32.00.\u001b[0m\n",
      "\u001b[34mDetected Nvidia driver version: 535.54.03.\u001b[0m\n",
      "\u001b[34mNvidia driver compatible with cuda-toolkit. Disabling cuda-compat.\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Reading default configuration from /opt/amazon/lib/python3.7/site-packages/image_classification/default-input.json: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,224,224', 'precision_dtype': 'float32'}\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Merging with provided configuration from /opt/ml/input/config/hyperparameters.json: {'image_shape': '3,32,32', 'num_classes': '2', 'num_training_samples': '1000'}\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Final configuration: {'use_pretrained_model': 0, 'num_layers': 152, 'epochs': 30, 'learning_rate': 0.1, 'lr_scheduler_factor': 0.1, 'optimizer': 'sgd', 'momentum': 0, 'weight_decay': 0.0001, 'beta_1': 0.9, 'beta_2': 0.999, 'eps': 1e-08, 'gamma': 0.9, 'mini_batch_size': 32, 'image_shape': '3,32,32', 'precision_dtype': 'float32', 'num_classes': '2', 'num_training_samples': '1000'}\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Searching for .lst files in /opt/ml/input/data/train_lst.\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Creating record files for train.lst\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Done creating record files...\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Searching for .lst files in /opt/ml/input/data/validation_lst.\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Creating record files for test.lst\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Done creating record files...\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] use_pretrained_model: 0\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] multi_label: 0\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Performing random weight initialization\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] ---- Parameters ----\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] num_layers: 152\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] data type: <class 'numpy.float32'>\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] epochs: 30\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] optimizer: sgd\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] momentum: 0.9\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] weight_decay: 0.0001\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] learning_rate: 0.1\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] num_training_samples: 1000\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] mini_batch_size: 32\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] image_shape: 3,32,32\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] num_classes: 2\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] augmentation_type: None\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] kv_store: device\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] checkpoint_frequency not set, will store the best model\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] --------------------\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:23 INFO 139869505746752] Setting number of threads: 7\u001b[0m\n",
      "\u001b[34m[12:10:27] /opt/brazil-pkg-cache/packages/AIAlgorithmsMXNet/AIAlgorithmsMXNet-1.4.x_ecl_Cuda_11.1.x.230.0/AL2_x86_64/generic-flavor/src/src/operator/nn/./cudnn/./cudnn_algoreg-inl.h:97: Running performance tests to find the best convolution algorithm, this can take a while... (setting env variable MXNET_CUDNN_AUTOTUNE_DEFAULT to 0 to disable)\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:32 INFO 139869505746752] Epoch[0] Batch [20]#011Speed: 118.663 samples/sec#011accuracy=0.556548\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:34 INFO 139869505746752] Epoch[0] Train-accuracy=0.591734\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:34 INFO 139869505746752] Epoch[0] Time cost=7.106\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:35 INFO 139869505746752] Epoch[0] Validation-accuracy=0.500000\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:35 INFO 139869505746752] Storing the best model with validation accuracy: 0.500000\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:35 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0001.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:39 INFO 139869505746752] Epoch[1] Batch [20]#011Speed: 177.261 samples/sec#011accuracy=0.663690\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:41 INFO 139869505746752] Epoch[1] Train-accuracy=0.662298\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:41 INFO 139869505746752] Epoch[1] Time cost=5.347\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:41 INFO 139869505746752] Epoch[1] Validation-accuracy=0.781250\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:41 INFO 139869505746752] Storing the best model with validation accuracy: 0.781250\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:42 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0002.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:45 INFO 139869505746752] Epoch[2] Batch [20]#011Speed: 179.032 samples/sec#011accuracy=0.686012\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:47 INFO 139869505746752] Epoch[2] Train-accuracy=0.679435\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:47 INFO 139869505746752] Epoch[2] Time cost=5.287\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:48 INFO 139869505746752] Epoch[2] Validation-accuracy=0.750000\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:52 INFO 139869505746752] Epoch[3] Batch [20]#011Speed: 182.061 samples/sec#011accuracy=0.715774\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:53 INFO 139869505746752] Epoch[3] Train-accuracy=0.748992\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:53 INFO 139869505746752] Epoch[3] Time cost=5.235\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:54 INFO 139869505746752] Epoch[3] Validation-accuracy=0.803571\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:54 INFO 139869505746752] Storing the best model with validation accuracy: 0.803571\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:55 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0004.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:10:58 INFO 139869505746752] Epoch[4] Batch [20]#011Speed: 181.436 samples/sec#011accuracy=0.787202\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:00 INFO 139869505746752] Epoch[4] Train-accuracy=0.783266\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:00 INFO 139869505746752] Epoch[4] Time cost=5.254\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:00 INFO 139869505746752] Epoch[4] Validation-accuracy=0.817708\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:01 INFO 139869505746752] Storing the best model with validation accuracy: 0.817708\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:01 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0005.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:05 INFO 139869505746752] Epoch[5] Batch [20]#011Speed: 178.561 samples/sec#011accuracy=0.830357\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:06 INFO 139869505746752] Epoch[5] Train-accuracy=0.818548\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:06 INFO 139869505746752] Epoch[5] Time cost=5.308\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:07 INFO 139869505746752] Epoch[5] Validation-accuracy=0.807292\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:11 INFO 139869505746752] Epoch[6] Batch [20]#011Speed: 180.428 samples/sec#011accuracy=0.824405\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:13 INFO 139869505746752] Epoch[6] Train-accuracy=0.817540\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:13 INFO 139869505746752] Epoch[6] Time cost=5.283\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:13 INFO 139869505746752] Epoch[6] Validation-accuracy=0.802083\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:17 INFO 139869505746752] Epoch[7] Batch [20]#011Speed: 182.861 samples/sec#011accuracy=0.854167\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:19 INFO 139869505746752] Epoch[7] Train-accuracy=0.853831\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:19 INFO 139869505746752] Epoch[7] Time cost=5.219\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:19 INFO 139869505746752] Epoch[7] Validation-accuracy=0.825893\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:20 INFO 139869505746752] Storing the best model with validation accuracy: 0.825893\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:20 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0008.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:24 INFO 139869505746752] Epoch[8] Batch [20]#011Speed: 180.568 samples/sec#011accuracy=0.854167\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:25 INFO 139869505746752] Epoch[8] Train-accuracy=0.851815\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:25 INFO 139869505746752] Epoch[8] Time cost=5.274\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:26 INFO 139869505746752] Epoch[8] Validation-accuracy=0.770833\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:30 INFO 139869505746752] Epoch[9] Batch [20]#011Speed: 182.634 samples/sec#011accuracy=0.857143\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:32 INFO 139869505746752] Epoch[9] Train-accuracy=0.847782\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:32 INFO 139869505746752] Epoch[9] Time cost=5.231\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:32 INFO 139869505746752] Epoch[9] Validation-accuracy=0.822917\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:36 INFO 139869505746752] Epoch[10] Batch [20]#011Speed: 180.419 samples/sec#011accuracy=0.876488\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:38 INFO 139869505746752] Epoch[10] Train-accuracy=0.866935\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:38 INFO 139869505746752] Epoch[10] Time cost=5.285\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:38 INFO 139869505746752] Epoch[10] Validation-accuracy=0.791667\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:42 INFO 139869505746752] Epoch[11] Batch [20]#011Speed: 182.230 samples/sec#011accuracy=0.866071\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:44 INFO 139869505746752] Epoch[11] Train-accuracy=0.861895\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:44 INFO 139869505746752] Epoch[11] Time cost=5.240\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:45 INFO 139869505746752] Epoch[11] Validation-accuracy=0.794643\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:49 INFO 139869505746752] Epoch[12] Batch [20]#011Speed: 180.588 samples/sec#011accuracy=0.904762\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:50 INFO 139869505746752] Epoch[12] Train-accuracy=0.900202\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:50 INFO 139869505746752] Epoch[12] Time cost=5.260\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:51 INFO 139869505746752] Epoch[12] Validation-accuracy=0.812500\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:55 INFO 139869505746752] Epoch[13] Batch [20]#011Speed: 181.972 samples/sec#011accuracy=0.912202\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:57 INFO 139869505746752] Epoch[13] Train-accuracy=0.909274\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:57 INFO 139869505746752] Epoch[13] Time cost=5.267\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:57 INFO 139869505746752] Epoch[13] Validation-accuracy=0.843750\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:57 INFO 139869505746752] Storing the best model with validation accuracy: 0.843750\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:11:58 INFO 139869505746752] Saved checkpoint to \"/opt/ml/model/image-classification-0014.params\"\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:01 INFO 139869505746752] Epoch[14] Batch [20]#011Speed: 180.506 samples/sec#011accuracy=0.944940\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:03 INFO 139869505746752] Epoch[14] Train-accuracy=0.938508\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:03 INFO 139869505746752] Epoch[14] Time cost=5.341\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:04 INFO 139869505746752] Epoch[14] Validation-accuracy=0.822917\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:08 INFO 139869505746752] Epoch[15] Batch [20]#011Speed: 183.676 samples/sec#011accuracy=0.913690\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:09 INFO 139869505746752] Epoch[15] Train-accuracy=0.910282\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:09 INFO 139869505746752] Epoch[15] Time cost=5.228\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:10 INFO 139869505746752] Epoch[15] Validation-accuracy=0.758929\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:14 INFO 139869505746752] Epoch[16] Batch [20]#011Speed: 180.908 samples/sec#011accuracy=0.934524\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:16 INFO 139869505746752] Epoch[16] Train-accuracy=0.924395\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:16 INFO 139869505746752] Epoch[16] Time cost=5.283\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:16 INFO 139869505746752] Epoch[16] Validation-accuracy=0.791667\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:20 INFO 139869505746752] Epoch[17] Batch [20]#011Speed: 181.428 samples/sec#011accuracy=0.937500\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:22 INFO 139869505746752] Epoch[17] Train-accuracy=0.932460\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:22 INFO 139869505746752] Epoch[17] Time cost=5.261\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:22 INFO 139869505746752] Epoch[17] Validation-accuracy=0.807292\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:26 INFO 139869505746752] Epoch[18] Batch [20]#011Speed: 181.650 samples/sec#011accuracy=0.937500\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:28 INFO 139869505746752] Epoch[18] Train-accuracy=0.944556\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:28 INFO 139869505746752] Epoch[18] Time cost=5.229\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:29 INFO 139869505746752] Epoch[18] Validation-accuracy=0.755208\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:33 INFO 139869505746752] Epoch[19] Batch [20]#011Speed: 181.333 samples/sec#011accuracy=0.959821\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:34 INFO 139869505746752] Epoch[19] Train-accuracy=0.962702\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:34 INFO 139869505746752] Epoch[19] Time cost=5.267\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:35 INFO 139869505746752] Epoch[19] Validation-accuracy=0.839286\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:39 INFO 139869505746752] Epoch[20] Batch [20]#011Speed: 180.615 samples/sec#011accuracy=0.977679\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:41 INFO 139869505746752] Epoch[20] Train-accuracy=0.971774\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:41 INFO 139869505746752] Epoch[20] Time cost=5.284\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:41 INFO 139869505746752] Epoch[20] Validation-accuracy=0.833333\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:45 INFO 139869505746752] Epoch[21] Batch [20]#011Speed: 182.710 samples/sec#011accuracy=0.949405\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:47 INFO 139869505746752] Epoch[21] Train-accuracy=0.954637\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:47 INFO 139869505746752] Epoch[21] Time cost=5.225\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:47 INFO 139869505746752] Epoch[21] Validation-accuracy=0.817708\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:51 INFO 139869505746752] Epoch[22] Batch [20]#011Speed: 182.155 samples/sec#011accuracy=0.970238\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:53 INFO 139869505746752] Epoch[22] Train-accuracy=0.963710\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:53 INFO 139869505746752] Epoch[22] Time cost=5.237\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:54 INFO 139869505746752] Epoch[22] Validation-accuracy=0.786458\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:58 INFO 139869505746752] Epoch[23] Batch [20]#011Speed: 183.933 samples/sec#011accuracy=0.968750\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:59 INFO 139869505746752] Epoch[23] Train-accuracy=0.966734\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:12:59 INFO 139869505746752] Epoch[23] Time cost=5.213\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:00 INFO 139869505746752] Epoch[23] Validation-accuracy=0.834821\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:04 INFO 139869505746752] Epoch[24] Batch [20]#011Speed: 176.774 samples/sec#011accuracy=0.964286\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:06 INFO 139869505746752] Epoch[24] Train-accuracy=0.959677\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:06 INFO 139869505746752] Epoch[24] Time cost=5.349\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:06 INFO 139869505746752] Epoch[24] Validation-accuracy=0.822917\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:10 INFO 139869505746752] Epoch[25] Batch [20]#011Speed: 183.717 samples/sec#011accuracy=0.953869\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:12 INFO 139869505746752] Epoch[25] Train-accuracy=0.957661\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:12 INFO 139869505746752] Epoch[25] Time cost=5.210\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:13 INFO 139869505746752] Epoch[25] Validation-accuracy=0.828125\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:17 INFO 139869505746752] Epoch[26] Batch [20]#011Speed: 180.143 samples/sec#011accuracy=0.971726\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:18 INFO 139869505746752] Epoch[26] Train-accuracy=0.969758\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:18 INFO 139869505746752] Epoch[26] Time cost=5.278\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:19 INFO 139869505746752] Epoch[26] Validation-accuracy=0.812500\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:23 INFO 139869505746752] Epoch[27] Batch [20]#011Speed: 181.118 samples/sec#011accuracy=0.995536\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:25 INFO 139869505746752] Epoch[27] Train-accuracy=0.988911\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:25 INFO 139869505746752] Epoch[27] Time cost=5.261\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:25 INFO 139869505746752] Epoch[27] Validation-accuracy=0.803571\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:29 INFO 139869505746752] Epoch[28] Batch [20]#011Speed: 180.643 samples/sec#011accuracy=0.988095\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:31 INFO 139869505746752] Epoch[28] Train-accuracy=0.988911\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:31 INFO 139869505746752] Epoch[28] Time cost=5.286\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:31 INFO 139869505746752] Epoch[28] Validation-accuracy=0.828125\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:35 INFO 139869505746752] Epoch[29] Batch [20]#011Speed: 181.192 samples/sec#011accuracy=0.988095\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:37 INFO 139869505746752] Epoch[29] Train-accuracy=0.986895\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:37 INFO 139869505746752] Epoch[29] Time cost=5.273\u001b[0m\n",
      "\u001b[34m[09/07/2023 12:13:38 INFO 139869505746752] Epoch[29] Validation-accuracy=0.817708\u001b[0m\n",
      "\n",
      "2023-09-07 12:14:07 Uploading - Uploading generated training model\n",
      "2023-09-07 12:14:07 Completed - Training job completed\n",
      "Training seconds: 398\n",
      "Billable seconds: 398\n"
     ]
    }
   ],
   "source": [
    "img_classifier_model.fit(model_inputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If all goes well, you'll end up with a model topping out above `.8` validation accuracy. With only 1000 training samples in the CIFAR dataset, that's pretty good. We could definitely pursue data augmentation & gathering more samples to help us improve further, but for now let's proceed to deploy our model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting ready to deploy\n",
    "\n",
    "To begin with, let's configure Model Monitor to track our deployment. We'll define a `DataCaptureConfig` below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture'"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_capture_url = f\"s3://{bucket}/{prefix}/data_capture\"\n",
    "data_capture_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sagemaker.model_monitor import DataCaptureConfig\n",
    "\n",
    "data_capture_config = DataCaptureConfig(\n",
    "    destination_s3_uri= data_capture_url, #f\"s3://{bucket}/{prefix}/data_capture\",\n",
    "    enable_capture=True,\n",
    "    sampling_percentage=70\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the `destination_s3_uri` parameter: At the end of the project, we can explore the `data_capture` directory in S3 to find crucial data about the inputs and outputs Model Monitor has observed on our model endpoint over time.\n",
    "\n",
    "With that done, deploy your model on a single `ml.m5.xlarge` instance with the data capture config attached:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: image-classification-2023-09-07-12-14-33-954\n",
      "INFO:sagemaker:Creating endpoint-config with name image-classification-2023-09-07-12-14-33-954\n",
      "INFO:sagemaker:Creating endpoint with name image-classification-2023-09-07-12-14-33-954\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------!"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sagemaker.base_predictor.Predictor at 0x7f376733d780>"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "deployment = img_classifier_model.deploy(\n",
    "    data_capture_config=data_capture_config,\n",
    "    initial_instance_count=1,\n",
    "    instance_type=\"ml.m5.xlarge\"\n",
    ")\n",
    "\n",
    "deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'image-classification-2023-09-07-12-14-33-954'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "endpoint = deployment.endpoint_name\n",
    "endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note the endpoint name for later as well.\n",
    "\n",
    "Next, instantiate a Predictor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:sagemaker.deprecations:The class RealTimePredictor has been renamed in sagemaker>=2.\n",
      "See: https://sagemaker.readthedocs.io/en/stable/v2.html for details.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sagemaker.deprecations.deprecated_class.<locals>.DeprecatedClass at 0x7f376785e6e0>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reference: https://github.com/aws/sagemaker-python-sdk/issues/166\n",
    "from sagemaker.predictor import RealTimePredictor\n",
    "\n",
    "predictor = RealTimePredictor(endpoint)\n",
    "predictor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Though sagemaker.predictor.RealTimePredictor is deprecated and has been renamed in sagemaker>=2 (kindly refer to [doc]( https://sagemaker.readthedocs.io/en/stable/v2.html), I am still using it instead of using sagemaker.predictor.Predictor in order to conform to this project specs.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.predictor.Predictor\n",
    "# from sagemaker.predictor import Predictor\n",
    "\n",
    "# predictor = Predictor(endpoint)\n",
    "# predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the code snippet below we are going to prepare one of your saved images for prediction. Use the predictor to process the `payload`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'[0.9811985492706299, 0.018801426514983177]'"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sagemaker.serializers import IdentitySerializer\n",
    "import base64\n",
    "\n",
    "predictor.serializer = IdentitySerializer(\"image/png\")\n",
    "with open(\"./test/bicycle_s_001789.png\", \"rb\") as f:\n",
    "    payload = f.read()\n",
    "\n",
    "    \n",
    "inference = predictor.predict(payload)\n",
    "inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[0.9811985492706299, 0.018801426514983177]'"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inference.decode(\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9811985492706299, 0.018801426514983177]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eval(inference.decode(\"utf-8\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Your `inference` object is an array of two values, the predicted probability value for each of your classes (bicycle and motorcycle respectively.) So, for example, a value of `b'[0.91, 0.09]'` indicates the probability of being a bike is 91% and being a motorcycle is 9%."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Draft Lambdas and Step Function Workflow\n",
    "\n",
    "Your operations team uses Step Functions to orchestrate serverless workflows. One of the nice things about Step Functions is that [workflows can call other workflows](https://docs.aws.amazon.com/step-functions/latest/dg/connect-stepfunctions.html), so the team can easily plug your workflow into the broader production architecture for Scones Unlimited.\n",
    "\n",
    "In this next stage you're going to write and deploy three Lambda functions, and then use the Step Functions visual editor to chain them together! Our functions are going to work with a simple data object:\n",
    "\n",
    "```python\n",
    "{\n",
    "    \"inferences\": [], # Output of predictor.predict\n",
    "    \"s3_key\": \"\", # Source data S3 key\n",
    "    \"s3_bucket\": \"\", # Source data S3 bucket\n",
    "    \"image_data\": \"\"  # base64 encoded string containing the image data\n",
    "}\n",
    "```\n",
    "\n",
    "A good test object that you can use for Lambda tests and Step Function executions, throughout the next section, might look like this:\n",
    "\n",
    "```python\n",
    "{\n",
    "  \"image_data\": \"\",\n",
    "  \"s3_bucket\": MY_BUCKET_NAME, # Fill in with your bucket\n",
    "  \"s3_key\": \"test/bicycle_s_000513.png\"\n",
    "}\n",
    "```\n",
    "\n",
    "Using these fields, your functions can read and write the necessary data to execute your workflow. Let's start with the first function. Your first Lambda function will copy an object from S3, base64 encode it, and then return it to the step function as `image_data` in an event.\n",
    "\n",
    "Go to the Lambda dashboard and create a new Lambda function with a descriptive name like \"serializeImageData\" and select thr 'Python 3.8' runtime. Add the same permissions as the SageMaker role you created earlier. (Reminder: you do this in the Configuration tab under \"Permissions\"). Once you're ready, use the starter code below to craft your Lambda handler:\n",
    "\n",
    "```python\n",
    "import json\n",
    "import boto3\n",
    "import base64\n",
    "\n",
    "s3 = boto3.client('s3')\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "    \"\"\"A function to serialize target data from S3\"\"\"\n",
    "    \n",
    "    # Get the s3 address from the Step Function event input\n",
    "    key = ## TODO: fill in\n",
    "    bucket = ## TODO: fill in\n",
    "    \n",
    "    # Download the data from s3 to /tmp/image.png\n",
    "    ## TODO: fill in\n",
    "    \n",
    "    # We read the data from a file\n",
    "    with open(\"/tmp/image.png\", \"rb\") as f:\n",
    "        image_data = base64.b64encode(f.read())\n",
    "\n",
    "    # Pass the data back to the Step Function\n",
    "    print(\"Event:\", event.keys())\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': {\n",
    "            \"image_data\": image_data,\n",
    "            \"s3_bucket\": bucket,\n",
    "            \"s3_key\": key,\n",
    "            \"inferences\": []\n",
    "        }\n",
    "    }\n",
    "```\n",
    "\n",
    "The next function is responsible for the classification part - we're going to take the image output from the previous function, decode it, and then pass inferences back to the the Step Function.\n",
    "\n",
    "Because this Lambda will have runtime dependencies (i.e. the SageMaker SDK) you'll need to package them in your function. *Key reading:* https://docs.aws.amazon.com/lambda/latest/dg/python-package-create.html#python-package-create-with-dependency\n",
    "\n",
    "Create a new Lambda function with the same rights and a descriptive name, then fill in the starter code below for your classifier Lambda.\n",
    "\n",
    "```python\n",
    "import json\n",
    "import sagemaker\n",
    "import base64\n",
    "from sagemaker.serializers import IdentitySerializer\n",
    "\n",
    "# Fill this in with the name of your deployed model\n",
    "ENDPOINT = ## TODO: fill in\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "\n",
    "    # Decode the image data\n",
    "    image = base64.b64decode(## TODO: fill in)\n",
    "\n",
    "    # Instantiate a Predictor\n",
    "    predictor = ## TODO: fill in\n",
    "\n",
    "    # For this model the IdentitySerializer needs to be \"image/png\"\n",
    "    predictor.serializer = IdentitySerializer(\"image/png\")\n",
    "    \n",
    "    # Make a prediction:\n",
    "    inferences = ## TODO: fill in\n",
    "    \n",
    "    # We return the data back to the Step Function    \n",
    "    event[\"inferences\"] = inferences.decode('utf-8')\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': json.dumps(event)\n",
    "    }\n",
    "```\n",
    "\n",
    "Finally, we need to filter low-confidence inferences. Define a threshold between 1.00 and 0.000 for your model: what is reasonble for you? If the model predicts at `.70` for it's highest confidence label, do we want to pass that inference along to downstream systems? Make one last Lambda function and tee up the same permissions:\n",
    "\n",
    "```python\n",
    "import json\n",
    "\n",
    "\n",
    "THRESHOLD = .93\n",
    "\n",
    "\n",
    "def lambda_handler(event, context):\n",
    "    \n",
    "    # Grab the inferences from the event\n",
    "    inferences = ## TODO: fill in\n",
    "    \n",
    "    # Check if any values in our inferences are above THRESHOLD\n",
    "    meets_threshold = ## TODO: fill in\n",
    "    \n",
    "    # If our threshold is met, pass our data back out of the\n",
    "    # Step Function, else, end the Step Function with an error\n",
    "    if meets_threshold:\n",
    "        pass\n",
    "    else:\n",
    "        raise(\"THRESHOLD_CONFIDENCE_NOT_MET\")\n",
    "\n",
    "    return {\n",
    "        'statusCode': 200,\n",
    "        'body': json.dumps(event)\n",
    "    }\n",
    "```\n",
    "Once you have tested the lambda functions, save the code for each lambda function in a python script called 'lambda.py'.\n",
    "\n",
    "With your lambdas in place, you can use the Step Functions visual editor to construct a workflow that chains them together. In the Step Functions console you'll have the option to author a Standard step function *Visually*.\n",
    "\n",
    "When the visual editor opens, you'll have many options to add transitions in your workflow. We're going to keep it simple and have just one: to invoke Lambda functions. Add three of them chained together. For each one, you'll be able to select the Lambda functions you just created in the proper order, filter inputs and outputs, and give them descriptive names.\n",
    "\n",
    "Make sure that you:\n",
    "\n",
    "1. Are properly filtering the inputs and outputs of your invokations (e.g. `$.body`)\n",
    "2. Take care to remove the error handling from the last function - it's supposed to \"fail loudly\" for your operations colleagues!\n",
    "\n",
    "Take a screenshot of your working step function in action and export the step function as JSON for your submission package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "Great! Now you can use the files in `./test` as test files for our workflow. Depending on our threshold, our workflow should reliably pass predictions about images from `./test` on to downstream systems, while erroring out for inferences below our confidence threshold!\n",
    "\n",
    "### Testing and Evaluation\n",
    "\n",
    "Do several step function invokations using data from the `./test` folder. This process should give you confidence that the workflow both *succeeds* AND *fails* as expected. In addition, SageMaker Model Monitor will generate recordings of your data and inferences which we can visualize.\n",
    "\n",
    "Here's a function that can help you generate test inputs for your invokations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"image_data\": \"\", \"s3_bucket\": \"sagemaker-us-west-2-659095202386\", \"s3_key\": \"udacity-project-4/test/safety_bike_s_000438.png\", \"prefix\": \"udacity-project-4\"}'"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "import boto3\n",
    "import json\n",
    "\n",
    "\n",
    "def generate_test_case():\n",
    "    # Setup s3 in boto3\n",
    "    s3 = boto3.resource('s3')\n",
    "    \n",
    "    # Randomly pick from sfn or test folders in our bucket\n",
    "    # https://techoverflow.net/2021/03/08/how-to-filter-for-objects-in-a-given-s3-directory-using-boto3/\n",
    "    objects = s3.Bucket(bucket).objects.filter(Prefix=f\"{prefix}/test/\")\n",
    "    # print(objects)\n",
    "    \n",
    "    # Grab any random object key from that folder!\n",
    "    obj = random.choice([x.key for x in objects])\n",
    "    \n",
    "    return json.dumps({\n",
    "        \"image_data\": \"\",\n",
    "        \"s3_bucket\": bucket,\n",
    "        \"s3_key\": obj,\n",
    "        \n",
    "        \"prefix\":prefix\n",
    "    })\n",
    "\n",
    "def generate_test_case_():\n",
    "    s3 = boto3.client(\"s3\")\n",
    "    response = s3.list_objects_v2(\n",
    "            Bucket=bucket,\n",
    "            Prefix =f\"{prefix}/test\",\n",
    "            MaxKeys=100)\n",
    "    \n",
    "    print(response[\"Contents\"])\n",
    "\n",
    "generate_test_case()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"image_data\": \"\", \"s3_bucket\": \"sagemaker-us-west-2-659095202386\", \"s3_key\": \"udacity-project-4/test/cycle_s_001648.png\", \"prefix\": \"udacity-project-4\"}'"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_test_case()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'{\"image_data\": \"\", \"s3_bucket\": \"sagemaker-us-west-2-659095202386\", \"s3_key\": \"udacity-project-4/test/safety_bike_s_001690.png\", \"prefix\": \"udacity-project-4\"}'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generate_test_case()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Step Function dashboard for your new function, you can create new executions and copy in the generated test cases. Do several executions so that you can generate data you can evaluate and visualize.\n",
    "\n",
    "Once you've done several executions, let's visualize the record of our inferences. Pull in the JSONLines data from your inferences like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# data_capture_url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from sagemaker.s3 import S3Downloader\n",
    "\n",
    "# # In S3 your data will be saved to a datetime-aware path\n",
    "# # Find a path related to a datetime you're interested in\n",
    "# data_path = \"\"\n",
    "\n",
    "# S3Downloader.download(data_path, \"captured_data\")\n",
    "\n",
    "# # Feel free to repeat this multiple times and pull in more data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In S3 your data will be saved to a datetime-aware path\n",
    "# Find a path related to a datetime you're interested in\n",
    "# Feel free to repeat this multiple times and pull in more data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.s3 import S3Downloader\n",
    "import boto3\n",
    "\n",
    "def download_capture_files(bucket:str,capture_folder:str):\n",
    "    # Setup s3 in boto3\n",
    "    s3 = boto3.resource('s3')\n",
    "    \n",
    "    # Randomly pick from sfn or test folders in our bucket\n",
    "    # https://techoverflow.net/2021/03/08/how-to-filter-for-objects-in-a-given-s3-directory-using-boto3/\n",
    "    objects = s3.Bucket(bucket).objects.filter(Prefix=capture_folder)\n",
    "    # print(objects)\n",
    "    \n",
    "    # Get files names\n",
    "    files = [x.key for x in objects]\n",
    "    \n",
    "    for file in files:\n",
    "        data_path = f\"s3://{os.path.join(bucket, file)}\"\n",
    "        print(f\"Processing file {data_path} ...\")\n",
    "        S3Downloader.download(data_path, \"captured_data\")\n",
    "    \n",
    "    print(\"Done\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/12/28-55-057-8df3f4da-10c7-4630-bd9a-06a194bc3d9d.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/12/40-51-274-4393e003-c3f9-4075-bdf9-363ea6946cde.jsonl ...\n",
      "Done\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/10-45-594-227a6f78-67a9-4979-87ed-4ecdf70f06f6.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/30-51-071-f3e162c9-0020-4dc1-8f39-8bd69ef0f838.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/40-54-217-c6202f75-dd1e-4462-a7e7-493c26ef0ec1.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/48-49-505-314b45a2-629e-4000-af03-35ef9c3aed30.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/54-12-854-d890778d-8323-43c0-a2c5-398b858e6aba.jsonl ...\n",
      "Done\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/14/00-25-148-d28f14d6-1cd3-40bb-94f0-7a3c10003718.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/14/01-40-609-6dd40f52-1036-4ac2-8b91-219e9d89ce7b.jsonl ...\n",
      "Processing file s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/14/03-57-607-1f07d5f2-5492-449a-9bdc-79441e08be13.jsonl ...\n",
      "Done\n"
     ]
    }
   ],
   "source": [
    "capture_folders=[\n",
    "    \"udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/12/\",\n",
    "    \"udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/13/\",\n",
    "    \"udacity-project-4/data_capture/image-classification-2023-09-07-12-14-33-954/AllTraffic/2023/09/07/14/\",\n",
    "]\n",
    "\n",
    "for folder in capture_folders:\n",
    "    download_capture_files(bucket, folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# data_path = \"s3://sagemaker-us-west-2-659095202386/udacity-project-4/data_capture/image-classification-2023-09-05-10-24-54-478/AllTraffic/2023/09/05/14/25-08-500-d6392e73-0915-4e27-bf51-8b7e1f95cab1.jsonl\"\n",
    "\n",
    "# S3Downloader.download(data_path, \"captured_data\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data are in JSONLines format, where multiple valid JSON objects are stacked on top of eachother in a single `jsonl` file. We'll import an open-source library, `jsonlines` that was purpose built for parsing this format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting jsonlines\n",
      "  Obtaining dependency information for jsonlines from https://files.pythonhosted.org/packages/f8/62/d9ba6323b9202dd2fe166beab8a86d29465c41a0288cbe229fac60c1ab8d/jsonlines-4.0.0-py3-none-any.whl.metadata\n",
      "  Using cached jsonlines-4.0.0-py3-none-any.whl.metadata (1.6 kB)\n",
      "Requirement already satisfied: attrs>=19.2.0 in /opt/conda/lib/python3.10/site-packages (from jsonlines) (23.1.0)\n",
      "Using cached jsonlines-4.0.0-py3-none-any.whl (8.7 kB)\n",
      "Installing collected packages: jsonlines\n",
      "Successfully installed jsonlines-4.0.0\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install jsonlines\n",
    "import jsonlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can extract the data from each of the source files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jsonl: 30-51-071-f3e162c9-0020-4dc1-8f39-8bd69ef0f838.jsonl\n",
      "jsonl: 40-51-274-4393e003-c3f9-4075-bdf9-363ea6946cde.jsonl\n",
      "jsonl: 54-12-854-d890778d-8323-43c0-a2c5-398b858e6aba.jsonl\n",
      "jsonl: 01-40-609-6dd40f52-1036-4ac2-8b91-219e9d89ce7b.jsonl\n",
      "jsonl: .ipynb_checkpoints\n",
      "jsonl: 40-54-217-c6202f75-dd1e-4462-a7e7-493c26ef0ec1.jsonl\n",
      "jsonl: 00-25-148-d28f14d6-1cd3-40bb-94f0-7a3c10003718.jsonl\n",
      "jsonl: 28-55-057-8df3f4da-10c7-4630-bd9a-06a194bc3d9d.jsonl\n",
      "jsonl: 03-57-607-1f07d5f2-5492-449a-9bdc-79441e08be13.jsonl\n",
      "jsonl: 10-45-594-227a6f78-67a9-4979-87ed-4ecdf70f06f6.jsonl\n",
      "jsonl: 48-49-505-314b45a2-629e-4000-af03-35ef9c3aed30.jsonl\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "# List the file names we downloaded\n",
    "file_handles = os.listdir(\"./captured_data\")\n",
    "\n",
    "# Dump all the data into an array\n",
    "json_data = []\n",
    "for jsonl in file_handles:\n",
    "    print(f\"jsonl: {jsonl}\")\n",
    "    #if os.path.isfile(jsonl):\n",
    "    if jsonl != \".ipynb_checkpoints\": # a fix to avoid the notebook checkpoint being used (not enough time to restart the work and avoid this file being present)\n",
    "        with jsonlines.open(f\"./captured_data/{jsonl}\") as f:\n",
    "            json_data.append(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 88\n",
      "drwxr-xr-x 2 root root 6144 Sep  7 13:18 .ipynb_checkpoints\n",
      "-rw-r--r-- 1 root root 3648 Sep  7 14:10 28-55-057-8df3f4da-10c7-4630-bd9a-06a194bc3d9d.jsonl\n",
      "-rw-r--r-- 1 root root 4330 Sep  7 14:10 40-51-274-4393e003-c3f9-4075-bdf9-363ea6946cde.jsonl\n",
      "-rw-r--r-- 1 root root 4013 Sep  7 14:10 10-45-594-227a6f78-67a9-4979-87ed-4ecdf70f06f6.jsonl\n",
      "-rw-r--r-- 1 root root 4267 Sep  7 14:10 30-51-071-f3e162c9-0020-4dc1-8f39-8bd69ef0f838.jsonl\n",
      "-rw-r--r-- 1 root root 3284 Sep  7 14:10 40-54-217-c6202f75-dd1e-4462-a7e7-493c26ef0ec1.jsonl\n",
      "-rw-r--r-- 1 root root 4166 Sep  7 14:10 48-49-505-314b45a2-629e-4000-af03-35ef9c3aed30.jsonl\n",
      "-rw-r--r-- 1 root root 4502 Sep  7 14:10 54-12-854-d890778d-8323-43c0-a2c5-398b858e6aba.jsonl\n",
      "-rw-r--r-- 1 root root 4618 Sep  7 14:10 00-25-148-d28f14d6-1cd3-40bb-94f0-7a3c10003718.jsonl\n",
      "-rw-r--r-- 1 root root 8561 Sep  7 14:10 01-40-609-6dd40f52-1036-4ac2-8b91-219e9d89ce7b.jsonl\n",
      "-rw-r--r-- 1 root root 8486 Sep  7 14:10 03-57-607-1f07d5f2-5492-449a-9bdc-79441e08be13.jsonl\n",
      "drwxr-xr-x 3 root root 6144 Sep  7 14:10 .\n",
      "drwxr-xr-x 7 root root 6144 Sep  7 14:21 ..\n"
     ]
    }
   ],
   "source": [
    "!ls -lart ./captured_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'captureData': {'endpointInput': {'observedContentType': 'image/png',\n",
       "   'mode': 'INPUT',\n",
       "   'data': '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',\n",
       "   'encoding': 'BASE64'},\n",
       "  'endpointOutput': {'observedContentType': 'application/json',\n",
       "   'mode': 'OUTPUT',\n",
       "   'data': '[0.0077815307304263115, 0.9922184944152832]',\n",
       "   'encoding': 'JSON'}},\n",
       " 'eventMetadata': {'eventId': '6cc444b7-7536-4384-a3ca-bde57da2967a',\n",
       "  'inferenceTime': '2023-09-07T13:30:51Z'},\n",
       " 'eventVersion': '0'}"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data should now be a list of dictionaries, with significant nesting. We'll give you an example of some code that grabs data out of the objects and visualizes it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0.0077815307304263115, 0.9922184944152832], '2023-09-07T13:30:51Z')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Define how we'll get our data\n",
    "def simple_getter(obj):\n",
    "    inferences = obj[\"captureData\"][\"endpointOutput\"][\"data\"]\n",
    "    timestamp = obj[\"eventMetadata\"][\"inferenceTime\"]\n",
    "    return json.loads(inferences), timestamp\n",
    "\n",
    "simple_getter(json_data[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, here's an example of a visualization you can build with this data. In this last part, you will take some time and build your own - the captured data has the input images, the resulting inferences, and the timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Populate the data for the x and y axis\n",
    "x = []\n",
    "y = []\n",
    "for obj in json_data:\n",
    "    inference, timestamp = simple_getter(obj)\n",
    "    \n",
    "    y.append(max(inference))\n",
    "    x.append(timestamp)\n",
    "\n",
    "# Todo: here is an visualization example, take some time to build another visual that helps monitor the result\n",
    "# Plot the data\n",
    "plt.scatter(x, y, c=['r' if k<.94 else 'b' for k in y ])\n",
    "plt.axhline(y=0.94, color='g', linestyle='--')\n",
    "plt.ylim(bottom=.88)\n",
    "\n",
    "# Add labels\n",
    "plt.ylabel(\"Confidence\")\n",
    "plt.suptitle(\"Observed Recent Inferences\", size=14)\n",
    "plt.title(\"Pictured with confidence threshold for production use\", size=10)\n",
    "\n",
    "# Give it some pizzaz!\n",
    "plt.style.use(\"Solarize_Light2\")\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Todo: build your own visualization\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Populate the data for the x and y axis\n",
    "x = []\n",
    "y = []\n",
    "for obj in json_data:\n",
    "    inference, timestamp = simple_getter(obj)\n",
    "    \n",
    "    y.append(max(inference))\n",
    "    x.append(timestamp)\n",
    "\n",
    "THRESHOLD = .8 #.93\n",
    "\n",
    "# Plot the data\n",
    "plt.scatter(x, y, c=['r' if k<THRESHOLD else 'b' for k in y ])\n",
    "plt.axhline(y=THRESHOLD, color='g')\n",
    "# plt.ylim(bottom=.88)\n",
    "\n",
    "# Add labels\n",
    "plt.ylabel(\"Confidence\")\n",
    "plt.suptitle(\"Observed Recent Inferences\", size=14)\n",
    "plt.title(\"Pictured with confidence threshold for production use\", size=10)\n",
    "\n",
    "\n",
    "plt.style.use(\"Solarize_Light2\")\n",
    "plt.gcf().autofmt_xdate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Congratulations!\n",
    "\n",
    "You've reached the end of the project. In this project you created an event-drivent ML workflow that can be incorporated into the Scones Unlimited production architecture. You used the SageMaker Estimator API to deploy your SageMaker Model and Endpoint, and you used AWS Lambda and Step Functions to orchestrate your ML workflow. Using SageMaker Model Monitor, you instrumented and observed your Endpoint, and at the end of the project you built a visualization to help stakeholders understand the performance of the Endpoint over time. If you're up for it, you can even go further with these stretch goals:\n",
    "\n",
    "* Extend your workflow to incorporate more classes: the CIFAR dataset includes other vehicles that Scones Unlimited can identify with this model.\n",
    "* Modify your event driven workflow: can you rewrite your Lambda functions so that the workflow can process multiple image inputs in parallel? Can the Step Function \"fan out\" to accomodate this new workflow?\n",
    "* Consider the test data generator we provided for you. Can we use it to create a \"dummy data\" generator, to simulate a continuous stream of input data? Or a big paralell load of data?\n",
    "* What if we want to get notified every time our step function errors out? Can we use the Step Functions visual editor in conjunction with a service like SNS to accomplish this? Try it out!\n",
    "\n",
    "\n"
   ]
  }
 ],
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