{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "821be114-acbc-4d87-8ef8-03ca6cb2dbeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from time import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "a17d7aa5-505e-4306-ae47-f39830b3406b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = tf.data.Dataset.range(10000)\n",
    "dataset = dataset.map(lambda x: sum([x**i for i in range(1000)]))\n",
    "dataset = dataset.cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "b77c9415-f5f3-49fc-a81e-169119e86413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.57"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first time reading through the data will generate the data using\n",
    "# `range` and `map`.\n",
    "tik = time()\n",
    "list(dataset.as_numpy_iterator())\n",
    "tok = time()\n",
    "round(tok-tik,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "94724841-417a-4490-ae0e-784e18d35c35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Subsequent iterations read from the cache.\n",
    "tik = time()\n",
    "list(dataset.as_numpy_iterator())\n",
    "tok = time()\n",
    "round(tok-tik,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7929ee4f-75be-4dce-a984-00dff458e180",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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