codeCarbon_IMDbSentimentData_carbonAnalysis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install codecarbon"
      ],
      "metadata": {
        "id": "SrXptFRAbmRA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from codecarbon import EmissionsTracker\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout, Activation\n",
        "from tensorflow.keras.layers import Embedding\n",
        "from tensorflow.keras.layers import LSTM, Bidirectional\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n",
        "from tensorflow.keras.datasets import imdb\n",
        "from tensorflow.keras.utils import pad_sequences"
      ],
      "metadata": {
        "id": "ZKjT7kRfbzZh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_features = 50000 # vocabulary size\n",
        "maxlen = 512 # The length of every input sequence\n",
        "\n",
        "embedding_size = 64  # the dimension of the embeddings\n",
        "dropout = 0.2\n",
        "filters = 64  # num of 1D convolution filters (output num channels)\n",
        "kernel_size = 5  # 1D convolution kernel width\n",
        "pool_size = 4\n",
        "lstm_output_size = 16  # number of LSTM units\n",
        "batch_size = 32\n",
        "\n",
        "epochs = 5  # number of training epochs. For Testing Purpose\n",
        "test_batch_size = 32  # batch size for testing"
      ],
      "metadata": {
        "id": "501gEKRvbzdF"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loading Data and Analysing its Carbon Impact using CodeCarbon"
      ],
      "metadata": {
        "id": "Onsf0Yk_eThw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tracker = EmissionsTracker(project_name=\"codeCarbon_IMDbSentimentData_LoadingData_Analysis\")\n",
        "tracker.start_task(\"load Dataset\")\n",
        "(x_train, y_train), (x_test, y_test) = imdb.load_data(num_words=max_features)\n",
        "print(f\"Length of Training Data: {len(x_train)}\\n\\n{x_train}\")\n",
        "print(\"\\n==============\\n\")\n",
        "print(f\"Length of Test Data: {len(x_test)}\\n\\n{x_test}\")\n",
        "tracker.stop_task()\n",
        "tracker.stop()"
      ],
      "metadata": {
        "id": "D9uJ7UzuyTu0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37e3f510-6a90-4d45-e338-dc1afe80f91b"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:16:16] [setup] RAM Tracking...\n",
            "[codecarbon INFO @ 06:16:16] [setup] GPU Tracking...\n",
            "[codecarbon INFO @ 06:16:16] Tracking Nvidia GPU via pynvml\n",
            "[codecarbon INFO @ 06:16:16] [setup] CPU Tracking...\n",
            "[codecarbon WARNING @ 06:16:16] No CPU tracking mode found. Falling back on CPU constant mode. \n",
            " Linux OS detected: Please ensure RAPL files exist at \\sys\\class\\powercap\\intel-rapl to measure CPU\n",
            "\n",
            "[codecarbon WARNING @ 06:16:17] We saw that you have a Intel(R) Xeon(R) CPU @ 2.30GHz but we don't know it. Please contact us.\n",
            "[codecarbon INFO @ 06:16:17] CPU Model on constant consumption mode: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "[codecarbon INFO @ 06:16:17] >>> Tracker's metadata:\n",
            "[codecarbon INFO @ 06:16:17]   Platform system: Linux-6.1.85+-x86_64-with-glibc2.35\n",
            "[codecarbon INFO @ 06:16:17]   Python version: 3.10.12\n",
            "[codecarbon INFO @ 06:16:17]   CodeCarbon version: 2.7.1\n",
            "[codecarbon INFO @ 06:16:17]   Available RAM : 12.675 GB\n",
            "[codecarbon INFO @ 06:16:17]   CPU count: 2\n",
            "[codecarbon INFO @ 06:16:17]   CPU model: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "[codecarbon INFO @ 06:16:17]   GPU count: 1\n",
            "[codecarbon INFO @ 06:16:17]   GPU model: 1 x Tesla T4\n",
            "[codecarbon INFO @ 06:16:17] Saving emissions data to file /content/emissions.csv\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "\u001b[1m17464789/17464789\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 0us/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:16:21] Energy consumed for RAM : 0.000005 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:16:21] Energy consumed for all GPUs : 0.000010 kWh. Total GPU Power : 9.73465280835274 W\n",
            "[codecarbon INFO @ 06:16:21] Energy consumed for all CPUs : 0.000043 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:16:21] 0.000057 kWh of electricity used since the beginning.\n",
            "[codecarbon INFO @ 06:16:21] Energy consumed for RAM : 0.000005 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:16:21] Energy consumed for all GPUs : 0.000010 kWh. Total GPU Power : 0.0 W\n",
            "[codecarbon INFO @ 06:16:21] Energy consumed for all CPUs : 0.000043 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:16:21] 0.000057 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Length of Training Data: 25000\n",
            "\n",
            "[list([1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 22665, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 21631, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 19193, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 5244, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 10311, 8, 4, 107, 117, 5952, 15, 256, 4, 31050, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 12118, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 5345, 19, 178, 32])\n",
            " list([1, 194, 1153, 194, 8255, 78, 228, 5, 6, 1463, 4369, 5012, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 8163, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 6853, 5, 163, 11, 3215, 10156, 4, 1153, 9, 194, 775, 7, 8255, 11596, 349, 2637, 148, 605, 15358, 8003, 15, 123, 125, 68, 23141, 6853, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 36893, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 8255, 5, 25249, 656, 245, 2350, 5, 4, 9837, 131, 152, 491, 18, 46151, 32, 7464, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95])\n",
            " list([1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 5974, 54, 61, 369, 13, 71, 149, 14, 22, 112, 4, 2401, 311, 12, 16, 3711, 33, 75, 43, 1829, 296, 4, 86, 320, 35, 534, 19, 263, 4821, 1301, 4, 1873, 33, 89, 78, 12, 66, 16, 4, 360, 7, 4, 58, 316, 334, 11, 4, 1716, 43, 645, 662, 8, 257, 85, 1200, 42, 1228, 2578, 83, 68, 3912, 15, 36, 165, 1539, 278, 36, 69, 44076, 780, 8, 106, 14, 6905, 1338, 18, 6, 22, 12, 215, 28, 610, 40, 6, 87, 326, 23, 2300, 21, 23, 22, 12, 272, 40, 57, 31, 11, 4, 22, 47, 6, 2307, 51, 9, 170, 23, 595, 116, 595, 1352, 13, 191, 79, 638, 89, 2, 14, 9, 8, 106, 607, 624, 35, 534, 6, 227, 7, 129, 113])\n",
            " ...\n",
            " list([1, 11, 6, 230, 245, 6401, 9, 6, 1225, 446, 2, 45, 2174, 84, 8322, 4007, 21, 4, 912, 84, 14532, 325, 725, 134, 15271, 1715, 84, 5, 36, 28, 57, 1099, 21, 8, 140, 8, 703, 5, 11656, 84, 56, 18, 1644, 14, 9, 31, 7, 4, 9406, 1209, 2295, 26094, 1008, 18, 6, 20, 207, 110, 563, 12, 8, 2901, 17793, 8, 97, 6, 20, 53, 4767, 74, 4, 460, 364, 1273, 29, 270, 11, 960, 108, 45, 40, 29, 2961, 395, 11, 6, 4065, 500, 7, 14492, 89, 364, 70, 29, 140, 4, 64, 4780, 11, 4, 2678, 26, 178, 4, 529, 443, 17793, 5, 27, 710, 117, 2, 8123, 165, 47, 84, 37, 131, 818, 14, 595, 10, 10, 61, 1242, 1209, 10, 10, 288, 2260, 1702, 34, 2901, 17793, 4, 65, 496, 4, 231, 7, 790, 5, 6, 320, 234, 2766, 234, 1119, 1574, 7, 496, 4, 139, 929, 2901, 17793, 7750, 5, 4241, 18, 4, 8497, 13164, 250, 11, 1818, 7561, 4, 4217, 5408, 747, 1115, 372, 1890, 1006, 541, 9303, 7, 4, 59, 11027, 4, 3586, 22459])\n",
            " list([1, 1446, 7079, 69, 72, 3305, 13, 610, 930, 8, 12, 582, 23, 5, 16, 484, 685, 54, 349, 11, 4120, 2959, 45, 58, 1466, 13, 197, 12, 16, 43, 23, 21469, 5, 62, 30, 145, 402, 11, 4131, 51, 575, 32, 61, 369, 71, 66, 770, 12, 1054, 75, 100, 2198, 8, 4, 105, 37, 69, 147, 712, 75, 3543, 44, 257, 390, 5, 69, 263, 514, 105, 50, 286, 1814, 23, 4, 123, 13, 161, 40, 5, 421, 4, 116, 16, 897, 13, 40691, 40, 319, 5872, 112, 6700, 11, 4803, 121, 25, 70, 3468, 4, 719, 3798, 13, 18, 31, 62, 40, 8, 7200, 4, 29455, 7, 14, 123, 5, 942, 25, 8, 721, 12, 145, 5, 202, 12, 160, 580, 202, 12, 6, 52, 58, 11418, 92, 401, 728, 12, 39, 14, 251, 8, 15, 251, 5, 21213, 12, 38, 84, 80, 124, 12, 9, 23])\n",
            " list([1, 17, 6, 194, 337, 7, 4, 204, 22, 45, 254, 8, 106, 14, 123, 4, 12815, 270, 14437, 5, 16923, 12255, 732, 2098, 101, 405, 39, 14, 1034, 4, 1310, 9, 115, 50, 305, 12, 47, 4, 168, 5, 235, 7, 38, 111, 699, 102, 7, 4, 4039, 9245, 9, 24, 6, 78, 1099, 17, 2345, 16553, 21, 27, 9685, 6139, 5, 29043, 1603, 92, 1183, 4, 1310, 7, 4, 204, 42, 97, 90, 35, 221, 109, 29, 127, 27, 118, 8, 97, 12, 157, 21, 6789, 2, 9, 6, 66, 78, 1099, 4, 631, 1191, 5, 2642, 272, 191, 1070, 6, 7585, 8, 2197, 2, 10755, 544, 5, 383, 1271, 848, 1468, 12183, 497, 16876, 8, 1597, 8778, 19280, 21, 60, 27, 239, 9, 43, 8368, 209, 405, 10, 10, 12, 764, 40, 4, 248, 20, 12, 16, 5, 174, 1791, 72, 7, 51, 6, 1739, 22, 4, 204, 131, 9])]\n",
            "\n",
            "==============\n",
            "\n",
            "Length of Test Data: 25000\n",
            "\n",
            "[list([1, 591, 202, 14, 31, 6, 717, 10, 10, 18142, 10698, 5, 4, 360, 7, 4, 177, 5760, 394, 354, 4, 123, 9, 1035, 1035, 1035, 10, 10, 13, 92, 124, 89, 488, 7944, 100, 28, 1668, 14, 31, 23, 27, 7479, 29, 220, 468, 8, 124, 14, 286, 170, 8, 157, 46, 5, 27, 239, 16, 179, 15387, 38, 32, 25, 7944, 451, 202, 14, 6, 717])\n",
            " list([1, 14, 22, 3443, 6, 176, 7, 5063, 88, 12, 2679, 23, 1310, 5, 109, 943, 4, 114, 9, 55, 606, 5, 111, 7, 4, 139, 193, 273, 23, 4, 172, 270, 11, 7216, 10626, 4, 8463, 2801, 109, 1603, 21, 4, 22, 3861, 8, 6, 1193, 1330, 10, 10, 4, 105, 987, 35, 841, 16873, 19, 861, 1074, 5, 1987, 17975, 45, 55, 221, 15, 670, 5304, 526, 14, 1069, 4, 405, 5, 2438, 7, 27, 85, 108, 131, 4, 5045, 5304, 3884, 405, 9, 3523, 133, 5, 50, 13, 104, 51, 66, 166, 14, 22, 157, 9, 4, 530, 239, 34, 8463, 2801, 45, 407, 31, 7, 41, 3778, 105, 21, 59, 299, 12, 38, 950, 5, 4521, 15, 45, 629, 488, 2733, 127, 6, 52, 292, 17, 4, 6936, 185, 132, 1988, 5304, 1799, 488, 2693, 47, 6, 392, 173, 4, 21686, 4378, 270, 2352, 4, 1500, 7, 4, 65, 55, 73, 11, 346, 14, 20, 9, 6, 976, 2078, 7, 5293, 861, 12746, 5, 4182, 30, 3127, 23651, 56, 4, 841, 5, 990, 692, 8, 4, 1669, 398, 229, 10, 10, 13, 2822, 670, 5304, 14, 9, 31, 7, 27, 111, 108, 15, 2033, 19, 7836, 1429, 875, 551, 14, 22, 9, 1193, 21, 45, 4829, 5, 45, 252, 8, 12508, 6, 565, 921, 3639, 39, 4, 529, 48, 25, 181, 8, 67, 35, 1732, 22, 49, 238, 60, 135, 1162, 14, 9, 290, 4, 58, 10, 10, 472, 45, 55, 878, 8, 169, 11, 374, 5687, 25, 203, 28, 8, 818, 12, 125, 4, 3077])\n",
            " list([1, 111, 748, 4368, 1133, 33782, 24563, 4, 87, 1551, 1262, 7, 31, 318, 9459, 7, 4, 498, 5076, 748, 63, 29, 5161, 220, 686, 10941, 5, 17, 12, 575, 220, 2507, 17, 6, 185, 132, 24563, 16, 53, 928, 11, 2, 74, 4, 438, 21, 27, 10044, 589, 8, 22, 107, 20123, 19550, 997, 1638, 8, 35, 2076, 9019, 11, 22, 231, 54, 29, 1706, 29, 100, 18995, 2425, 34, 12998, 8738, 48078, 5, 19353, 98, 31, 2122, 33, 6, 58, 14, 3808, 1638, 8, 4, 365, 7, 2789, 3761, 356, 346, 4, 27608, 1060, 63, 29, 93, 11, 5421, 11, 15236, 33, 6, 58, 54, 1270, 431, 748, 7, 32, 2580, 16, 11, 94, 19469, 10, 10, 4, 993, 45222, 7, 4, 1766, 2634, 2164, 24563, 8, 847, 8, 1450, 121, 31, 7, 27, 86, 2663, 10760, 16, 6, 465, 993, 2006, 30995, 573, 17, 2, 42, 4, 17345, 37, 473, 6, 711, 6, 8869, 7, 328, 212, 70, 30, 258, 11, 220, 32, 7, 108, 21, 133, 12, 9, 55, 465, 849, 3711, 53, 33, 2071, 1969, 37, 70, 1144, 4, 5940, 1409, 74, 476, 37, 62, 91, 1329, 169, 4, 1330, 10104, 146, 655, 2212, 5, 258, 12, 184, 10104, 546, 5, 849, 10333, 7, 4, 22, 1436, 18, 631, 1386, 797, 7, 4, 8712, 71, 348, 425, 4320, 1061, 19, 10288, 5, 12141, 11, 661, 8, 339, 17863, 4, 2455, 11434, 7, 4, 1962, 10, 10, 263, 787, 9, 270, 11, 6, 9466, 4, 2, 48414, 121, 4, 5437, 26, 4434, 19, 68, 1372, 5, 28, 446, 6, 318, 7149, 8, 67, 51, 36, 70, 81, 8, 4392, 2294, 36, 1197, 8, 2, 25399, 18, 6, 711, 4, 9909, 26, 10296, 1125, 11, 14, 636, 720, 12, 426, 28, 77, 776, 8, 97, 38, 111, 7489, 6175, 168, 1239, 5189, 137, 25399, 18, 27, 173, 9, 2399, 17, 6, 12397, 428, 14657, 232, 11, 4, 8014, 37, 272, 40, 2708, 247, 30, 656, 6, 13182, 54, 25399, 3292, 98, 6, 2840, 40, 558, 37, 6093, 98, 4, 17345, 1197, 15, 14, 9, 57, 4893, 5, 4659, 6, 275, 711, 7937, 25399, 3292, 98, 6, 31036, 10, 10, 6639, 19, 14, 10241, 267, 162, 711, 37, 5900, 752, 98, 4, 17345, 2378, 90, 19, 6, 2, 7, 36744, 1810, 2, 4, 4770, 3183, 930, 8, 508, 90, 4, 1317, 8, 4, 48414, 17, 15454, 3965, 1853, 4, 1494, 8, 4468, 189, 4, 31036, 6287, 5774, 4, 4770, 5, 95, 271, 23, 6, 7742, 6063, 21627, 5437, 33, 1526, 6, 425, 3155, 33697, 4535, 1636, 7, 4, 4669, 11966, 469, 4, 4552, 54, 4, 150, 5664, 17345, 280, 53, 2, 25399, 18, 339, 29, 1978, 27, 7885, 5, 17303, 68, 1830, 19, 6571, 14605, 4, 1515, 7, 263, 65, 2132, 34, 6, 5680, 7489, 43, 159, 29, 9, 4706, 9, 387, 73, 195, 584, 10, 10, 1069, 4, 58, 810, 54, 14, 6078, 117, 22, 16, 93, 5, 1069, 4, 192, 15, 12, 16, 93, 34, 6, 1766, 28228, 33, 4, 5673, 7, 15, 18760, 9252, 3286, 325, 12, 62, 30, 776, 8, 67, 14, 17, 6, 12214, 44, 148, 687, 24563, 203, 42, 203, 24, 28, 69, 32157, 6676, 11, 330, 54, 29, 93, 2, 21, 845, 14148, 27, 1099, 7, 819, 4, 22, 1407, 17, 6, 14967, 787, 7, 2460, 19569, 2, 100, 30, 4, 3737, 3617, 3169, 2321, 42, 1898, 11, 4, 3814, 42, 101, 704, 7, 101, 999, 15, 1625, 94, 2926, 180, 5, 9, 9101, 34, 15205, 45, 6, 1429, 22, 60, 6, 1220, 31, 11, 94, 6408, 96, 21, 94, 749, 9, 57, 975])\n",
            " ...\n",
            " list([1, 13, 1408, 15, 8, 135, 14, 9, 35, 32, 46, 394, 20, 62, 30, 5093, 21, 45, 184, 78, 4, 1492, 910, 769, 2290, 2515, 395, 4257, 5, 1454, 11, 119, 16946, 89, 1036, 4, 116, 218, 78, 21, 407, 100, 30, 128, 262, 15, 7, 185, 2280, 284, 1842, 2, 37, 315, 4, 226, 20, 272, 2942, 40, 29, 152, 60, 181, 8, 30, 50, 553, 362, 80, 119, 12, 21, 846, 5518])\n",
            " list([1, 11, 119, 241, 9, 4, 840, 20, 12, 468, 15, 94, 3684, 562, 791, 39, 4, 86, 107, 8, 97, 14, 31, 33, 4, 2960, 7, 743, 46, 1028, 9, 3531, 5, 4, 768, 47, 8, 79, 90, 145, 164, 162, 50, 6, 501, 119, 7, 9, 4, 78, 232, 15, 16, 224, 11, 4, 333, 20, 4, 985, 200, 5, 28739, 5, 9, 1861, 8, 79, 357, 4, 20, 47, 220, 57, 206, 139, 11, 12, 5, 55, 117, 212, 13, 1276, 92, 124, 51, 45, 1188, 71, 536, 13, 520, 14, 20, 6, 2302, 7, 470])\n",
            " list([1, 6, 52, 7465, 430, 22, 9, 220, 2594, 8, 28, 24357, 519, 3227, 6, 769, 15, 47, 6, 3482, 4067, 8, 114, 5, 33, 222, 31, 55, 184, 704, 5586, 18020, 19, 346, 3153, 5, 6, 364, 350, 4, 184, 5586, 9, 133, 1810, 11, 5417, 13226, 21, 4, 7298, 42657, 570, 50, 2005, 2643, 9, 6, 1249, 17, 6, 25194, 27803, 21, 17, 6, 1211, 232, 1138, 2249, 29, 266, 56, 96, 346, 194, 308, 9, 194, 21, 29, 218, 1078, 19, 4, 78, 173, 7, 27, 20067, 5698, 3406, 718, 21264, 9, 6, 6907, 17, 210, 5, 3281, 5677, 47, 77, 395, 14, 172, 173, 18, 2740, 2931, 4517, 82, 127, 27, 173, 11, 6, 392, 217, 21, 50, 9, 57, 65, 12, 14274, 53, 40, 35, 390, 7, 11, 4, 3567, 7, 4, 314, 74, 6, 792, 22, 16261, 19, 714, 727, 5205, 382, 4, 91, 6533, 439, 19, 14, 20, 9, 1441, 5805, 1118, 4, 756, 25, 124, 4, 31, 12, 16, 93, 804, 34, 2005, 2643])]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/codecarbon/output_methods/file.py:52: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  df = pd.concat([df, pd.DataFrame.from_records([dict(total.values)])])\n",
            "/usr/local/lib/python3.10/dist-packages/codecarbon/output_methods/file.py:81: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
            "  df = pd.concat(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.637622597406154e-05"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Pad sequences (samples x time)')\n",
        "x_train = pad_sequences(x_train, maxlen=maxlen)\n",
        "x_test = pad_sequences(x_test, maxlen=maxlen)\n",
        "print('x_train shape:', x_train.shape)\n",
        "print('x_test shape:', x_test.shape)"
      ],
      "metadata": {
        "id": "xffwiKOHzAWk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9d6db02-cef7-4d8c-b253-973684af5393"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Pad sequences (samples x time)\n",
            "x_train shape: (25000, 512)\n",
            "x_test shape: (25000, 512)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Building Model"
      ],
      "metadata": {
        "id": "3MZDIIw0fFCB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def make_model(metrics: list, vocab_size: int, embedding_dim: int, dropout: float, filters: int, kernel_size: int, pool_size: int, lstm_output_size: int,) -> Sequential:\n",
        "\n",
        "    model = Sequential([\n",
        "        Embedding(vocab_size, embedding_dim, input_length=maxlen),\n",
        "        Dropout(dropout),\n",
        "        Conv1D(filters, kernel_size, padding='valid', activation='relu'),\n",
        "        MaxPooling1D(pool_size=pool_size),\n",
        "        Bidirectional(LSTM(lstm_output_size), merge_mode='ave'),\n",
        "        Dense(1),\n",
        "        Activation('sigmoid'),\n",
        "    ])\n",
        "\n",
        "    model.compile(\n",
        "        optimizer='adam',\n",
        "        loss='binary_crossentropy',\n",
        "        metrics=metrics\n",
        "    )\n",
        "\n",
        "    return model"
      ],
      "metadata": {
        "id": "rLdpxvqde8W8"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_model():\n",
        "    model = make_model(\n",
        "        metrics=[\"accuracy\"],\n",
        "        vocab_size=max_features,\n",
        "        embedding_dim=embedding_size,\n",
        "        dropout=dropout,\n",
        "        filters=filters,\n",
        "        kernel_size=kernel_size,\n",
        "        pool_size=pool_size,\n",
        "        lstm_output_size=lstm_output_size,\n",
        "    )\n",
        "\n",
        "    h = model.fit(\n",
        "        x_train,\n",
        "        y_train,\n",
        "        validation_data=(x_test, y_test),\n",
        "        batch_size=batch_size,\n",
        "        epochs=epochs,\n",
        "    )"
      ],
      "metadata": {
        "id": "0nSr03jSf-uE"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using CodeCarbon to Track the emissions"
      ],
      "metadata": {
        "id": "iHE_1Vi7hd3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tracker = EmissionsTracker(project_name=\"codeCarbon_IMDbSentimentData_carbonAnalysis\")\n",
        "tracker.start()\n",
        "train_model()\n",
        "tracker.stop()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLc6xHn8gBpL",
        "outputId": "1af71da5-e7a0-4bd5-8b5e-594fab09484d"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:21:39] [setup] RAM Tracking...\n",
            "[codecarbon INFO @ 06:21:39] [setup] GPU Tracking...\n",
            "[codecarbon INFO @ 06:21:39] Tracking Nvidia GPU via pynvml\n",
            "[codecarbon INFO @ 06:21:39] [setup] CPU Tracking...\n",
            "[codecarbon WARNING @ 06:21:39] No CPU tracking mode found. Falling back on CPU constant mode. \n",
            " Linux OS detected: Please ensure RAPL files exist at \\sys\\class\\powercap\\intel-rapl to measure CPU\n",
            "\n",
            "[codecarbon WARNING @ 06:21:40] We saw that you have a Intel(R) Xeon(R) CPU @ 2.30GHz but we don't know it. Please contact us.\n",
            "[codecarbon INFO @ 06:21:40] CPU Model on constant consumption mode: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "[codecarbon INFO @ 06:21:40] >>> Tracker's metadata:\n",
            "[codecarbon INFO @ 06:21:40]   Platform system: Linux-6.1.85+-x86_64-with-glibc2.35\n",
            "[codecarbon INFO @ 06:21:40]   Python version: 3.10.12\n",
            "[codecarbon INFO @ 06:21:40]   CodeCarbon version: 2.7.1\n",
            "[codecarbon INFO @ 06:21:40]   Available RAM : 12.675 GB\n",
            "[codecarbon INFO @ 06:21:40]   CPU count: 2\n",
            "[codecarbon INFO @ 06:21:40]   CPU model: Intel(R) Xeon(R) CPU @ 2.30GHz\n",
            "[codecarbon INFO @ 06:21:40]   GPU count: 1\n",
            "[codecarbon INFO @ 06:21:40]   GPU model: 1 x Tesla T4\n",
            "[codecarbon INFO @ 06:21:40] Saving emissions data to file /content/emissions.csv\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/5\n",
            "\u001b[1m322/782\u001b[0m \u001b[32m━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━\u001b[0m \u001b[1m6s\u001b[0m 15ms/step - accuracy: 0.6469 - loss: 0.6045"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:21:56] Energy consumed for RAM : 0.000020 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:21:56] Energy consumed for all GPUs : 0.000130 kWh. Total GPU Power : 31.131600798605167 W\n",
            "[codecarbon INFO @ 06:21:56] Energy consumed for all CPUs : 0.000177 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:21:56] 0.000327 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m26s\u001b[0m 22ms/step - accuracy: 0.7353 - loss: 0.5017 - val_accuracy: 0.8740 - val_loss: 0.3044\n",
            "Epoch 2/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:22:11] Energy consumed for RAM : 0.000040 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:22:11] Energy consumed for all GPUs : 0.000302 kWh. Total GPU Power : 41.25638406296859 W\n",
            "[codecarbon INFO @ 06:22:11] Energy consumed for all CPUs : 0.000354 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:22:11] 0.000696 kWh of electricity used since the beginning.\n",
            "[codecarbon INFO @ 06:22:26] Energy consumed for RAM : 0.000059 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:22:26] Energy consumed for all GPUs : 0.000417 kWh. Total GPU Power : 27.558094405202315 W\n",
            "[codecarbon INFO @ 06:22:26] Energy consumed for all CPUs : 0.000532 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:22:26] 0.001007 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m780/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9416 - loss: 0.1694"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:22:41] Energy consumed for RAM : 0.000079 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:22:41] Energy consumed for all GPUs : 0.000605 kWh. Total GPU Power : 45.29605791060786 W\n",
            "[codecarbon INFO @ 06:22:41] Energy consumed for all CPUs : 0.000709 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:22:41] 0.001393 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 20ms/step - accuracy: 0.9416 - loss: 0.1694 - val_accuracy: 0.8680 - val_loss: 0.3301\n",
            "Epoch 3/5\n",
            "\u001b[1m635/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━\u001b[0m \u001b[1m2s\u001b[0m 14ms/step - accuracy: 0.9786 - loss: 0.0765"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:22:56] Energy consumed for RAM : 0.000099 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:22:56] Energy consumed for all GPUs : 0.000779 kWh. Total GPU Power : 41.817918538787985 W\n",
            "[codecarbon INFO @ 06:22:56] Energy consumed for all CPUs : 0.000886 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:22:56] 0.001764 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 21ms/step - accuracy: 0.9775 - loss: 0.0784 - val_accuracy: 0.8719 - val_loss: 0.3782\n",
            "Epoch 4/5\n",
            "\u001b[1m229/782\u001b[0m \u001b[32m━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━\u001b[0m \u001b[1m9s\u001b[0m 16ms/step - accuracy: 0.9899 - loss: 0.0361"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:23:11] Energy consumed for RAM : 0.000119 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:23:11] Energy consumed for all GPUs : 0.000950 kWh. Total GPU Power : 41.02747849480924 W\n",
            "[codecarbon INFO @ 06:23:11] Energy consumed for all CPUs : 0.001063 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:23:11] 0.002132 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 21ms/step - accuracy: 0.9880 - loss: 0.0433 - val_accuracy: 0.8681 - val_loss: 0.4464\n",
            "Epoch 5/5\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:23:26] Energy consumed for RAM : 0.000139 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:23:26] Energy consumed for all GPUs : 0.001134 kWh. Total GPU Power : 44.225801088350885 W\n",
            "[codecarbon INFO @ 06:23:26] Energy consumed for all CPUs : 0.001240 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:23:26] 0.002513 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m779/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━\u001b[0m \u001b[1m0s\u001b[0m 15ms/step - accuracy: 0.9924 - loss: 0.0289"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:23:41] Energy consumed for RAM : 0.000158 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:23:41] Energy consumed for all GPUs : 0.001325 kWh. Total GPU Power : 45.76527601808267 W\n",
            "[codecarbon INFO @ 06:23:41] Energy consumed for all CPUs : 0.001417 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:23:41] 0.002900 kWh of electricity used since the beginning.\n",
            "[codecarbon INFO @ 06:23:41] 0.006898 g.CO2eq/s mean an estimation of 217.52165983557416 kg.CO2eq/year\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\r\u001b[1m782/782\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m20s\u001b[0m 20ms/step - accuracy: 0.9924 - loss: 0.0289 - val_accuracy: 0.8661 - val_loss: 0.5075\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[codecarbon INFO @ 06:23:43] Energy consumed for RAM : 0.000162 kWh. RAM Power : 4.7530388832092285 W\n",
            "[codecarbon INFO @ 06:23:43] Energy consumed for all GPUs : 0.001356 kWh. Total GPU Power : 44.196883321020465 W\n",
            "[codecarbon INFO @ 06:23:43] Energy consumed for all CPUs : 0.001447 kWh. Total CPU Power : 42.5 W\n",
            "[codecarbon INFO @ 06:23:43] 0.002965 kWh of electricity used since the beginning.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0008465368770214266"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "0o0B10cAjJRq"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}