Module_21_Lab_acc_workloads_rapids.ipynb

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        "<a href=\"https://colab.research.google.com/github/akshatva7/CADD/blob/main/Module_21_Lab_acc_workloads_rapids.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "## Download Data\n",
        "\n",
        "In order to complete this lab, please download data from [here](https://poloclub.github.io/data-science-teaching-kit/). Create a folder sample_data inside your current directory and upload all the data files (data0.csv, etc.) to this folder. Now you are all set up for completing this lab!"
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      "source": [
        "### Part 1. KMeans with CPU"
      ]
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    {
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      "source": [
        "#### Q1. Read the five provided datasets\n",
        "\n",
        "Read the five provided datasets (data{0, 1, 2, 3, 4}.csv) using `pandas.read_csv()` and save the data to `pandas.DataFrame` variables. Additionally, measure the time taken to read each dataset for later use.  You can use `time()` function to measure running time."
      ]
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    {
      "cell_type": "code",
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      "source": [
        "import pandas as pd\n",
        "from time import time\n",
        "\n",
        "# TODO: Read the five datasets using pd.read_csv()\n",
        "\n",
        "# append the dataframes of each of the five datasets to dfs, running times of each dataset to pd_running_times\n",
        "\n",
        "dfs = []\n",
        "pd_running_times = []"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "#### Q2. Perform KMeans implemented in sklearn\n",
        "\n",
        "Run KMeans clustering with `sklearn.cluster.KMeans`. Measure the running time foor KMeans on each dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "from sklearn.cluster import KMeans as sklearnKMeans\n",
        "from time import time\n",
        "\n",
        "# TDOO: Run KMeans using sklearnKMeans on each dataset with n_clusters = 10\n",
        "\n",
        "# measure the running time on each dataset and append to sklearn_running_times\n",
        "sklearn_running_times = []"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G_qSmvAaSjwE"
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      "source": [
        "### Part 2. KMeans with GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8ftUDOKTVC8"
      },
      "source": [
        "#### Q3. Read datasets with cuDF\n",
        "\n",
        "Read the five datasets with `cudf.read_csv()` function in `cuDF` library and save the data into `cudf.DataFrame` variables. Additionally, measure the time taken to read each dataset for later use.\n",
        "\n"
      ]
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      "source": [
        "import cudf\n",
        "\n",
        "# TODO: Read the five datasets using cudf.read_csv()\n",
        "\n",
        "# append the dataframes of each of the five datasets to gdfs, running times of each dataset to cudf_running_times\n",
        "\n",
        "gdfs = []\n",
        "cudf_running_times = []"
      ],
      "execution_count": 3,
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            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-dc247add9eb8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mcudf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# TODO: Read the five datasets using cudf.read_csv()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# append the dataframes of each of the five datasets to gdfs, running times of each dataset to cudf_running_times\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'cudf'",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
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      "source": [
        "#### Q4. Perform KMeans implemented in cuML\n",
        "Perform KMeans clustering implemented in cuML. Measure the running time for KMeans on each dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "evSjCKMnLpOb"
      },
      "source": [
        "from cuml.cluster import KMeans as cumlKMeans\n",
        "from time import time\n",
        "\n",
        "# TDOO: Run KMeans using cumlKMeans on each dataset with n_clusters = 10\n",
        "\n",
        "# measure the running time on each dataset and append to cuml_running_times\n",
        "\n",
        "cuml_running_times = []"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aXUNhqtPqqVK"
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      "source": [
        "### Part 3. Plot the values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jyXfWQTS8pF0"
      },
      "source": [
        "Now that we have performed K-means with both sklearn and cuML, we can clearly see that there is a huge difference in the time taken in both cases. Let us plot these values to see if we can find a trend!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IDfUwZIR1yK-"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "X = [25000 , 63115, 200000, 300000, 1419554]\n",
        "#X is number of lines of data in each data{0, 1, 2, 3, 4}.csv file\n",
        "\n",
        "y = pd_running_times\n",
        "z = cudf_running_times\n",
        "\n",
        "#TODO: Plot a simultaneous graph with values of running times to read data of both pandas and cudf, i.e., pd_running_times and cudf_running_times\n",
        "\n",
        "#X axis is the number of lines and Y axis has the time in milliseconds. Provide a legend to differentiate pd_running_times and cudf_running_times"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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      "source": [
        "y = sklearn_running_times\n",
        "z = cuml_running_times\n",
        "\n",
        "#TODO: Plot a simultaneous graph with values of running times to perform k-means using both sklearn and cuml, i.e., sklearn_running_times and cuml_running_times\n",
        "\n",
        "#X axis is the number of lines and Y axis has the time in milliseconds. Provide a legend to differentiate sklearn_running_times and cuml_running_times"
      ],
      "execution_count": null,
      "outputs": []
    }
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