Classifier: Benchmarking_XGBoost_on_GPU.ipynb

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      "name": "Benchmarking_XGBoost_on_GPU.ipynb",
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      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/gulabpatel/ML_Algos_Python/blob/main/Benchmarking_XGBoost_on_GPU.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CdoXqKYNBgDN"
      },
      "source": [
        "**Link to supporting video for below note book walkhthrough** - [https://www.youtube.com/watch?v=X7cKC6GgyxY](https://www.youtube.com/watch?v=X7cKC6GgyxY)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xWH8jZB_OeEl",
        "outputId": "0a17a254-9f5b-49be-efe4-a2bfcab16e25"
      },
      "source": [
        "!pip install xgboost"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.6/dist-packages (0.90)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.4.1)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from xgboost) (1.18.5)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kbHRfwu_CTJP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "30684728-e061-4be2-a923-44328e46a140"
      },
      "source": [
        "import xgboost as xgb\n",
        "xgb.__version__"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'0.90'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wYsohgWlDB8X"
      },
      "source": [
        "from __future__ import print_function\n",
        "import sys,tempfile, urllib, os\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "import numpy as np"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZtuVC35u3qTL"
      },
      "source": [
        "from sklearn.datasets import fetch_openml\n",
        "covtyp = fetch_openml(name='covertype', version=4)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3OR4SZNm65qf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd843b5e-7788-4081-85ca-04b0049a73ab"
      },
      "source": [
        "covtyp.data.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(581012, 54)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QhW9jLy_7mHv",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "63bb9f03-b14f-485c-f096-d8889b2d5a5a"
      },
      "source": [
        "np.unique(covtyp.target)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['1', '2', '3', '4', '5', '6', '7'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eSauT6En37r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93d69383-7ec9-4c68-b807-cdf598bf773a"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Nov 23 18:01:09 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   70C    P8    11W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kdUEoVk8uQ6"
      },
      "source": [
        "cov_df = pd.DataFrame(data= np.c_[covtyp['data'], covtyp['target']],\n",
        "                     columns= covtyp['feature_names'] + ['target'])"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TB9_2CqOsPP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "321c8222-6ada-4ea1-c07b-6d6f799611a3"
      },
      "source": [
        "cov_df.memory_usage(index=True).sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "255645408"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cwaOT3fCDVR8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "outputId": "90a11709-63e6-4591-e26e-7152aa567ab3"
      },
      "source": [
        "cov_df.head()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Elevation Aspect Slope  ... Soil_Type39 Soil_Type40 target\n",
              "0      2596     51     3  ...           0           0      5\n",
              "1      2590     56     2  ...           0           0      5\n",
              "2      2804    139     9  ...           0           0      2\n",
              "3      2785    155    18  ...           0           0      2\n",
              "4      2595     45     2  ...           0           0      5\n",
              "\n",
              "[5 rows x 55 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOgRjV_ULCtl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acaac411-4608-4226-ce8a-70c78bdbf844"
      },
      "source": [
        "print (\"Rows     : \" ,cov_df.shape[0])\n",
        "print (\"Columns  : \" ,cov_df.shape[1])"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows     :  581012\n",
            "Columns  :  55\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCjmIZE4K8iW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20dde295-da1e-4e0c-8940-954ab31a37ef"
      },
      "source": [
        "cov_df.target.value_counts()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    283301\n",
              "1    211840\n",
              "3     35754\n",
              "7     20510\n",
              "6     17367\n",
              "5      9493\n",
              "4      2747\n",
              "Name: target, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9G_kjG8QSd4Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60a74fcf-950f-4814-ca23-b030fc7e11e8"
      },
      "source": [
        "cov_df.dtypes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Elevation                             object\n",
              "Aspect                                object\n",
              "Slope                                 object\n",
              "Horizontal_Distance_To_Hydrology      object\n",
              "Vertical_Distance_To_Hydrology        object\n",
              "Horizontal_Distance_To_Roadways       object\n",
              "Hillshade_9am                         object\n",
              "Hillshade_Noon                        object\n",
              "Hillshade_3pm                         object\n",
              "Horizontal_Distance_To_Fire_Points    object\n",
              "Wilderness_Area1                      object\n",
              "Wilderness_Area2                      object\n",
              "Wilderness_Area3                      object\n",
              "Wilderness_Area4                      object\n",
              "Soil_Type1                            object\n",
              "Soil_Type2                            object\n",
              "Soil_Type3                            object\n",
              "Soil_Type4                            object\n",
              "Soil_Type5                            object\n",
              "Soil_Type6                            object\n",
              "Soil_Type7                            object\n",
              "Soil_Type8                            object\n",
              "Soil_Type9                            object\n",
              "Soil_Type10                           object\n",
              "Soil_Type11                           object\n",
              "Soil_Type12                           object\n",
              "Soil_Type13                           object\n",
              "Soil_Type14                           object\n",
              "Soil_Type15                           object\n",
              "Soil_Type16                           object\n",
              "Soil_Type17                           object\n",
              "Soil_Type18                           object\n",
              "Soil_Type19                           object\n",
              "Soil_Type20                           object\n",
              "Soil_Type21                           object\n",
              "Soil_Type22                           object\n",
              "Soil_Type23                           object\n",
              "Soil_Type24                           object\n",
              "Soil_Type25                           object\n",
              "Soil_Type26                           object\n",
              "Soil_Type27                           object\n",
              "Soil_Type28                           object\n",
              "Soil_Type29                           object\n",
              "Soil_Type30                           object\n",
              "Soil_Type31                           object\n",
              "Soil_Type32                           object\n",
              "Soil_Type33                           object\n",
              "Soil_Type34                           object\n",
              "Soil_Type35                           object\n",
              "Soil_Type36                           object\n",
              "Soil_Type37                           object\n",
              "Soil_Type38                           object\n",
              "Soil_Type39                           object\n",
              "Soil_Type40                           object\n",
              "target                                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjdZdC0_aY4r"
      },
      "source": [
        "for cols in cov_df.columns:\n",
        "  cov_df[cols] = pd.to_numeric(cov_df[cols])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9bAi7NZYBBiP"
      },
      "source": [
        "#class is from 1 to 7, make it 0 to 6\n",
        "cov_df['target'] = cov_df['target']-1"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUYeVzKa9q9V"
      },
      "source": [
        "cov_df_X = cov_df.copy()\n",
        "cov_df_y =  cov_df_X.pop('target')"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZsKHF1ejlkKF"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(cov_df_X, cov_df_y, train_size=0.85, test_size=0.15)"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ClWiGUWzOVEj"
      },
      "source": [
        "dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "dtest = xgb.DMatrix(X_test, label=y_test)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qq1o-HfycZUI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f5d72453-8a0c-450e-8a44-289983033d01"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Nov 23 18:01:21 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   69C    P8    11W /  70W |      0MiB / 15079MiB |      0%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vrlpkFM3dbyX"
      },
      "source": [
        "import time\n",
        "#setting tree and tree depth\n",
        "num_round = 50\n",
        "maxdepth = 8\n",
        "param = {\n",
        "  'colsample_bylevel': 1,\n",
        "  'colsample_bytree': 1,\n",
        "  'gamma': 0,\n",
        "  'learning_rate': 0.1, \n",
        "  'random_state': 1010,\n",
        "  'objective': 'multi:softmax', \n",
        "  'num_class': 7, \n",
        "}"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DmseAQp_ewTB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "458af17f-753e-4355-8882-efd93f13024a"
      },
      "source": [
        "param['tree_method'] = 'hist'\n",
        "param['grow_policy'] = 'depthwise'\n",
        "param['max_depth'] = maxdepth\n",
        "param['max_leaves'] = 0\n",
        "param['verbosity'] = 0\n",
        "cpu_result = {} \n",
        "start_time = time.time()\n",
        "# Training with the above parameters\n",
        "xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=cpu_result, verbose_eval=20)\n",
        "\n",
        "print(\"CPU Training Time: %s seconds\" % (str(time.time() - start_time)))"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\ttest-merror:0.219525\n",
            "[20]\ttest-merror:0.183622\n",
            "[40]\ttest-merror:0.170621\n",
            "[49]\ttest-merror:0.165079\n",
            "CPU Training Time: 72.32366943359375 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfUk-gQrhSnc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "67568466-5f75-4533-8adb-5e8792fd1dc7"
      },
      "source": [
        "param['tree_method'] = 'gpu_hist'\n",
        "param['grow_policy'] = 'depthwise'\n",
        "param['max_depth'] = maxdepth\n",
        "param['max_leaves'] = 0\n",
        "param['verbosity'] = 0\n",
        "param['gpu_id'] = 0\n",
        "param['updater'] = 'grow_gpu_hist'\n",
        "param['predictor'] = 'gpu_predictor'\n",
        "\n",
        "gpu_result = {} \n",
        "start_time = time.time()\n",
        "# Training with the above parameters\n",
        "xgb.train(param, dtrain, num_round, evals=[(dtest, 'test')], evals_result=gpu_result, verbose_eval=20)\n",
        "\n",
        "print(\"GPU Training Time: %s seconds\" % (str(time.time() - start_time)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\ttest-merror:0.219892\n",
            "[20]\ttest-merror:0.183438\n",
            "[40]\ttest-merror:0.171941\n",
            "[49]\ttest-merror:0.164288\n",
            "GPU Training Time: 10.640031099319458 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vsFAwRcQhzy-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6791af50-9e06-48ef-ecff-4328f1b41190"
      },
      "source": [
        "!nvidia-smi"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mon Nov 23 18:02:44 2020       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 455.38       Driver Version: 418.67       CUDA Version: 10.1     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  Tesla T4            Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   80C    P0    57W /  70W |    385MiB / 15079MiB |     82%      Default |\n",
            "|                               |                      |                 ERR! |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}