Getting_Introduced_to_Hummingbird_ml.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Getting_Introduced_to_Hummingbird-ml",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rHZBj2em0XqG",
        "outputId": "430f7720-8273-48b1-b67d-455a3e630921"
      },
      "source": [
        "!pip install hummingbird-ml"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: hummingbird-ml in /usr/local/lib/python3.7/dist-packages (0.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.21.3 in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (1.0.1)\n",
            "Requirement already satisfied: dill in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (0.3.4)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (5.4.8)\n",
            "Requirement already satisfied: torch>=1.4 in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (1.10.0+cu111)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (1.4.1)\n",
            "Requirement already satisfied: onnxconverter-common>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (1.8.1)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from hummingbird-ml) (1.19.5)\n",
            "Requirement already satisfied: onnx in /usr/local/lib/python3.7/dist-packages (from onnxconverter-common>=1.6.0->hummingbird-ml) (1.10.2)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from onnxconverter-common>=1.6.0->hummingbird-ml) (3.17.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->hummingbird-ml) (1.1.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.3->hummingbird-ml) (3.0.0)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.4->hummingbird-ml) (3.10.0.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from onnx->onnxconverter-common>=1.6.0->hummingbird-ml) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i2LIFHW10n7z",
        "outputId": "82b254e0-605f-4f79-cb71-70774040f4e6"
      },
      "source": [
        "import numpy as np\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from hummingbird.ml import convert"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/experimental/enable_hist_gradient_boosting.py:17: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
            "  \"Since version 1.0, \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VWYXbjSP1q5R"
      },
      "source": [
        "# Create some random data for binary classification\n",
        "num_classes = 2\n",
        "X = np.random.rand(100000, 28)\n",
        "y = np.random.randint(num_classes, size=100000)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_50dh2O2OCJ",
        "outputId": "e777d3d7-69c6-49f3-d2ce-5bad309ae5ac"
      },
      "source": [
        "skl_model = RandomForestClassifier(n_estimators=10, max_depth=10)\n",
        "skl_model.fit(X, y)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(max_depth=10, n_estimators=10)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQCb_0vB2R6A",
        "outputId": "4000774d-98c0-4065-b0ad-34756b771b2d"
      },
      "source": [
        "# Use Hummingbird to convert the model to PyTorch\n",
        "model = convert(skl_model, 'pytorch')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:103: FutureWarning: Attribute `n_features_` was deprecated in version 1.0 and will be removed in 1.2. Use `n_features_in_` instead.\n",
            "  warnings.warn(msg, category=FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tmpho8ta2V4p",
        "outputId": "c37dfc0f-ef64-4132-dab7-0861a3f44a5f"
      },
      "source": [
        "%%timeit -r 3\n",
        "\n",
        "model.predict(X) # Run predictions on CPU"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10 loops, best of 3: 178 ms per loop\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q6c3L5IL2dZW",
        "outputId": "e483a4ae-3177-4572-9a94-2d4895c16b2c"
      },
      "source": [
        "%time\n",
        "\n",
        "model.to('cuda') # Run predictions on GPU\n",
        "model.predict(X)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 4 µs, sys: 0 ns, total: 4 µs\n",
            "Wall time: 7.87 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, ..., 1, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gjcrWpdq4QpJ"
      },
      "source": [
        "So, it can be seen that, hummingbird-ml is coverting our traditional ML models into a PyTorch model. Doing this, our traditional ML models are becoming faster as it can now use the GPU service. "
      ]
    }
  ]
}