bento.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting iris_classifier.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile iris_classifier.py\n",
    "#writing script for BentoML Service Class\n",
    "from bentoml import env,artifacts,api, BentoService\n",
    "from bentoml.artifact import SklearnModelArtifact\n",
    "from bentoml.adapters import DataframeInput\n",
    "\n",
    "@env(auto_pip_dependencies=True,\n",
    "     conda_dependencies=['bentoml'])\n",
    "@artifacts([SklearnModelArtifact(\"model\")])\n",
    "class IrisClassifier(BentoService):\n",
    "    @api(input=DataframeInput())\n",
    "  \n",
    "    def predict(self,df):\n",
    "        return self.artifact.model.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting main.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile main.py\n",
    "\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.datasets import load_iris\n",
    "from iris_classifier import IrisClassifier \n",
    "\n",
    "if __name__==\"__main__\":\n",
    "    #load data\n",
    "    iris=load_iris()\n",
    "    X,y=iris.data, iris.target\n",
    "\n",
    "    #train model\n",
    "    clf=SVC()\n",
    "    clf.fit(X,y)\n",
    "\n",
    "    #create an iris classifier service instance \n",
    "    service=IrisClassifier()\n",
    "\n",
    "    #pack the newly trained service classifier\n",
    "    service.pack(\"model\",clf)\n",
    "\n",
    "    # Save the prediction service to disk for model serving\n",
    "    saved_path=service.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2020-07-26 06:42:24,979] INFO - BentoService bundle 'IrisClassifier:20200726064151_392FB8' saved to: C:\\Users\\Yashika Sharma/bentoml/repository/IrisClassifier\\20200726064151_392FB8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Yashika Sharma\\anaconda3\\envs\\bento\\lib\\site-packages\\setuptools\\distutils_patch.py:25: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n",
      "  warnings.warn(\n",
      "C:\\Users\\Yashika Sharma\\AppData\\Roaming\\Python\\Python38\\site-packages\\traitlets\\config\\loader.py:795: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if len(key) is 1:\n",
      "C:\\Users\\Yashika Sharma\\AppData\\Roaming\\Python\\Python38\\site-packages\\traitlets\\config\\loader.py:804: SyntaxWarning: \"is\" with a literal. Did you mean \"==\"?\n",
      "  if len(key) is 1:\n"
     ]
    }
   ],
   "source": [
    "!python main.py\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Yashika Sharma\\anaconda3\\envs\\bento\\lib\\site-packages\\setuptools\\distutils_patch.py:25: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'saved_path' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-c8234edf732c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbentoml\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mloaded_iris\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mbentoml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msaved_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'saved_path' is not defined"
     ]
    }
   ],
   "source": [
    "import bentoml\n",
    "loaded_iris=bentoml.load(saved_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!bentoml get IrisClassifier:latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!bentoml get --print-location IrisClassifier:latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!bentoml serve IrisClassifier:latest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!cd {\"saved_path\"} && docker build --quiet -t iris-classifier-server ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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thank you for sharing