examples/models/sklearn_iris/sklearn_iris.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn IRIS Model\n",
    "\n",
    " * Wrap a scikit-learn python model for use as a prediction microservice in seldon-core\n",
    "   * Run locally on Docker to test\n",
    "   * Deploy on seldon-core running on minikube\n",
    " \n",
    "## Depenencies\n",
    "\n",
    " * [Helm](https://github.com/kubernetes/helm)\n",
    " * [Minikube](https://github.com/kubernetes/minikube)\n",
    " * [S2I](https://github.com/openshift/source-to-image)\n",
    "\n",
    "```bash\n",
    "pip install sklearn\n",
    "pip install grpcio-tools\n",
    "```\n",
    "\n",
    "## Train locally\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading iris data set...\n",
      "Dataset loaded!\n",
      "Training model...\n",
      "Model trained!\n",
      "Saving model in IrisClassifier.sav\n",
      "Model saved!\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.externals import joblib\n",
    "from sklearn import datasets\n",
    "\n",
    "def main():\n",
    "    clf = LogisticRegression()\n",
    "    p = Pipeline([('clf', clf)])\n",
    "    print('Training model...')\n",
    "    p.fit(X, y)\n",
    "    print('Model trained!')\n",
    "\n",
    "    filename_p = 'IrisClassifier.sav'\n",
    "    print('Saving model in %s' % filename_p)\n",
    "    joblib.dump(p, filename_p)\n",
    "    print('Model saved!')\n",
    "    \n",
    "if __name__ == \"__main__\":\n",
    "    print('Loading iris data set...')\n",
    "    iris = datasets.load_iris()\n",
    "    X, y = iris.data, iris.target\n",
    "    print('Dataset loaded!')\n",
    "    main()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrap model using s2i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## REST test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> Installing application source...\n",
      "---> Installing dependencies ...\n",
      "Collecting scikit-learn==0.19.0 (from -r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/a4/b3/209652a5d60ce4a2a8a35ad893d7565bbb0f87ce043264ba5c9e7de304cd/scikit_learn-0.19.0-cp36-cp36m-manylinux1_x86_64.whl (12.4MB)\n",
      "Collecting scipy==0.18.1 (from -r requirements.txt (line 2))\n",
      "Downloading https://files.pythonhosted.org/packages/74/c0/f0bf4eaef1b6aa7bdd1ae5597ce1d9e729417b3ca085c47d0f1c640d34f8/scipy-0.18.1-cp36-cp36m-manylinux1_x86_64.whl (42.5MB)\n",
      "Installing collected packages: scikit-learn, scipy\n",
      "Successfully installed scikit-learn-0.19.0 scipy-0.18.1\n",
      "You are using pip version 9.0.1, however version 10.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "Build completed successfully\n"
     ]
    }
   ],
   "source": [
    "!s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "92a3d2d30811f084881df501b16eba1ac52334927ba407ecf07f627a9c67015b\r\n"
     ]
    }
   ],
   "source": [
    "!docker run --name \"iris_predictor\" -d --rm -p 5000:5000 sklearn-iris:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rm -f proto/prediction*.py\r\n",
      "rm -f proto/prediction.proto\r\n",
      "rm -rf proto/__pycache__\r\n",
      "cp ../../proto/prediction.proto ./proto\r\n",
      "python -m grpc.tools.protoc -I. --python_out=. --grpc_python_out=. ./proto/prediction.proto\r\n"
     ]
    }
   ],
   "source": [
    "!cd ../../../wrappers/testing && make build_protos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Send some random features that conform to the contract"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\r\n",
      "SENDING NEW REQUEST:\r\n",
      "{'meta': {}, 'data': {'names': ['sepal_length', 'sepal_width', 'petal_length', 'petal_width'], 'ndarray': [[6.369, 3.498, 6.062, 2.57]]}}\r\n",
      "RECEIVED RESPONSE:\r\n",
      "{'data': {'names': ['t:0', 't:1', 't:2'], 'ndarray': [[0.00021525713562610643, 0.11504072045280644, 0.8847440224115675]]}}\r\n",
      "\r\n",
      "Time 0.004708290100097656\r\n"
     ]
    }
   ],
   "source": [
    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iris_predictor\r\n"
     ]
    }
   ],
   "source": [
    "!docker rm iris_predictor --force"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## grpc test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---> Installing application source...\n",
      "---> Installing dependencies ...\n",
      "Collecting scikit-learn==0.19.0 (from -r requirements.txt (line 1))\n",
      "Downloading https://files.pythonhosted.org/packages/a4/b3/209652a5d60ce4a2a8a35ad893d7565bbb0f87ce043264ba5c9e7de304cd/scikit_learn-0.19.0-cp36-cp36m-manylinux1_x86_64.whl (12.4MB)\n",
      "Collecting scipy==0.18.1 (from -r requirements.txt (line 2))\n",
      "Downloading https://files.pythonhosted.org/packages/74/c0/f0bf4eaef1b6aa7bdd1ae5597ce1d9e729417b3ca085c47d0f1c640d34f8/scipy-0.18.1-cp36-cp36m-manylinux1_x86_64.whl (42.5MB)\n",
      "Installing collected packages: scikit-learn, scipy\n",
      "Successfully installed scikit-learn-0.19.0 scipy-0.18.1\n",
      "You are using pip version 9.0.1, however version 10.0.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\n",
      "Build completed successfully\n"
     ]
    }
   ],
   "source": [
    "!s2i build -E .s2i/environment_grpc . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b2e8b9153904380d976e3a421d345066fc735d0217efd1e478a0cab70d39ca54\r\n"
     ]
    }
   ],
   "source": [
    "!docker run --name \"iris_predictor\" -d --rm -p 5000:5000 sklearn-iris:0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test using NDArray payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "SENDING NEW REQUEST:\n",
      "data {\n",
      "  names: \"sepal_length\"\n",
      "  names: \"sepal_width\"\n",
      "  names: \"petal_length\"\n",
      "  names: \"petal_width\"\n",
      "  ndarray {\n",
      "    values {\n",
      "      list_value {\n",
      "        values {\n",
      "          number_value: 6.893\n",
      "        }\n",
      "        values {\n",
      "          number_value: 4.302\n",
      "        }\n",
      "        values {\n",
      "          number_value: 8.419\n",
      "        }\n",
      "        values {\n",
      "          number_value: 1.102\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "RECEIVED RESPONSE:\n",
      "data {\n",
      "  names: \"t:0\"\n",
      "  names: \"t:1\"\n",
      "  names: \"t:2\"\n",
      "  ndarray {\n",
      "    values {\n",
      "      list_value {\n",
      "        values {\n",
      "          number_value: 1.3326678349719564e-05\n",
      "        }\n",
      "        values {\n",
      "          number_value: 0.37925859004409174\n",
      "        }\n",
      "        values {\n",
      "          number_value: 0.6207280832775586\n",
      "        }\n",
      "      }\n",
      "    }\n",
      "  }\n",
      "}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p --grpc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test using Tensor payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------\n",
      "SENDING NEW REQUEST:\n",
      "data {\n",
      "  names: \"sepal_length\"\n",
      "  names: \"sepal_width\"\n",
      "  names: \"petal_length\"\n",
      "  names: \"petal_width\"\n",
      "  tensor {\n",
      "    shape: 1\n",
      "    shape: 4\n",
      "    values: 7.986\n",
      "    values: 3.85\n",
      "    values: 6.16\n",
      "    values: 0.769\n",
      "  }\n",
      "}\n",
      "\n",
      "RECEIVED RESPONSE:\n",
      "data {\n",
      "  names: \"t:0\"\n",
      "  names: \"t:1\"\n",
      "  names: \"t:2\"\n",
      "  tensor {\n",
      "    shape: 1\n",
      "    shape: 3\n",
      "    values: 0.005626056223331023\n",
      "    values: 0.9558037301171475\n",
      "    values: 0.03857021365952149\n",
      "  }\n",
      "}\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!python ../../../wrappers/testing/tester.py contract.json 0.0.0.0 5000 -p --grpc --tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!minikube start --memory 4096 --feature-gates=CustomResourceValidation=true --extra-config=apiserver.Authorization.Mode=RBAC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kubectl create clusterrolebinding kube-system-cluster-admin --clusterrole=cluster-admin --serviceaccount=kube-system:default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!helm init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!helm install ../../../helm-charts/seldon-core-crd --name seldon-core-crd  --set usage_metrics.enabled=true\n",
    "!helm install ../../../helm-charts/seldon-core --name seldon-core "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!eval $(minikube docker-env) && s2i build . seldonio/seldon-core-s2i-python3 sklearn-iris:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kubectl create -f sklearn_iris_deployment.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wait until ready (replicas == replicasAvailable)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!kubectl get seldondeployments seldon-deployment-example -o jsonpath='{.status}' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!cd ../../../util/api_tester && make build_protos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python ../../../util/api_tester/api-tester.py contract.json \\\n",
    "    `minikube ip` `kubectl get svc -l app=seldon-apiserver-container-app -o jsonpath='{.items[0].spec.ports[0].nodePort}'` \\\n",
    "    --oauth-key oauth-key --oauth-secret oauth-secret -p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!minikube delete"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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