sklearn_iris_customdata.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scikit-Learn Iris Model using customData\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 a Kubernetes cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dependencies\n",
    "\n",
    "* [s2i](https://github.com/openshift/source-to-image)\n",
    "* Seldon Core v1.0.3+ installed\n",
    "* `pip install sklearn seldon-core protobuf grpcio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install sklearn seldon-core protobuf grpcio \n",
    "# !pip3 install grpcio-tools\n",
    "# !pip3 install --upgrade protobuf\n",
    "# !pip3 install --upgrade pip\n",
    "!pip install -r ./custom-models/iris/requirements.txt\n",
    "!pip install --upgrade pip\n",
    "# !pip install --upgrade protobuf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 uninstall protobuf\n",
    "# !pip3 install protobuf==4.21"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 ./custom-models/iris/IrisClassifier.sav\n",
      "Model saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/dileep.gadiraju/opt/anaconda3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "\n",
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "import joblib\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "\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 = \"./custom-models/iris/IrisClassifier.sav\"\n",
    "    print(\"Saving model in %s\" % filename_p)\n",
    "    joblib.dump(p, filename_p)\n",
    "    print(\"Model saved!\")\n",
    "\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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Protobuf Specification\n",
    "\n",
    "First, we'll need to define our custom protobuf specification so that it can be leveraged."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./custom-models/iris/iris.proto\n"
     ]
    }
   ],
   "source": [
    "%%writefile ./custom-models/iris/iris.proto\n",
    "\n",
    "syntax = \"proto3\";\n",
    "\n",
    "package iris;\n",
    "\n",
    "message IrisPredictRequest {\n",
    "    float sepal_length = 1;\n",
    "    float sepal_width = 2;\n",
    "    float petal_length = 3;\n",
    "    float petal_width = 4;\n",
    "}\n",
    "\n",
    "message IrisPredictResponse {\n",
    "    float setosa = 1;\n",
    "    float versicolor = 2;\n",
    "    float virginica = 3;\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom Protobuf Compilation\n",
    "\n",
    "We will need to compile our custom protobuf for python so that we can unpack the `customData` field passed to our `predict` method later on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m grpc.tools.protoc --python_out=./custom-models/iris --proto_path=./custom-models/iris iris.proto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## gRPC test\n",
    "\n",
    "Wrap model using s2i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
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      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         2.5s\n",
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      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         3.1s\n",
      "\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Building 3.5s (3/4)                                                         \n",
      "\u001b[34m => [internal] load build definition from Dockerfile                       0.1s\n",
      "\u001b[0m\u001b[34m => => transferring dockerfile: 37B                                        0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         3.3s\n",
      "\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Building 3.7s (3/4)                                                         \n",
      "\u001b[34m => [internal] load build definition from Dockerfile                       0.1s\n",
      "\u001b[0m\u001b[34m => => transferring dockerfile: 37B                                        0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         3.4s\n",
      "\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Building 3.8s (3/4)                                                         \n",
      "\u001b[34m => [internal] load build definition from Dockerfile                       0.1s\n",
      "\u001b[0m\u001b[34m => => transferring dockerfile: 37B                                        0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         3.6s\n",
      "\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Building 4.0s (3/4)                                                         \n",
      "\u001b[34m => [internal] load build definition from Dockerfile                       0.1s\n",
      "\u001b[0m\u001b[34m => => transferring dockerfile: 37B                                        0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m => [internal] load metadata for docker.io/library/python:3.7-slim         3.7s\n",
      "\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[?25h\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[1A\u001b[0G\u001b[?25l[+] Building 4.1s (11/11) FINISHED                                              \n",
      "\u001b[34m => [internal] load build definition from Dockerfile                       0.1s\n",
      "\u001b[0m\u001b[34m => => transferring dockerfile: 37B                                        0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load .dockerignore                                          0.1s\n",
      "\u001b[0m\u001b[34m => => transferring context: 2B                                            0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load metadata for docker.io/library/python:3.7-slim         3.8s\n",
      "\u001b[0m\u001b[34m => [auth] library/python:pull token for registry-1.docker.io              0.0s\n",
      "\u001b[0m\u001b[34m => [internal] load build context                                          0.0s\n",
      "\u001b[0m\u001b[34m => => transferring context: 4.07kB                                        0.0s\n",
      "\u001b[0m\u001b[34m => [1/5] FROM docker.io/library/python:3.7-slim@sha256:aa949f5f10e9b28e1  0.0s\n",
      "\u001b[0m\u001b[34m => CACHED [2/5] COPY . /app                                               0.0s\n",
      "\u001b[0m\u001b[34m => CACHED [3/5] WORKDIR /app                                              0.0s\n",
      "\u001b[0m\u001b[34m => CACHED [4/5] RUN pip install -r requirements.txt                       0.0s\n",
      "\u001b[0m\u001b[34m => CACHED [5/5] RUN pip install --upgrade pip                             0.0s\n",
      "\u001b[0m\u001b[34m => exporting to image                                                     0.0s\n",
      "\u001b[0m\u001b[34m => => exporting layers                                                    0.0s\n",
      "\u001b[0m\u001b[34m => => writing image sha256:f520af0a00f2e1cf1b5f5790464759ff6e6ec392695a0  0.0s\n",
      "\u001b[0m\u001b[34m => => naming to docker.io/seldonio/sklearn-iris-customdata:0.1            0.0s\n",
      "\u001b[0m\u001b[?25h\n",
      "Use 'docker scan' to run Snyk tests against images to find vulnerabilities and learn how to fix them\n"
     ]
    }
   ],
   "source": [
    "# !s2i build ./custom-models/iris seldonio/seldon-core-s2i-python37-ubi8:1.7.0-dev seldonio/sklearn-iris-customdata:0.1\n",
    "!docker build ./custom-models/iris -t seldonio/sklearn-iris-customdata:0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Serve the model locally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "329e9bcbe7e8754e03e6112f386aff33def9f640d12a616fcc1c0fa7beeb6b2e\n"
     ]
    }
   ],
   "source": [
    "!docker run --name \"iris_predictor\" -d --rm -p 5001:5001 seldonio/sklearn-iris-customdata:0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys,os\n",
    "# !export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python\n",
    "# for item, value in os.environ.items():\n",
    "#     print('{}: {}'.format(item, value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test using custom protobuf payload"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "setosa: 3.1866779863776173e-06\n",
      "versicolor: 0.11372585594654083\n",
      "virginica: 0.8862709403038025\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python\n",
    "import grpc\n",
    "import sys,os\n",
    "sys.path.append(os.path.expanduser('./custom-models/iris'))\n",
    "from iris_pb2 import IrisPredictRequest, IrisPredictResponse\n",
    "\n",
    "from seldon_core.proto import prediction_pb2, prediction_pb2_grpc\n",
    "\n",
    "channel = grpc.insecure_channel(\"localhost:5001\")\n",
    "stub = prediction_pb2_grpc.ModelStub(channel)\n",
    "\n",
    "iris_request = IrisPredictRequest(\n",
    "    sepal_length=7.233, sepal_width=4.652, petal_length=7.39, petal_width=0.324\n",
    ")\n",
    "\n",
    "seldon_request = prediction_pb2.SeldonMessage()\n",
    "seldon_request.customData.Pack(iris_request)\n",
    "\n",
    "response = stub.Predict(seldon_request)\n",
    "\n",
    "iris_response = IrisPredictResponse()\n",
    "response.customData.Unpack(iris_response)\n",
    "\n",
    "print(iris_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stop serving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "iris_predictor\n"
     ]
    }
   ],
   "source": [
    "!docker rm iris_predictor --force"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup Seldon Core\n",
    "\n",
    "Use the [setup notebook](https://github.com/SeldonIO/seldon-core/blob/master/notebooks/seldon_core_setup.ipynb) to setup Seldon Core with an ingress - either Ambassador or Istio\n",
    "\n",
    "Then port-forward to that ingress on localhost:8004 in a separate terminal either with:\n",
    "\n",
    "* Ambassador: `kubectl port-forward $(kubectl get pods -n seldon -l app.kubernetes.io/name=ambassador -o jsonpath='{.items[0].metadata.name}') -n seldon 8004:8080`\n",
    "* Istio: `kubectl port-forward $(kubectl get pods -l istio=ingressgateway -n istio-system -o jsonpath='{.items[0].metadata.name}') -n istio-system 8004:80`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error from server (AlreadyExists): namespaces \"seldon\" already exists\n"
     ]
    }
   ],
   "source": [
    "!kubectl create namespace seldon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Context \"kind-seldon\" modified.\n"
     ]
    }
   ],
   "source": [
    "!kubectl config set-context $(kubectl config current-context) --namespace=seldon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy your Seldon Model\n",
    "\n",
    "We first create a configuration file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing sklearn_iris_customdata_deployment.yaml\n"
     ]
    }
   ],
   "source": [
    "%%writefile sklearn_iris_customdata_deployment.yaml\n",
    "\n",
    "apiVersion: machinelearning.seldon.io/v1\n",
    "kind: SeldonDeployment\n",
    "metadata:\n",
    "  name: seldon-deployment-example\n",
    "spec:\n",
    "  name: sklearn-iris-deployment\n",
    "  predictors:\n",
    "  - componentSpecs:\n",
    "    - spec:\n",
    "        containers:\n",
    "        - image: groszewn/sklearn-iris-customdata:0.1\n",
    "          imagePullPolicy: IfNotPresent\n",
    "          name: sklearn-iris-classifier\n",
    "    graph:\n",
    "      children: []\n",
    "      endpoint:\n",
    "        type: GRPC\n",
    "      name: sklearn-iris-classifier\n",
    "      type: MODEL\n",
    "    name: sklearn-iris-predictor\n",
    "    replicas: 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the model in our cluster\n",
    "\n",
    "Apply the Seldon Deployment configuration file we just created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seldondeployment.machinelearning.seldon.io/seldon-deployment-example created\n"
     ]
    }
   ],
   "source": [
    "!kubectl create -f sklearn_iris_customdata_deployment.yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check that the model has been deployed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Waiting for deployment \"seldon-92a927e5e90d7602e08ba9b9304f70e8\" rollout to finish: 0 of 1 updated replicas are available...\n",
      "^C\n"
     ]
    }
   ],
   "source": [
    "!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=seldon-deployment-example -o jsonpath='{.items[0].metadata.name}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test by sending prediction calls\n",
    "\n",
    "`IrisPredictRequest` sent via the `customData` field."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_request = IrisPredictRequest(\n",
    "    sepal_length=7.233, sepal_width=4.652, petal_length=7.39, petal_width=0.324\n",
    ")\n",
    "\n",
    "seldon_request = prediction_pb2.SeldonMessage()\n",
    "seldon_request.customData.Pack(iris_request)\n",
    "\n",
    "channel = grpc.insecure_channel(\"localhost:8004\")\n",
    "stub = prediction_pb2_grpc.SeldonStub(channel)\n",
    "\n",
    "metadata = [(\"seldon\", \"seldon-deployment-example\"), (\"namespace\", \"seldon\")]\n",
    "\n",
    "response = stub.Predict(request=seldon_request, metadata=metadata)\n",
    "\n",
    "iris_response = IrisPredictResponse()\n",
    "response.customData.Unpack(iris_response)\n",
    "\n",
    "print(iris_response)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleanup our deployment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seldondeployment.machinelearning.seldon.io \"seldon-deployment-example\" deleted\n"
     ]
    }
   ],
   "source": [
    "!kubectl delete -f sklearn_iris_customdata_deployment.yaml"
   ]
  }
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