KubeFlow_Pipeline_IRIS_Classifier_Kailash.ipynb

{
 "cells": [
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Topics Covered\n",
    "\n",
    "**Kubeflow Pipeline Building Demo - IRIS classifier Model training and prediction**\n",
    "1. Python function needed to train and predict\n",
    "2. Creating components from python functions\n",
    "3. Initialise kubeflow pipeline\n",
    "4. define the pipeline function and put together all the components\n",
    "5. Mounting volume for component's output storage\n",
    "6. Compiling pipeline and generating yaml - it can be directly uploaded to kubeflow and create experiments and runs using UI\n",
    "7. Create run from pipeline function using the code\n",
    "8. How to disable cache to see the each steps output on second and successive runs\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp\n",
    "import kfp.components as comp\n",
    "import requests\n",
    "import kfp.dsl as dsl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: kfp\n",
      "Version: 1.8.18\n",
      "Summary: KubeFlow Pipelines SDK\n",
      "Home-page: https://github.com/kubeflow/pipelines\n",
      "Author: The Kubeflow Authors\n",
      "Author-email: \n",
      "License: UNKNOWN\n",
      "Location: /home/at0m/.local/lib/python3.10/site-packages\n",
      "Requires: absl-py, click, cloudpickle, Deprecated, docstring-parser, fire, google-api-core, google-api-python-client, google-auth, google-cloud-storage, jsonschema, kfp-pipeline-spec, kfp-server-api, kubernetes, protobuf, pydantic, PyYAML, requests-toolbelt, strip-hints, tabulate, typer, uritemplate\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "!pip show kfp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def prepare_data():\n",
    "    import pandas as pd\n",
    "    print(\"---- Inside prepare_data component ----\")\n",
    "    # Load dataset\n",
    "    df = pd.read_csv(\"https://raw.githubusercontent.com/at0m-b0mb/KubeFlow-Pipeline-IRIS-Classifier-Demo/main/iris.csv\")\n",
    "    df = df.dropna()\n",
    "    df.to_csv(f'data/final_df.csv', index=False)\n",
    "    print(\"\\n ---- data csv is saved to PV location /data/final_df.csv ----\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_test_split():\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from sklearn.model_selection import train_test_split\n",
    "    print(\"---- Inside train_test_split component ----\")\n",
    "    final_data = pd.read_csv(f'data/final_df.csv')\n",
    "    target_column = 'class'\n",
    "    X = final_data.loc[:, final_data.columns != target_column]\n",
    "    y = final_data.loc[:, final_data.columns == target_column]\n",
    "    \n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,stratify = y, random_state=47)\n",
    "    \n",
    "    np.save(f'data/X_train.npy', X_train)\n",
    "    np.save(f'data/X_test.npy', X_test)\n",
    "    np.save(f'data/y_train.npy', y_train)\n",
    "    np.save(f'data/y_test.npy', y_test)\n",
    "    \n",
    "    print(\"\\n---- X_train ----\")\n",
    "    print(\"\\n\")\n",
    "    print(X_train)\n",
    "    \n",
    "    print(\"\\n---- X_test ----\")\n",
    "    print(\"\\n\")\n",
    "    print(X_test)\n",
    "    \n",
    "    print(\"\\n---- y_train ----\")\n",
    "    print(\"\\n\")\n",
    "    print(y_train)\n",
    "    \n",
    "    print(\"\\n---- y_test ----\")\n",
    "    print(\"\\n\")\n",
    "    print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def training_basic_classifier():\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    \n",
    "    print(\"---- Inside training_basic_classifier component ----\")\n",
    "    \n",
    "    X_train = np.load(f'data/X_train.npy',allow_pickle=True)\n",
    "    y_train = np.load(f'data/y_train.npy',allow_pickle=True)\n",
    "    \n",
    "    classifier = LogisticRegression(max_iter=500)\n",
    "    classifier.fit(X_train,y_train)\n",
    "    import pickle\n",
    "    with open(f'data/model.pkl', 'wb') as f:\n",
    "        pickle.dump(classifier, f)\n",
    "    \n",
    "    print(\"\\n logistic regression classifier is trained on iris data and saved to PV location /data/model.pkl ----\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_on_test_data():\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    import pickle\n",
    "    print(\"---- Inside predict_on_test_data component ----\")\n",
    "    with open(f'data/model.pkl','rb') as f:\n",
    "        logistic_reg_model = pickle.load(f)\n",
    "    X_test = np.load(f'data/X_test.npy',allow_pickle=True)\n",
    "    y_pred = logistic_reg_model.predict(X_test)\n",
    "    np.save(f'data/y_pred.npy', y_pred)\n",
    "    \n",
    "    print(\"\\n---- Predicted classes ----\")\n",
    "    print(\"\\n\")\n",
    "    print(y_pred)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_prob_on_test_data():\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    import pickle\n",
    "    print(\"---- Inside predict_prob_on_test_data component ----\")\n",
    "    with open(f'data/model.pkl','rb') as f:\n",
    "        logistic_reg_model = pickle.load(f)\n",
    "    X_test = np.load(f'data/X_test.npy',allow_pickle=True)\n",
    "    y_pred_prob = logistic_reg_model.predict_proba(X_test)\n",
    "    np.save(f'data/y_pred_prob.npy', y_pred_prob)\n",
    "    \n",
    "    print(\"\\n---- Predicted Probabilities ----\")\n",
    "    print(\"\\n\")\n",
    "    print(y_pred_prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_metrics():\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    from sklearn.metrics import accuracy_score,precision_score,recall_score,log_loss\n",
    "    from sklearn import metrics\n",
    "    print(\"---- Inside get_metrics component ----\")\n",
    "    y_test = np.load(f'data/y_test.npy',allow_pickle=True)\n",
    "    y_pred = np.load(f'data/y_pred.npy',allow_pickle=True)\n",
    "    y_pred_prob = np.load(f'data/y_pred_prob.npy',allow_pickle=True)\n",
    "    acc = accuracy_score(y_test, y_pred)\n",
    "    prec = precision_score(y_test, y_pred,average='micro')\n",
    "    recall = recall_score(y_test, y_pred,average='micro')\n",
    "    entropy = log_loss(y_test, y_pred_prob)\n",
    "    \n",
    "    y_test = np.load(f'data/y_test.npy',allow_pickle=True)\n",
    "    y_pred = np.load(f'data/y_pred.npy',allow_pickle=True)\n",
    "    print(metrics.classification_report(y_test, y_pred))\n",
    "    \n",
    "    print(\"\\n Model Metrics:\", {'accuracy': round(acc, 2), 'precision': round(prec, 2), 'recall': round(recall, 2), 'entropy': round(entropy, 2)})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Kubeflow pipeline creation work start from here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_prepare_data = kfp.components.create_component_from_func(\n",
    "    func=prepare_data,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_train_test_split = kfp.components.create_component_from_func(\n",
    "    func=train_test_split,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1','scikit-learn==1.3.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_training_basic_classifier = kfp.components.create_component_from_func(\n",
    "    func=training_basic_classifier,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1','scikit-learn==1.3.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_predict_on_test_data = kfp.components.create_component_from_func(\n",
    "    func=predict_on_test_data,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1','scikit-learn==1.3.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_predict_prob_on_test_data = kfp.components.create_component_from_func(\n",
    "    func=predict_prob_on_test_data,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1','scikit-learn==1.3.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "create_step_get_metrics = kfp.components.create_component_from_func(\n",
    "    func=get_metrics,\n",
    "    base_image='python:3.10',\n",
    "    packages_to_install=['pandas==2.1.1','numpy==1.26.1','scikit-learn==1.3.1']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Define the pipeline\n",
    "@dsl.pipeline(\n",
    "   name='IRIS classifier Kubeflow Pipeline',\n",
    "   description='IRIS classifier by Kailash'\n",
    ")\n",
    "# Define parameters to be fed into pipeline\n",
    "def iris_classifier_pipeline(data_path: str):\n",
    "    vop = dsl.VolumeOp(\n",
    "    name=\"t-vol\",\n",
    "    resource_name=\"t-vol\", \n",
    "    size=\"1Gi\", \n",
    "    modes=dsl.VOLUME_MODE_RWO)\n",
    "    \n",
    "    prepare_data_task = create_step_prepare_data().add_pvolumes({data_path: vop.volume})\n",
    "    train_test_split = create_step_train_test_split().add_pvolumes({data_path: vop.volume}).after(prepare_data_task)\n",
    "    classifier_training = create_step_training_basic_classifier().add_pvolumes({data_path: vop.volume}).after(train_test_split)\n",
    "    log_predicted_class = create_step_predict_on_test_data().add_pvolumes({data_path: vop.volume}).after(classifier_training)\n",
    "    log_predicted_probabilities = create_step_predict_prob_on_test_data().add_pvolumes({data_path: vop.volume}).after(log_predicted_class)\n",
    "    log_metrics_task = create_step_get_metrics().add_pvolumes({data_path: vop.volume}).after(log_predicted_probabilities)\n",
    "\n",
    "    \n",
    "    prepare_data_task.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    train_test_split.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    classifier_training.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    log_predicted_class.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    log_predicted_probabilities.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    log_metrics_task.execution_options.caching_strategy.max_cache_staleness = \"P0D\"\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "kfp.compiler.Compiler().compile(\n",
    "    pipeline_func=iris_classifier_pipeline,\n",
    "    package_path='KubeFlow_Pipeline_IRIS_Classifier_Kailash.yaml')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = kfp.Client()\n",
    "#session_cookie = \"MTY2MDY0Mjg0OXxOd3dBTkRSVE5FeElTMEZDVDFVeU5EZE1SMHhUVHpRMU5FcFpNMWRNVWpaTFVrOHlXRFJOVlRReVVFNUxOazFZVEVWQ05FUkZTRUU9fM7IcyOyK49OM0dMDjRJR85gqDksj-YOOLsagNs-_-KR\"\n",
    "# HOST = \"http://localhost:8080/\"\n",
    "# namespace = \"kubeflow\"\n",
    "# client = kfp.Client(\n",
    "#     host=f\"{HOST}/pipeline\",\n",
    "#     #cookies=f\"authservice_session={session_cookie}\",\n",
    "#     namespace=namespace,\n",
    "# )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-10-16\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<a href=\"/pipeline/#/experiments/details/ad24eee3-1475-475f-9aa8-6425a4cebe18\" target=\"_blank\" >Experiment details</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<a href=\"/pipeline/#/runs/details/0ce3ceaa-4342-4bc7-9ace-631340a201ea\" target=\"_blank\" >Run details</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DATA_PATH = '/data'\n",
    "\n",
    "import datetime\n",
    "print(datetime.datetime.now().date())\n",
    "\n",
    "\n",
    "pipeline_func = iris_classifier_pipeline\n",
    "experiment_name = 'iris_classifier_exp' +\"_\"+ str(datetime.datetime.now().date())\n",
    "run_name = pipeline_func.__name__ + ' run'\n",
    "namespace = \"kubeflow\"\n",
    "\n",
    "arguments = {\"data_path\":DATA_PATH}\n",
    "\n",
    "kfp.compiler.Compiler().compile(pipeline_func,  \n",
    "  '{}.zip'.format(experiment_name))\n",
    "\n",
    "run_result = client.create_run_from_pipeline_func(pipeline_func, \n",
    "                                                  experiment_name=experiment_name, \n",
    "                                                  run_name=run_name, \n",
    "                                                  arguments=arguments)\n",
    "\n",
    "# from kubernetes import client as k8s_client\n",
    "# pipeline_conf = kfp.dsl.PipelineConf()\n",
    "# pipeline_conf.set_image_pull_secrets([k8s_client.V1ObjectReference(namespace='kubeflow', \n",
    "#                                                                                  name=\"secret\")])\n",
    "# pipeline_conf.set_image_pull_policy(\"IfNotPresent\")\n",
    "    \n",
    "# Compile pipeline to generate compressed YAML definition of the pipeline.\n",
    "# kfp.compiler.Compiler().compile(pipeline_func,  \n",
    "#   '{}.zip'.format(experiment_name))\n",
    "\n",
    "# Submit pipeline directly from pipeline function\n",
    "# run_result = client.create_run_from_pipeline_func(pipeline_func, \n",
    "#                                                   experiment_name=experiment_name, \n",
    "#                                                   run_name=run_name, \n",
    "#                                                   arguments=arguments,\n",
    "#                                                   namespace = namespace,\n",
    "#                                                   pipeline_conf=pipeline_conf)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  }
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