{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Kubeflow Pipelines e2e mnist example\n",
    "\n",
    "In this notebook you will create e2e mnist Kubeflow Pipeline to perfom:\n",
    "- Hyperparameter tuning using Katib\n",
    "- Distributive training with the best hyperparameters using TFJob\n",
    "- Serve the trained model using KFServing\n",
    "\n",
    "Reference documentation:\n",
    "\n",
    "- https://www.kubeflow.org/docs/components/training/tftraining/\n",
    "- https://www.kubeflow.org/docs/components/katib/\n",
    "- https://www.kubeflow.org/docs/components/kfserving/\n",
    "\n",
    "**Note**: This Pipeline runs in the multi-user mode. Follow [this guide](https://github.com/kubeflow/katib/tree/master/examples/v1beta1/kubeflow-pipelines#multi-user-pipelines-setup) to give your Notebook access to Kubeflow Pipelines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Install required packages (Kubeflow Pipelines and Katib SDK).\n",
    "!pip install kfp==1.8.12\n",
    "!pip install kubeflow-katib==0.13.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp\n",
    "import kfp.dsl as dsl\n",
    "from kfp import components\n",
    "\n",
    "from kubeflow.katib import ApiClient\n",
    "from kubeflow.katib import V1beta1ExperimentSpec\n",
    "from kubeflow.katib import V1beta1AlgorithmSpec\n",
    "from kubeflow.katib import V1beta1ObjectiveSpec\n",
    "from kubeflow.katib import V1beta1ParameterSpec\n",
    "from kubeflow.katib import V1beta1FeasibleSpace\n",
    "from kubeflow.katib import V1beta1TrialTemplate\n",
    "from kubeflow.katib import V1beta1TrialParameterSpec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the Pipelines tasks\n",
    "\n",
    "To run this Pipeline, you should define:\n",
    "1. Katib hyperparameter tuning\n",
    "2. TFJob training\n",
    "3. KFServing inference\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1. Katib hyperparameter tuning task\n",
    "\n",
    "Create the Kubeflow Pipelines task for the Katib hyperparameter tuning. This Experiment uses \"random\" algorithm and TFJob for the Trial's worker.\n",
    "\n",
    "The Katib Experiment is similar to this example: https://github.com/kubeflow/katib/blob/master/examples/v1beta1/kubeflow-training-operator/tfjob-mnist-with-summaries.yaml."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should define the Experiment name, namespace and number of training steps in the arguments.\n",
    "def create_katib_experiment_task(experiment_name, experiment_namespace, training_steps):\n",
    "    # Trial count specification.\n",
    "    max_trial_count = 5\n",
    "    max_failed_trial_count = 3\n",
    "    parallel_trial_count = 2\n",
    "\n",
    "    # Objective specification.\n",
    "    objective = V1beta1ObjectiveSpec(\n",
    "        type=\"minimize\",\n",
    "        goal=0.001,\n",
    "        objective_metric_name=\"loss\"\n",
    "    )\n",
    "\n",
    "    # Algorithm specification.\n",
    "    algorithm = V1beta1AlgorithmSpec(\n",
    "        algorithm_name=\"random\",\n",
    "    )\n",
    "\n",
    "    # Experiment search space.\n",
    "    # In this example we tune learning rate and batch size.\n",
    "    parameters = [\n",
    "        V1beta1ParameterSpec(\n",
    "            name=\"learning_rate\",\n",
    "            parameter_type=\"double\",\n",
    "            feasible_space=V1beta1FeasibleSpace(\n",
    "                min=\"0.01\",\n",
    "                max=\"0.05\"\n",
    "            ),\n",
    "        ),\n",
    "        V1beta1ParameterSpec(\n",
    "            name=\"batch_size\",\n",
    "            parameter_type=\"int\",\n",
    "            feasible_space=V1beta1FeasibleSpace(\n",
    "                min=\"80\",\n",
    "                max=\"100\"\n",
    "            ),\n",
    "        )\n",
    "    ]\n",
    "\n",
    "    # Experiment Trial template.\n",
    "    # TODO (andreyvelich): Use community image for the mnist example.\n",
    "    trial_spec = {\n",
    "        \"apiVersion\": \"kubeflow.org/v1\",\n",
    "        \"kind\": \"TFJob\",\n",
    "        \"spec\": {\n",
    "            \"tfReplicaSpecs\": {\n",
    "                \"Chief\": {\n",
    "                    \"replicas\": 1,\n",
    "                    \"restartPolicy\": \"OnFailure\",\n",
    "                    \"template\": {\n",
    "                        \"metadata\": {\n",
    "                            \"annotations\": {\n",
    "                                \"sidecar.istio.io/inject\": \"false\"\n",
    "                            }\n",
    "                        },\n",
    "                        \"spec\": {\n",
    "                            \"containers\": [\n",
    "                                {\n",
    "                                    \"name\": \"tensorflow\",\n",
    "                                    \"image\": \"docker.io/liuhougangxa/tf-estimator-mnist\",\n",
    "                                    \"command\": [\n",
    "                                        \"python\",\n",
    "                                        \"/opt/model.py\",\n",
    "                                        \"--tf-train-steps=\" + str(training_steps),\n",
    "                                        \"--tf-learning-rate=${trialParameters.learningRate}\",\n",
    "                                        \"--tf-batch-size=${trialParameters.batchSize}\"\n",
    "                                    ]\n",
    "                                }\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                },\n",
    "                \"Worker\": {\n",
    "                    \"replicas\": 1,\n",
    "                    \"restartPolicy\": \"OnFailure\",\n",
    "                    \"template\": {\n",
    "                        \"metadata\": {\n",
    "                            \"annotations\": {\n",
    "                                \"sidecar.istio.io/inject\": \"false\"\n",
    "                            }\n",
    "                        },\n",
    "                        \"spec\": {\n",
    "                            \"containers\": [\n",
    "                                {\n",
    "                                    \"name\": \"tensorflow\",\n",
    "                                    \"image\": \"docker.io/liuhougangxa/tf-estimator-mnist\",\n",
    "                                    \"command\": [\n",
    "                                        \"python\",\n",
    "                                        \"/opt/model.py\",\n",
    "                                        \"--tf-train-steps=\" + str(training_steps),\n",
    "                                        \"--tf-learning-rate=${trialParameters.learningRate}\",\n",
    "                                        \"--tf-batch-size=${trialParameters.batchSize}\"\n",
    "                                    ]\n",
    "                                }\n",
    "                            ]\n",
    "                        }\n",
    "                    }\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # Configure parameters for the Trial template.\n",
    "    trial_template = V1beta1TrialTemplate(\n",
    "        primary_container_name=\"tensorflow\",\n",
    "        trial_parameters=[\n",
    "            V1beta1TrialParameterSpec(\n",
    "                name=\"learningRate\",\n",
    "                description=\"Learning rate for the training model\",\n",
    "                reference=\"learning_rate\"\n",
    "            ),\n",
    "            V1beta1TrialParameterSpec(\n",
    "                name=\"batchSize\",\n",
    "                description=\"Batch size for the model\",\n",
    "                reference=\"batch_size\"\n",
    "            ),\n",
    "        ],\n",
    "        trial_spec=trial_spec\n",
    "    )\n",
    "\n",
    "    # Create an Experiment from the above parameters.\n",
    "    experiment_spec = V1beta1ExperimentSpec(\n",
    "        max_trial_count=max_trial_count,\n",
    "        max_failed_trial_count=max_failed_trial_count,\n",
    "        parallel_trial_count=parallel_trial_count,\n",
    "        objective=objective,\n",
    "        algorithm=algorithm,\n",
    "        parameters=parameters,\n",
    "        trial_template=trial_template\n",
    "    )\n",
    "\n",
    "    # Create the KFP task for the Katib Experiment.\n",
    "    # Experiment Spec should be serialized to a valid Kubernetes object.\n",
    "    katib_experiment_launcher_op = components.load_component_from_url(\n",
    "        \"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/katib-launcher/component.yaml\")\n",
    "    op = katib_experiment_launcher_op(\n",
    "        experiment_name=experiment_name,\n",
    "        experiment_namespace=experiment_namespace,\n",
    "        experiment_spec=ApiClient().sanitize_for_serialization(experiment_spec),\n",
    "        experiment_timeout_minutes=60,\n",
    "        delete_finished_experiment=False)\n",
    "\n",
    "    return op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2. TFJob training task\n",
    "\n",
    "Create the Kubeflow Pipelines task for the TFJob training. In this example TFJob runs the Chief and Worker with 1 replica.\n",
    "\n",
    "Learn more about TFJob replica specifications in the Kubeflow docs: https://www.kubeflow.org/docs/components/training/tftraining/#what-is-tfjob."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function converts Katib Experiment HP results to args.\n",
    "def convert_katib_results(katib_results) -> str:\n",
    "    import json\n",
    "    import pprint\n",
    "    katib_results_json = json.loads(katib_results)\n",
    "    print(\"Katib results:\")\n",
    "    pprint.pprint(katib_results_json)\n",
    "    best_hps = []\n",
    "    for pa in katib_results_json[\"currentOptimalTrial\"][\"parameterAssignments\"]:\n",
    "        if pa[\"name\"] == \"learning_rate\":\n",
    "            best_hps.append(\"--tf-learning-rate=\" + pa[\"value\"])\n",
    "        elif pa[\"name\"] == \"batch_size\":\n",
    "            best_hps.append(\"--tf-batch-size=\" + pa[\"value\"])\n",
    "    print(\"Best Hyperparameters: {}\".format(best_hps))\n",
    "    return \" \".join(best_hps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should define the TFJob name, namespace, number of training steps, output of Katib and model volume tasks in the arguments.\n",
    "def create_tfjob_task(tfjob_name, tfjob_namespace, training_steps, katib_op, model_volume_op):\n",
    "    import json\n",
    "    # Get parameters from the Katib Experiment.\n",
    "    # Parameters are in the format \"--tf-learning-rate=0.01 --tf-batch-size=100\"\n",
    "    convert_katib_results_op = components.func_to_container_op(convert_katib_results)\n",
    "    best_hp_op = convert_katib_results_op(katib_op.output)\n",
    "    best_hps = str(best_hp_op.output)\n",
    "\n",
    "    # Create the TFJob Chief and Worker specification with the best Hyperparameters.\n",
    "    # TODO (andreyvelich): Use community image for the mnist example.\n",
    "    tfjob_chief_spec = {\n",
    "        \"replicas\": 1,\n",
    "        \"restartPolicy\": \"OnFailure\",\n",
    "        \"template\": {\n",
    "            \"metadata\": {\n",
    "                \"annotations\": {\n",
    "                    \"sidecar.istio.io/inject\": \"false\"\n",
    "                }\n",
    "            },\n",
    "            \"spec\": {\n",
    "                \"containers\": [\n",
    "                    {\n",
    "                        \"name\": \"tensorflow\",\n",
    "                        \"image\": \"docker.io/liuhougangxa/tf-estimator-mnist\",\n",
    "                        \"command\": [\n",
    "                            \"sh\",\n",
    "                            \"-c\"\n",
    "                        ],\n",
    "                        \"args\": [\n",
    "                            \"python /opt/model.py --tf-export-dir=/mnt/export --tf-train-steps={} {}\".format(training_steps, best_hps)\n",
    "                        ],\n",
    "                        \"volumeMounts\": [\n",
    "                            {\n",
    "                                \"mountPath\": \"/mnt/export\",\n",
    "                                \"name\": \"model-volume\"\n",
    "                            }\n",
    "                        ]\n",
    "                    }\n",
    "                ],\n",
    "                \"volumes\": [\n",
    "                    {\n",
    "                        \"name\": \"model-volume\",\n",
    "                        \"persistentVolumeClaim\": {\n",
    "                            \"claimName\": str(model_volume_op.outputs[\"name\"])\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    tfjob_worker_spec = {\n",
    "        \"replicas\": 1,\n",
    "        \"restartPolicy\": \"OnFailure\",\n",
    "        \"template\": {\n",
    "            \"metadata\": {\n",
    "                \"annotations\": {\n",
    "                    \"sidecar.istio.io/inject\": \"false\"\n",
    "                }\n",
    "            },\n",
    "            \"spec\": {\n",
    "                \"containers\": [\n",
    "                    {\n",
    "                        \"name\": \"tensorflow\",\n",
    "                        \"image\": \"docker.io/liuhougangxa/tf-estimator-mnist\",\n",
    "                        \"command\": [\n",
    "                            \"sh\",\n",
    "                            \"-c\",\n",
    "                        ],\n",
    "                        \"args\": [\n",
    "                          \"python /opt/model.py --tf-export-dir=/mnt/export --tf-train-steps={} {}\".format(training_steps, best_hps) \n",
    "                        ],\n",
    "                    }\n",
    "                ],\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    # Create the KFP task for the TFJob.\n",
    "    tfjob_launcher_op = components.load_component_from_url(\n",
    "        \"https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/launcher/component.yaml\")\n",
    "    op = tfjob_launcher_op(\n",
    "        name=tfjob_name,\n",
    "        namespace=tfjob_namespace,\n",
    "        chief_spec=json.dumps(tfjob_chief_spec),\n",
    "        worker_spec=json.dumps(tfjob_worker_spec),\n",
    "        tfjob_timeout_minutes=60,\n",
    "        delete_finished_tfjob=False)\n",
    "    return op"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3. KFServing inference\n",
    "\n",
    "Create the Kubeflow Pipelines task for the KFServing inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You should define the model name, namespace, output of the TFJob and model volume tasks in the arguments.\n",
    "def create_kfserving_task(model_name, model_namespace, tfjob_op, model_volume_op):\n",
    "\n",
    "    inference_service = '''\n",
    "apiVersion: \"serving.kubeflow.org/v1beta1\"\n",
    "kind: \"InferenceService\"\n",
    "metadata:\n",
    "  name: {}\n",
    "  namespace: {}\n",
    "  annotations:\n",
    "    \"sidecar.istio.io/inject\": \"false\"\n",
    "spec:\n",
    "  predictor:\n",
    "    tensorflow:\n",
    "      storageUri: \"pvc://{}/\"\n",
    "'''.format(model_name, model_namespace, str(model_volume_op.outputs[\"name\"]))\n",
    "\n",
    "    kfserving_launcher_op = components.load_component_from_url(\n",
    "        'https://raw.githubusercontent.com/kubeflow/pipelines/master/components/kubeflow/kfserving/component.yaml')\n",
    "    kfserving_launcher_op(action=\"create\", inferenceservice_yaml=inference_service).after(tfjob_op)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the Kubeflow Pipeline\n",
    "\n",
    "You should create the Kubeflow Pipeline from the above tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=\"/pipeline/#/experiments/details/dfb75db0-cc0f-410e-966d-f24526f1bc74\" 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/a6b4800d-6b45-43d0-979d-7c2e2dcc38aa\" target=\"_blank\" >Run details</a>."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run ID:  a6b4800d-6b45-43d0-979d-7c2e2dcc38aa\n"
     ]
    }
   ],
   "source": [
    "name=\"mnist-e2e\"\n",
    "namespace=\"kubeflow-user-example-com\"\n",
    "training_steps=\"200\"\n",
    "\n",
    "@dsl.pipeline(\n",
    "    name=\"End to End Pipeline\",\n",
    "    description=\"An end to end mnist example including hyperparameter tuning, train and inference\"\n",
    ")\n",
    "def mnist_pipeline(name=name, namespace=namespace, training_steps=training_steps):\n",
    "    # Run the hyperparameter tuning with Katib.\n",
    "    katib_op = create_katib_experiment_task(name, namespace, training_steps)\n",
    "\n",
    "    # Create volume to train and serve the model.\n",
    "    model_volume_op = dsl.VolumeOp(\n",
    "        name=\"model-volume\",\n",
    "        resource_name=\"model-volume\",\n",
    "        size=\"1Gi\",\n",
    "        modes=dsl.VOLUME_MODE_RWO\n",
    "    )\n",
    "\n",
    "    # Run the distributive training with TFJob.\n",
    "    tfjob_op = create_tfjob_task(name, namespace, training_steps, katib_op, model_volume_op)\n",
    "\n",
    "    # Create the KFServing inference.\n",
    "    create_kfserving_task(name, namespace, tfjob_op, model_volume_op)\n",
    "\n",
    "# Run the Kubeflow Pipeline in the user's namespace.\n",
    "kfp_client=kfp.Client()\n",
    "run_id = kfp_client.create_run_from_pipeline_func(mnist_pipeline, namespace=namespace, arguments={}).run_id\n",
    "print(\"Run ID: \", run_id)"
   ]
  },
  {
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sYiZ/pbyTYTuP3pa7Yff2642J/F+Hjz+T5m+2lCnKYxf2tPLSRSiD2NgTdWrJsK8GKqHZ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\nTutXlCCM4dP+efM4DFFFH6+qoegI25VDxZ51ZRjq6+3jbZ+oxVWaB9m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AAQQQ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\nXZme4dme9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AACCCQgsGXrFps2bVoCQxN++auvvrJ33nkn4RGSGPLpp59a165d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\nFbeSJUtYKXcrXaqk5c6d+zjnyugInHoCB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bcY5RCi197WjOCkNC23oNnPdpmJwoULR5m3\np7H1IX1+fn6eDiXbvhlfz3CCxVoyQx/C56lpbWRtWis5KXPSUh4fffSRk6GsY2owWjO99UGGNAQQ\nQAABBBBAAAEEEEAguoD3f+VF78lnBBBAAIFkF9Cg8MQ/ZsjqwN88jn1/8eryUqX2kjPr9V8r99iR\nnQikkIC/v3+8r6SBSQ182oHj8+fPy19//WXOt2v8aukIbfqwtvDwcNHP+r5+/Xqz39sXLS0RW9Ma\nwHbr3r27NGjQwHzUcgz60DhPLbbgt91f15CUpg+b06YlNSZOmGjKQmhQW8tWuJtm/65evdpkYp88\nedIpaaGBcDXU0hJapzl68+SS2T+zvPzyy6b8xebNm+WLL74QHXPhwoXStk1bSlNER+QzAggggAAC\nCCCAAAIZXICAcQb/BmD5CCBw4wR2hQXK8DUT5fj5k14noYHkHUd3yxt1XpLSAcW99uMAAqlVYODA\ngfL000+b4OY333wj9kPz7IfGacay3TSQWb16dVmwYIG9K8a7nd2sQdbp06ebh8hpbWI7c9k+wR04\ntYPSu3btkg0bNthd4v3uHnvx4sVy7do189J6xglt9oPm7IffaV1hT+vVshEaMNb21ltvSZs2bUyG\ntK55586dom5qqxnK+nA+uy1atEguX75sXLTW8siRI03wXcuD9OrZyzxgr1ixYiZgrPfiUvglyZGZ\nH0jZfrwjgAACCCCAAAIIIICA9ZukICCAAAIIpLyAZhbHFSy2Z6UBZe07ttEgMo1tFN7TjMD+/ftN\nwNM94ZIlS4o+AE9b7dq1nYxfDcbqK7bmDqTOnDnTdK1Xr16MgLG7Pu+IESNkypQpTnmK2Mb3dCx/\nvvwmw1ezcrUusj60ToOxiQkYaz3iFStWSHBwsPxf+/8zl9OgcfTWsGFDE+j9448/zDU18Otu+gDA\n4sUjf4hU+PbIchsaANZMbX3Vr19fHnroIZPJrOfp9Xr26ukewtRGdj8UL8pBPiCAAAIIIIAAAggg\ngECGFYj5u4wZloKFI4AAAiknMOH36bFmFtszqVOsmoxs1N/01XNoCKQlgTJlysR4QFulSpVkyJAh\nYgcqq1WrZrJn3esqVaqUtGvXzr3L2dYgrTtQq3WAPZWSKFKkiHTr1s2UYdCTNSNZs5MffvhhZ6z4\nbvhn9pf+/fs7wVc9z8509jaGt7rDXbp0MQ+d0/M0UKwvDey6M631mGZFDxgwwBzTNdpNr6vB5F69\nejm1je35ucew59epUyezZn2gn7vVqlVL+vTp497FNgIIIIAAAggggAACCCBgBPysp3FfwwIBBLwL\naHZcxYoVvXfgCALRBILPhEbbE/WjZhd3WBA10y9qj8hPGix+rcZzoqUrBq4YJRcuX5TpLf8bZ5bx\nib2HvT5Iy9N12Jd2BUJDQ0WDsqmtvf7666b2rtYp1sxYrWMcEhIimlnsDn665619dD25c+eOETx1\n97O3tezC2bNnJSAgwAmc2sfc7xcuXJCDIQdNnV4txeCp7q+7f1zbWlJC6zjbAe+4+ns7fujQIbH+\nH0zi8zA+LYGhGcLatLSEt2C0Htf5aQA9e/bs+jFK02uqmY7h7T5EOYEPCCCAAAIIIIAAAgggkCEF\nKEmRIW87i0YAgRsp8PuRHXFe3lOwWE/Sc+sWqRrn+XRAIDUJ6EPv7AffeZtXfPq4z9UawNGzZt3H\n7W0NnN5Z6k77Y5Lf3fWMkzJYoUKFRF/xaRog1mB3fFps84vv9eJzHfoggAACCCCAAAIIIIBA+hWg\nJEX6vbes7AYIXLp0yXr6/GT56aefo1x948aNZv/Spcui7Pf0YfLkKbJqVeSDjjwdj2vfli1bZeqX\n08wYv/yyUqZM/TKuU3x6PDAw0Kz9r+3bfXqdhAxu3w9PdUMTMk5i++4NCzKnZs+czZSb0OCwu3kL\nFmsf+1x3f7YRQAABBBBAAAEEEEAAAQQQQACB5BIgYJxckoyDgCVw5sxZGfLW2/LV9BmOx+rVa6RN\n23YyesxY0V/Pjq3prx0PHvKWzJ03L7ZuXo8FBQXJI4+2kIEDB8nMmbPkG+s1aNBgr/1T4sCWLduM\nybq161PicvG6xtJlP5g5nT17Ll79fdXpwpWLsudEoCk7YQeNYwsW+2oejItAcgto5q++tFwEDQEE\nEEAAAQQQQAABBBBAIG0JUJIibd0vZpvGBNav/1Xad+hogibz5nxr1e+8w6cr2PZXZBbvmDGjpXWr\nlvLSy119ej0GT5xAyYDrv1o+cVPkg+y0VvHSfGWkacl6UWoWR7+C+9zox/iMQGoR6NevX2qZCvNA\nAAEEEEAAAQQQQAABBBBIoAAZxgkEozsC8RXYtHmzPN3pGSdYbNfQ1CByw0aNZeXKVc5QHTo+JX37\nvu58Dg4+II+1e0KKFb9D6j/YUObMmesc0wdDde/RSypUrCxVqlaXQYOHSHh4uMyaNVsGDBho+r33\n3vvy6aefO+fYG4uXLJXGTZqZcWvXqSvjxo2XiCsRsmbNGjOn+fMXmK46N52jXc5Cy1zo5wULFtpD\nJdv7b79ttLKiW5o56XqffvoZOXz4sBm/a9du0rnL8861li//0cxD52M7fvbZF2ZNZe4uZ1yOHTtm\n+uu63nvvA6lRs7YZu3nzFjFKfXwxebI5rn2GD3/fuY6vNyrnLxvlEho0Xrp3ZZzBYj0p+rlRBuID\nAggggAACCCCAAAIIIIAAAgggkEQBAsZJBOR0BDwJ7Ny5U9q37ygXLlyQfq+/FuWBS+fOnZN//tkl\np06ddk7dvXu37N271/m8YcMGs/3OO29L1qxZpGev3rJm7VqzTwOoS5culW6vvir/6dhRpkyZKn1f\n6yf33FNOHnignunTsEEDqVYt6oPRtC7yiy++ZI2XVd4bPkwqVaokI0aOlLHjPpLy5cubOS35t8by\njz/9ZD4vtQLM2jRQq3PWfsnZNKjb8T9PiZbSeOutITJ40ED534oV8sEHI81l9u3bL3v2XHc5efKk\nmcfZc2fFdtQ1PPnE49KiRQuZZ5Xy+OabWebc9z74QCZMnCiNGjUSdTwZdlI6dPyPhIaGOkv44Ycf\npHu3V6Vo0aIy8eOPZfPm351jvtzImTWH2CUo7Oto0HjqtvkycMUouXD5or07yrueo+fSEEAAAQQQ\nQAABBBBAAAEEEEAAAV8JUJLCV7KMm6EFDhw4INmzZzcGIz8cJc2aNTX1POOLkjdvXvl29kzTvUnj\nRnJfjVryzdczJSBXLtFgcp8+vaVt29bm+L79+02gdOCA/lKvXj2TjdzYOqdy5UpRLveF9TA9bV9N\nm2qyntu3/z/RQPXkKVOkZ8/uUqdOHVlhBWu1jvJaKzitc1i7bp3JQF65aqVVTuPOGCU1zpw5Y4LJ\nZmDrS/78+eX+++vYH+N8D78cLsOGviulSpWS0qVLyXbrwXjqFhISEue5doeePbrLs88+Y+a5cOFC\nWblqlbzyykvypfXgv/vuu0/efect0/VeK6C+aNFiCQs7ZZ8qI6ygsjrdfffd0qp1G9GHBFapUtk5\n7suNlyt3kL+P7pbjF8Kcy8zdscTZjr6RJ3uA6Dk0BBBAAAEEEEAAAQQQQAABBBBAwJcCZBj7Upex\nM6yAPuhpwfx50rXrK6IlEvq4yk3EB6V2rVpOt4IFC0qhQoUkKDjIykLeb/aPHPmhVKxUxbzmz59v\n9mkZi9hasHX+XXeVjvIQKg2OhoWFyfnz56VJk8YmI1ozkTWbuE/vXma4VatXye+//yHNmjaJMXxQ\ncLD06NnLeWm2ckJatmzZZI+VWf3Ms53l7rL3yPMvvGTmkJAxChcuYrr7Z/Y3a4u4ckWOHz9uxrmn\nXDlnqKpVq8rgwQOt4HAZZ59mFmvLnz+feY+4GmHeU+KLZgq/cf/LooHguJr20b5kF8clxXEEEEAA\nAQQQQAABBBBAAAEEEEiqAAHjpApyPgIeBDQ4qYHJ3j17SjkraLl8+XKZPn2G6XnTTTeZdy2voE0z\nejVo626HDh1yPmowVz/nzp1H8uXLa/a/+847svPv7ea1/IdlVq3hydZ1otbFdQb4d6NAgYKiQeVL\nly45h3bv3mMyenPkyCFNGjc2+99/f4R5b9OmtckyHjFilPmsWdLRW6k775RlS5c4r1GjIktJRO/n\n7bPWSv7oo/HSqlUr2f7XVtm86bcoAe2sN2WREyeOO6efPn3G2Y5t47aA28zhgwcPOt00EP7yK6/K\nzp3/OPtu9EbpgOIytvHgGOUp3PPSMhTaR/vSEEAAAQQQQAABBBBAAAEEEEAAAV8LUJLC18KMn6EF\nNOt13NjR0qBhY3mj/5tSs0YNp+TBrNmzpUyZu2TFL7/EyKr9beNGGT16jCkx8elnkQ+v0wxfLZ+g\n2cbjJ0yQEiWKy+XLl636xa+LZuquWb0yVutWLfWhb6usesh9pPOznayH7q025S06tG9vzitUqKCU\nv7e8bN22VapXq2bGrFu3ril3oeUpKlSIWb9Yg99ly94d63Xtg0uWLJGDrkC47r/LKkOh7cqVy3Lg\nQIgsXbbMBM9PWMF0DaLXsjKtN23abJXNmGqs9CF18Wnq3qxZM9FrfvnlV1KkWBGrPvIQiYiIEA1y\np6amWcOv1XhOzlrlJn4/skP2hgWZ6ZUMKGYecEdWcWq6W8wFAQQQQAABBBBAAAEEEEAAgfQvQMA4\n/d9jVpiCApn8Yibtly5dWgYNHChvW1nBL73SVZZ8v0h6dO9uZQVPlXaPPyFVq1YxmbzuaVaoUMF6\nCNskGfXf0Wb3E088IW3btBENhH76ySR5peur5mFxelBrC388cbz4+fmJn3sQa1v32e2xx9paD5cL\nljFjx1q1fBeZ3S0efVTefPMNu4s0btLIBIxr1qpp9tWuXcsEj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PFwQQQAABBBBAAAEEEEAAAQQQQACB1CVADePUdT+YTToSyJo1q7Rp3Vqmz5hhAqnffbfIrK5t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OoACHsmIhT1wK5mzZq6CXfcwAupDSB+gkuVKlXk+eefl6xZs1pdcLrsyncIhnPnztWR3kFB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\nC1QsxX2n6wgggAACCCCAAAIIIICAvfDCC/bdd9/Z4Ycfbh07drS33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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The finished Pipeline should look as follows.![Kubeflow Pipeline.png](attachment:0771d77e-2a25-4e2e-9344-f83c4cb2fd14.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict from the trained model\n",
    "\n",
    "Once Kubeflow Pipeline is finished, you are able to call the API endpoint with [mnist image](https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/kubeflow-pipelines/images/9.bmp) to predict from the trained model.\n",
    "\n",
    "**Note**: If you are using Kubeflow + Dex setup and runing this Notebook outside of your Kubernetes cluster, follow [this guide](https://github.com/kubeflow/kfserving/tree/master/docs/samples/istio-dex#authentication) to get Session ID for the API requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run a6b4800d-6b45-43d0-979d-7c2e2dcc38aa has been Succeeded\n",
      "\n",
      "Prediction for the image\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-8-e6f2009e4aa7>:13: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  data = np.array(image.convert('L').resize((28, 28))).astype(np.float).reshape(-1, 28, 28, 1)\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAABwAAAAcCAAAAABXZoBIAAAA1ElEQVR4nN3QPwtBYRQG8EMU0e0uZLIw+QKXRZlMGC0GX8CglE0pk0VxPwQmE5YrJYPVIjYMlImSwXNiMOi97319AM/6O6fzh+g/Y5hr5mrRNByseAZba4D7EnlSN8wy3uAYXJOwDEw0ohKwD9mtxehqRLQBCnZr8GPkJ/Ll79y0m37GiIjiK2AQsGMYiIbryyvjmZO20U9gAIcjTg43GhfethOROToO+En6xRUlZhnSjd+I6BY7xVIRY79w4XapR9IOSTWWYSWUqE0xlH771R7UrULefm5U2pxVCt0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.BmpImagePlugin.BmpImageFile image mode=L size=28x28 at 0x7F9A711F33A0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'predictions': [{'predictions': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0], 'classes': 9}]}\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from PIL import Image\n",
    "import requests\n",
    "\n",
    "# Pipeline Run should be succeeded.\n",
    "kfp_run = kfp_client.get_run(run_id=run_id)\n",
    "if kfp_run.run.status == \"Succeeded\":\n",
    "    print(\"Run {} has been Succeeded\\n\".format(run_id))\n",
    "\n",
    "    # Specify the image URL here.\n",
    "    image_url = \"https://raw.githubusercontent.com/kubeflow/katib/master/examples/v1beta1/kubeflow-pipelines/images/9.bmp\"\n",
    "    image = Image.open(requests.get(image_url, stream=True).raw)\n",
    "    data = np.array(image.convert('L').resize((28, 28))).astype(np.float).reshape(-1, 28, 28, 1)\n",
    "    data_formatted = np.array2string(data, separator=\",\", formatter={\"float\": lambda x: \"%.1f\" % x})\n",
    "    json_request = '{{ \"instances\" : {} }}'.format(data_formatted)\n",
    "\n",
    "    # Specify the prediction URL. If you are runing this notebook outside of Kubernetes cluster, you should set the Cluster IP.\n",
    "    url = \"http://{}-predictor-default.{}.svc.cluster.local/v1/models/{}:predict\".format(name, namespace, name)\n",
    "    response = requests.post(url, data=json_request)\n",
    "\n",
    "    print(\"Prediction for the image\")\n",
    "    display(image)\n",
    "    print(response.json())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
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