mnist/mnist_ibm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MNIST E2E on Kubeflow on IBM Cloud Kubernetes Service.\n",
    "\n",
    "This example guides you through:\n",
    "  \n",
    "  1. Taking an example TensorFlow model and modifying it to support distributed training\n",
    "  1. Serving the resulting model using TFServing\n",
    "  1. Deploying and using a web-app that uses the model\n",
    "  \n",
    "## Requirements\n",
    "\n",
    "  * You must be [running Kubeflow 1.0 on IBM Cloud Kubernetes Service](https://www.kubeflow.org/docs/ibm/install-kubeflow/).\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Required Services and Credentials\n",
    "\n",
    "Before proceeding to the next steps, we first need to provision the necessary IBM Services and input the credentials below.\n",
    "\n",
    "IBM Cloud Object Storage(COS): https://cloud.ibm.com/catalog/services/cloud-object-storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tip**: follow the steps below to access your COS instance dashboard. From the [IBM Cloud dashboard](https://cloud.ibm.com/resources):\n",
    "\n",
    "- Click the **Storage** tab\n",
    "- Select and click your target object storage (COS)\n",
    "\n",
    "\n",
    "**Create new credentials with HMAC**:\n",
    "\n",
    "  - Go to your COS dashboard (see the above **Tip**).\n",
    "  - In the **Service credentials** tab, click **New Credential+**.\n",
    "  - In the **Add Inline Configuration Parameters(Optional)**: box, add {\"HMAC\":true}\n",
    "  - Click **Add**. (For more information, see HMAC.)\n",
    "  \n",
    "**Replace** the information in the following cell with your COS credentials.\n",
    "\n",
    "You can find these credentials in your COS instance dashboard under the **Service credentials** tab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cos_credentials = {\n",
    "  \"apikey\": \"-------\",\n",
    "  \"cos_hmac_keys\": {\n",
    "    \"access_key_id\": \"------\",\n",
    "    \"secret_access_key\": \"------\"\n",
    "  },\n",
    "  \"endpoints\": \"https://cos-service.bluemix.net/endpoints\",\n",
    "  \"iam_apikey_description\": \"------\",\n",
    "  \"iam_apikey_name\": \"------\",\n",
    "  \"iam_role_crn\": \"------\",\n",
    "  \"iam_serviceid_crn\": \"------\",\n",
    "  \"resource_instance_id\": \"-------\"\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the endpoint.\n",
    "\n",
    "To do this, go to the **Endpoint** tab in the COS instance's dashboard to get the endpoint information, then enter it in the `service_endpoint` cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_endpoint = 's3.us.cloud-object-storage.appdomain.cloud'\n",
    "service_endpoint_with_https=\"https://\" + service_endpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare model\n",
    "\n",
    "There is a delta between existing distributed mnist examples and what's needed to run well as a TFJob.\n",
    "\n",
    "Basically, we must:\n",
    "\n",
    "1. Add options in order to make the model configurable.\n",
    "1. Use `tf.estimator.train_and_evaluate` to enable model exporting and serving.\n",
    "1. Define serving signatures for model serving.\n",
    "\n",
    "The resulting model is [model.py](model.py)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install Required Libraries\n",
    "\n",
    "Import the libraries required to train this model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os\n",
    "import uuid\n",
    "from importlib import reload\n",
    "import notebook_setup\n",
    "reload(notebook_setup)\n",
    "notebook_setup.notebook_setup(platform=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import k8s_util\n",
    "# Force a reload of kubeflow; since kubeflow is a multi namespace module\n",
    "# it looks like doing this in notebook_setup may not be sufficient\n",
    "import kubeflow\n",
    "reload(kubeflow)\n",
    "from kubernetes import client as k8s_client\n",
    "from kubernetes import config as k8s_config\n",
    "from kubeflow.tfjob.api import tf_job_client as tf_job_client_module\n",
    "from IPython.core.display import display, HTML\n",
    "import yaml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure docker credentials\n",
    "\n",
    "Get your docker registry user and password encoded in base64 <br>\n",
    "\n",
    "`echo -n USER:PASSWORD | base64` <br>\n",
    "\n",
    "Update the config auth section below with your Docker registry url and the previous generated base64 string <br>\n",
    "\n",
    "<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "config={\n",
    "    \"auths\": {\n",
    "        \"https://index.docker.io/v1/\": {\n",
    "            \"auth\": \"xxxxxxxxxxxxxxx\"\n",
    "        }\n",
    "    }\n",
    "}\n",
    "with open('config.json', 'w') as outfile:\n",
    "    json.dump(config, outfile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a config-map in the namespace you're using with the docker config\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !kubectl delete configmap docker-config\n",
    "!kubectl create configmap docker-config --from-file=config.json\n",
    "!rm config.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update the `DOCKER_REGISTRY` and build the training image using Kaniko"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kubernetes import client as k8s_client\n",
    "from kubernetes.client import rest as k8s_rest\n",
    "from kubeflow import fairing   \n",
    "from kubeflow.fairing import utils as fairing_utils\n",
    "from kubeflow.fairing.builders import append\n",
    "from kubeflow.fairing.deployers import job\n",
    "from kubeflow.fairing.preprocessors import base as base_preprocessor\n",
    "\n",
    "# Update the DOCKER_REGISTRY to your docker registry!!\n",
    "DOCKER_REGISTRY = \"dockerregistry\"\n",
    "namespace = fairing_utils.get_current_k8s_namespace()\n",
    "\n",
    "cos_username = cos_credentials['cos_hmac_keys']['access_key_id']\n",
    "cos_key = cos_credentials['cos_hmac_keys']['secret_access_key']\n",
    "cos_region = \"us-east-1\"\n",
    "\n",
    "logging.info(f\"Running in namespace {namespace}\")\n",
    "logging.info(f\"Using docker registry {DOCKER_REGISTRY}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO(https://github.com/kubeflow/fairing/issues/426): We should get rid of this once the default \n",
    "# Kaniko image is updated to a newer image than 0.7.0.\n",
    "from kubeflow.fairing import constants\n",
    "constants.constants.KANIKO_IMAGE = \"gcr.io/kaniko-project/executor:v0.14.0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kubeflow.fairing.builders import cluster\n",
    "\n",
    "# output_map is a map of extra files to add to the notebook.\n",
    "# It is a map from source location to the location inside the context.\n",
    "output_map =  {\n",
    "    \"Dockerfile.model\": \"Dockerfile\",\n",
    "    \"model.py\": \"model.py\"\n",
    "}\n",
    "\n",
    "\n",
    "preprocessor = base_preprocessor.BasePreProcessor(\n",
    "    command=[\"python\"], # The base class will set this.\n",
    "    input_files=[],\n",
    "    path_prefix=\"/app\", # irrelevant since we aren't preprocessing any files\n",
    "    output_map=output_map)\n",
    "\n",
    "preprocessor.preprocess()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use a Tensorflow image as the base image\n",
    "# We use a custom Dockerfile \n",
    "from kubeflow.fairing.cloud.k8s import MinioUploader\n",
    "from kubeflow.fairing.builders.cluster.minio_context import MinioContextSource\n",
    "minio_uploader = MinioUploader(endpoint_url=service_endpoint_with_https, minio_secret=cos_username, minio_secret_key=cos_key, region_name=cos_region)\n",
    "minio_context_source = MinioContextSource(endpoint_url=service_endpoint_with_https, minio_secret=cos_username, minio_secret_key=cos_key, region_name=cos_region)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO: Add IBM Container registry as part of the fairing SDK.\n",
    "cluster_builder = cluster.cluster.ClusterBuilder(registry=DOCKER_REGISTRY,\n",
    "                                                 base_image=\"\", # base_image is set in the Dockerfile\n",
    "                                                 preprocessor=preprocessor,\n",
    "                                                 image_name=\"mnist\",\n",
    "                                                 dockerfile_path=\"Dockerfile\",\n",
    "                                                 context_source=minio_context_source)\n",
    "cluster_builder.build()\n",
    "logging.info(f\"Built image {cluster_builder.image_tag}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Object Storage Bucket\n",
    "\n",
    "* Create a object storage bucket to store our models and other results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mnist_bucket = f\"{DOCKER_REGISTRY}-mnist\"\n",
    "minio_uploader.create_bucket(mnist_bucket)\n",
    "logging.info(f\"Bucket {mnist_bucket} created or already exists\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distributed training\n",
    "\n",
    "* We will train the model by using TFJob to run a distributed training job"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training job parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_name = f\"mnist-train-{uuid.uuid4().hex[:4]}\"\n",
    "num_ps = 1\n",
    "num_workers = 2\n",
    "model_dir = f\"s3://{mnist_bucket}/mnist\"\n",
    "export_path = f\"s3://{mnist_bucket}/mnist/export\" \n",
    "train_steps = 200\n",
    "batch_size = 100\n",
    "learning_rate = .01\n",
    "image = cluster_builder.image_tag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_spec = f\"\"\"apiVersion: kubeflow.org/v1\n",
    "kind: TFJob\n",
    "metadata:\n",
    "  name: {train_name}  \n",
    "spec:\n",
    "  tfReplicaSpecs:\n",
    "    Ps:\n",
    "      replicas: {num_ps}\n",
    "      template:\n",
    "        metadata:\n",
    "          annotations:\n",
    "            sidecar.istio.io/inject: \"false\"\n",
    "        spec:\n",
    "          serviceAccount: default-editor\n",
    "          containers:\n",
    "          - name: tensorflow\n",
    "            command:\n",
    "            - python\n",
    "            - /opt/model.py\n",
    "            - --tf-model-dir={model_dir}\n",
    "            - --tf-export-dir={export_path}\n",
    "            - --tf-train-steps={train_steps}\n",
    "            - --tf-batch-size={batch_size}\n",
    "            - --tf-learning-rate={learning_rate}\n",
    "            env:\n",
    "            - name: S3_ENDPOINT\n",
    "              value: {service_endpoint}\n",
    "            - name: AWS_REGION\n",
    "              value: {cos_region}\n",
    "            - name: BUCKET_NAME\n",
    "              value: {mnist_bucket}\n",
    "            - name: S3_USE_HTTPS\n",
    "              value: \"1\"\n",
    "            - name: S3_VERIFY_SSL\n",
    "              value: \"1\"\n",
    "            - name: AWS_ACCESS_KEY_ID\n",
    "              value: {cos_username}\n",
    "            - name: AWS_SECRET_ACCESS_KEY\n",
    "              value: {cos_key}\n",
    "            image: {image}\n",
    "            workingDir: /opt\n",
    "          restartPolicy: OnFailure\n",
    "    Chief:\n",
    "      replicas: 1\n",
    "      template:\n",
    "        metadata:\n",
    "          annotations:\n",
    "            sidecar.istio.io/inject: \"false\"\n",
    "        spec:\n",
    "          serviceAccount: default-editor\n",
    "          containers:\n",
    "          - name: tensorflow\n",
    "            command:\n",
    "            - python\n",
    "            - /opt/model.py\n",
    "            - --tf-model-dir={model_dir}\n",
    "            - --tf-export-dir={export_path}\n",
    "            - --tf-train-steps={train_steps}\n",
    "            - --tf-batch-size={batch_size}\n",
    "            - --tf-learning-rate={learning_rate}\n",
    "            env:\n",
    "            - name: S3_ENDPOINT\n",
    "              value: {service_endpoint}\n",
    "            - name: AWS_REGION\n",
    "              value: {cos_region}\n",
    "            - name: BUCKET_NAME\n",
    "              value: {mnist_bucket}\n",
    "            - name: S3_USE_HTTPS\n",
    "              value: \"1\"\n",
    "            - name: S3_VERIFY_SSL\n",
    "              value: \"1\"\n",
    "            - name: AWS_ACCESS_KEY_ID\n",
    "              value: {cos_username}\n",
    "            - name: AWS_SECRET_ACCESS_KEY\n",
    "              value: {cos_key}\n",
    "            image: {image}\n",
    "            workingDir: /opt\n",
    "          restartPolicy: OnFailure\n",
    "    Worker:\n",
    "      replicas: 1\n",
    "      template:\n",
    "        metadata:\n",
    "          annotations:\n",
    "            sidecar.istio.io/inject: \"false\"\n",
    "        spec:\n",
    "          serviceAccount: default-editor\n",
    "          containers:\n",
    "          - name: tensorflow\n",
    "            command:\n",
    "            - python\n",
    "            - /opt/model.py\n",
    "            - --tf-model-dir={model_dir}\n",
    "            - --tf-export-dir={export_path}\n",
    "            - --tf-train-steps={train_steps}\n",
    "            - --tf-batch-size={batch_size}\n",
    "            - --tf-learning-rate={learning_rate}\n",
    "            env:\n",
    "            - name: S3_ENDPOINT\n",
    "              value: {service_endpoint}\n",
    "            - name: AWS_REGION\n",
    "              value: {cos_region}\n",
    "            - name: BUCKET_NAME\n",
    "              value: {mnist_bucket}\n",
    "            - name: S3_USE_HTTPS\n",
    "              value: \"1\"\n",
    "            - name: S3_VERIFY_SSL\n",
    "              value: \"1\"\n",
    "            - name: AWS_ACCESS_KEY_ID\n",
    "              value: {cos_username}\n",
    "            - name: AWS_SECRET_ACCESS_KEY\n",
    "              value: {cos_key}\n",
    "            image: {image}\n",
    "            workingDir: /opt\n",
    "          restartPolicy: OnFailure\n",
    "\"\"\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the training job\n",
    "\n",
    "* You could write the spec to a YAML file and then do `kubectl apply -f {FILE}`\n",
    "* Since you are running in jupyter you will use the TFJob client\n",
    "* You will run the TFJob in a namespace created by a Kubeflow profile\n",
    "  * The namespace will be the same namespace you are running the notebook in\n",
    "  * Creating a profile ensures the namespace is provisioned with service accounts and other resources needed for Kubeflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_job_client = tf_job_client_module.TFJobClient()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf_job_body = yaml.safe_load(train_spec)\n",
    "tf_job = tf_job_client.create(tf_job_body, namespace=namespace)  \n",
    "\n",
    "logging.info(f\"Created job {namespace}.{train_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kubeflow.tfjob import TFJobClient\n",
    "tfjob_client = TFJobClient()\n",
    "tfjob_client.wait_for_job(train_name, namespace=namespace, watch=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get TF Job logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfjob_client.get_logs(train_name, namespace=namespace)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy Tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tb_name = \"mnist-tensorboard\"\n",
    "tb_deploy = f\"\"\"apiVersion: apps/v1\n",
    "kind: Deployment\n",
    "metadata:\n",
    "  labels:\n",
    "    app: mnist-tensorboard\n",
    "  name: {tb_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  selector:\n",
    "    matchLabels:\n",
    "      app: mnist-tensorboard\n",
    "  template:\n",
    "    metadata:\n",
    "      labels:\n",
    "        app: mnist-tensorboard\n",
    "        version: v1\n",
    "    spec:\n",
    "      serviceAccount: default-editor\n",
    "      containers:\n",
    "      - command:\n",
    "        - /usr/local/bin/tensorboard\n",
    "        - --logdir={model_dir}\n",
    "        - --port=80\n",
    "        image: tensorflow/tensorflow:1.15.2-py3\n",
    "        env:\n",
    "        - name: S3_ENDPOINT\n",
    "          value: {service_endpoint}\n",
    "        - name: AWS_REGION\n",
    "          value: {cos_region}\n",
    "        - name: BUCKET_NAME\n",
    "          value: {mnist_bucket}\n",
    "        - name: S3_USE_HTTPS\n",
    "          value: \"1\"\n",
    "        - name: S3_VERIFY_SSL\n",
    "          value: \"1\"\n",
    "        - name: AWS_ACCESS_KEY_ID\n",
    "          value: {cos_username}\n",
    "        - name: AWS_SECRET_ACCESS_KEY\n",
    "          value: {cos_key}  \n",
    "        name: tensorboard\n",
    "        ports:\n",
    "        - containerPort: 80\n",
    "\"\"\"\n",
    "tb_service = f\"\"\"apiVersion: v1\n",
    "kind: Service\n",
    "metadata:\n",
    "  labels:\n",
    "    app: mnist-tensorboard\n",
    "  name: {tb_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  ports:\n",
    "  - name: http-tb\n",
    "    port: 80\n",
    "    targetPort: 80\n",
    "  selector:\n",
    "    app: mnist-tensorboard\n",
    "  type: ClusterIP\n",
    "\"\"\"\n",
    "\n",
    "tb_virtual_service = f\"\"\"apiVersion: networking.istio.io/v1alpha3\n",
    "kind: VirtualService\n",
    "metadata:\n",
    "  name: {tb_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  gateways:\n",
    "  - kubeflow/kubeflow-gateway\n",
    "  hosts:\n",
    "  - '*'\n",
    "  http:\n",
    "  - match:\n",
    "    - uri:\n",
    "        prefix: /mnist/{namespace}/tensorboard/\n",
    "    rewrite:\n",
    "      uri: /\n",
    "    route:\n",
    "    - destination:\n",
    "        host: {tb_name}.{namespace}.svc.cluster.local\n",
    "        port:\n",
    "          number: 80\n",
    "    timeout: 300s\n",
    "\"\"\"\n",
    "\n",
    "tb_specs = [tb_deploy, tb_service, tb_virtual_service]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k8s_util.apply_k8s_specs(tb_specs, k8s_util.K8S_CREATE_OR_REPLACE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Tensorboard URL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this with the appropriate RBAC permissions <br>\n",
    "**Note:** You can get the node worker ip from `kubectl get no -o wide` <br>\n",
    "```bash\n",
    "export INGRESS_HOST=<worker-node-ip>\n",
    "export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}')\n",
    "printf \"Tensorboard URL: \\n${INGRESS_HOST}:${INGRESS_PORT}/mnist/anonymous/tensorboard/\\n\"\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Serve the model\n",
    "\n",
    "* Deploy the model using tensorflow serving\n",
    "* We need to create\n",
    "  1. A Kubernetes Deployment\n",
    "  1. A Kubernetes service\n",
    "  1. (Optional) Create a configmap containing the prometheus monitoring config"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "deploy_name = \"mnist-model\"\n",
    "model_base_path = export_path\n",
    "\n",
    "# The web ui defaults to mnist-service so if you change it you will\n",
    "# need to change it in the UI as well to send predictions to the mode\n",
    "model_service = \"mnist-service\"\n",
    "\n",
    "deploy_spec = f\"\"\"apiVersion: apps/v1\n",
    "kind: Deployment\n",
    "metadata:\n",
    "  labels:\n",
    "    app: mnist\n",
    "  name: {deploy_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  selector:\n",
    "    matchLabels:\n",
    "      app: mnist-model\n",
    "  template:\n",
    "    metadata:\n",
    "      # TODO(jlewi): Right now we disable the istio side car because otherwise ISTIO rbac will prevent the\n",
    "      # UI from sending RPCs to the server. We should create an appropriate ISTIO rbac authorization\n",
    "      # policy to allow traffic from the UI to the model servier.\n",
    "      # https://istio.io/docs/concepts/security/#target-selectors\n",
    "      annotations:        \n",
    "        sidecar.istio.io/inject: \"false\"\n",
    "      labels:\n",
    "        app: mnist-model\n",
    "        version: v1\n",
    "    spec:\n",
    "      serviceAccount: default-editor\n",
    "      containers:\n",
    "      - args:\n",
    "        - --port=9000\n",
    "        - --rest_api_port=8500\n",
    "        - --model_name=mnist\n",
    "        - --model_base_path={model_base_path}\n",
    "        command:\n",
    "        - /usr/bin/tensorflow_model_server\n",
    "        env:\n",
    "        - name: modelBasePath\n",
    "          value: {model_base_path}\n",
    "        - name: S3_ENDPOINT\n",
    "          value: {service_endpoint}\n",
    "        - name: AWS_REGION\n",
    "          value: {cos_region}\n",
    "        - name: BUCKET_NAME\n",
    "          value: {mnist_bucket}\n",
    "        - name: S3_USE_HTTPS\n",
    "          value: \"1\"\n",
    "        - name: S3_VERIFY_SSL\n",
    "          value: \"1\"\n",
    "        - name: AWS_ACCESS_KEY_ID\n",
    "          value: {cos_username}\n",
    "        - name: AWS_SECRET_ACCESS_KEY\n",
    "          value: {cos_key}  \n",
    "        image: tensorflow/serving:1.15.0\n",
    "        imagePullPolicy: IfNotPresent\n",
    "        livenessProbe:\n",
    "          initialDelaySeconds: 30\n",
    "          periodSeconds: 30\n",
    "          tcpSocket:\n",
    "            port: 9000\n",
    "        name: mnist\n",
    "        ports:\n",
    "        - containerPort: 9000\n",
    "        - containerPort: 8500\n",
    "        resources:\n",
    "          limits:\n",
    "            cpu: \"4\"\n",
    "            memory: 4Gi\n",
    "          requests:\n",
    "            cpu: \"1\"\n",
    "            memory: 1Gi\n",
    "        volumeMounts:\n",
    "        - mountPath: /var/config/\n",
    "          name: model-config\n",
    "      volumes:\n",
    "      - configMap:\n",
    "          name: {deploy_name}\n",
    "        name: model-config\n",
    "\"\"\"\n",
    "\n",
    "service_spec = f\"\"\"apiVersion: v1\n",
    "kind: Service\n",
    "metadata:\n",
    "  annotations:    \n",
    "    prometheus.io/path: /monitoring/prometheus/metrics\n",
    "    prometheus.io/port: \"8500\"\n",
    "    prometheus.io/scrape: \"true\"\n",
    "  labels:\n",
    "    app: mnist-model\n",
    "  name: {model_service}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  ports:\n",
    "  - name: grpc-tf-serving\n",
    "    port: 9000\n",
    "    targetPort: 9000\n",
    "  - name: http-tf-serving\n",
    "    port: 8500\n",
    "    targetPort: 8500\n",
    "  selector:\n",
    "    app: mnist-model\n",
    "  type: ClusterIP\n",
    "\"\"\"\n",
    "\n",
    "monitoring_config = f\"\"\"kind: ConfigMap\n",
    "apiVersion: v1\n",
    "metadata:\n",
    "  name: {deploy_name}\n",
    "  namespace: {namespace}\n",
    "data:\n",
    "  monitoring_config.txt: |-\n",
    "    prometheus_config: {{\n",
    "      enable: true,\n",
    "      path: \"/monitoring/prometheus/metrics\"\n",
    "    }}\n",
    "\"\"\"\n",
    "\n",
    "model_specs = [deploy_spec, service_spec, monitoring_config]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k8s_util.apply_k8s_specs(model_specs, k8s_util.K8S_CREATE_OR_REPLACE)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deploy the mnist UI\n",
    "\n",
    "* We will now deploy the UI to visualize the mnist results\n",
    "* Note: This is using a prebuilt and public docker image for the UI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ui_name = \"mnist-ui\"\n",
    "ui_deploy = f\"\"\"apiVersion: apps/v1\n",
    "kind: Deployment\n",
    "metadata:\n",
    "  name: {ui_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  replicas: 1\n",
    "  selector:\n",
    "    matchLabels:\n",
    "      app: mnist-web-ui\n",
    "  template:\n",
    "    metadata:\n",
    "      labels:\n",
    "        app: mnist-web-ui\n",
    "    spec:\n",
    "      containers:\n",
    "      - image: gcr.io/kubeflow-examples/mnist/web-ui:v20190112-v0.2-142-g3b38225\n",
    "        name: web-ui\n",
    "        ports:\n",
    "        - containerPort: 5000        \n",
    "      serviceAccount: default-editor\n",
    "\"\"\"\n",
    "\n",
    "ui_service = f\"\"\"apiVersion: v1\n",
    "kind: Service\n",
    "metadata:\n",
    "  annotations:\n",
    "  name: {ui_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  ports:\n",
    "  - name: http-mnist-ui\n",
    "    port: 80\n",
    "    targetPort: 5000\n",
    "  selector:\n",
    "    app: mnist-web-ui\n",
    "  type: ClusterIP\n",
    "\"\"\"\n",
    "\n",
    "ui_virtual_service = f\"\"\"apiVersion: networking.istio.io/v1alpha3\n",
    "kind: VirtualService\n",
    "metadata:\n",
    "  name: {ui_name}\n",
    "  namespace: {namespace}\n",
    "spec:\n",
    "  gateways:\n",
    "  - kubeflow/kubeflow-gateway\n",
    "  hosts:\n",
    "  - '*'\n",
    "  http:\n",
    "  - match:\n",
    "    - uri:\n",
    "        prefix: /mnist/{namespace}/ui/\n",
    "    rewrite:\n",
    "      uri: /\n",
    "    route:\n",
    "    - destination:\n",
    "        host: {ui_name}.{namespace}.svc.cluster.local\n",
    "        port:\n",
    "          number: 80\n",
    "    timeout: 300s\n",
    "\"\"\"\n",
    "\n",
    "ui_specs = [ui_deploy, ui_service, ui_virtual_service]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "k8s_util.apply_k8s_specs(ui_specs, k8s_util.K8S_CREATE_OR_REPLACE)     "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Access the  web UI\n",
    "\n",
    "* The endpoint will be\n",
    "\n",
    "Run this with the appropriate RBAC permissions <br>\n",
    "**Note:** You can get the node worker ip from `kubectl get no -o wide` <br>\n",
    "```bash\n",
    "export INGRESS_HOST=<worker-node-ip>\n",
    "export INGRESS_PORT=$(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name==\"http2\")].nodePort}')\n",
    "printf \"mnist-web-app URL: \\n${INGRESS_HOST}:${INGRESS_PORT}/mnist/anonymous/ui/\\n\"\n",
    "```"
   ]
  }
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