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Machine Learning Pipelines with Kubeflow and MinIO on Azure

Machine Learning Pipelines with Kubeflow and MinIO on Azure

Machine Learning (ML)  initiatives can push compute and storage infrastructures to their limits. Many DataOps teams rely on a Kubernetes-based hybrid cloud architecture to satisfy compute and object storage requirements for scalability, efficiency, reliability, multi-tenancy, and support for RESTful APIs. DataOps teams have standardized on tools that rely on high-performance S3 API-compatible object storage for their pipelines, training and inference needs.

Kubeflow is the standard machine learning toolkit for Kubernetes and it requires S3 API compatibility. Kubeflow is widely used throughout the data science community, but the requirement for S3 API compatible object storage limits deployment options. How would you run Kubeflow on Azure or GCP when they lack S3 API support for their object storage offerings?

MinIO Kubernetes-native object storage is S3 API compatible so you can run your preferred data science tools on any managed Kubernetes service (Azure Kubernetes Service , Google Kubernetes Engine, Amazon Kubernetes Service) and on any Kubernetes distribution (VMware Tanzu, Red Hat OpenShift, even Minikube.        

In this post we are going to set up a Kubeflow cluster using Azure Kubernetes Service (AKS) using MinIO as the underlying storage for the whole setup and to test it End to End we are going to deploy a pipeline that access its data on MinIO and stores the resulting model there as well. The problem we are going to use is the traditional MNIST challenge, which consists of an Optical Character Recognition (OCR) problem.

Setting up the Kubernetes Cluster

Let's start by setting up the AKS cluster called KubeFlowMinIO with four nodes within a resource group called MinIOKubeFlow.

export RESOURCE_GROUP_NAME=MinIOKubeFlow
export LOCATION=westus

az group create -n $RESOURCE_GROUP_NAME -l $LOCATION


export NAME=KubeFlowMinIO

export AGENT_SIZE=Standard_D4s_v3

export AGENT_COUNT=4


az aks create -g $RESOURCE_GROUP_NAME -n $NAME -s $AGENT_SIZE -c $AGENT_COUNT -l $LOCATION --generate-ssh-keys


This process will take a few minutes, and after that you'll have a working Kubernetes cluster ready to go. You just need to configure your local kubectl with the access for this cluster.

az aks get-credentials -n $NAME -g $RESOURCE_GROUP_NAME

Setting up MinIO

The next step is to set up the MinIO Operator to manage our Object Storage on Azure. We've simplified the management of MinIO on Kubernetes plenty, so there are multiple ways  to install the MinIO operator and you can choose the one that best matches your workflow. For this post we'll use MinIO's krew plugin to set up the MinIO Operator and our object storage.

Download the MinIO Krew plugin.

kubectl krew install minio

Then initialize the operator.

kubectl minio init

Now, let's go into the MinIO Operator UI to create our first Tenant. Enter  the following command to receive a locally accessible endpoint and a token to log in.

kubectl minio proxy -n minio-operator 

The expected output is:

Starting port forward of the Console UI.

To connect open a browser and go to http://localhost:9090

Current JWT to login: eyJhbGciOiJSUzI1NiIsImtpZCI6IkhWclVWMmc2YjNuZlRKcGY1YUxJTTh1Mjd2d3ZKZmh5dzBKaE10cm5QYUUifQ.eyJpc3MiOiJrdWJlcm5ldGVzL3NlcnZpY2VhY2NvdW50Iiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9uYW1lc3BhY2UiOiJtaW5pby1vcGVyYXRvciIsImt1YmVybmV0ZXMuaW8vc2VydmljZWFjY291bnQvc2VjcmV0Lm5hbWUiOiJjb25zb2xlLXNhLXRva2VuLTh2cDRxIiwia3ViZXJuZXRlcy5pby9zZXJ2aWNlYWNjb3VudC9zZXJ2aWNlLWFjY291bnQubmFtZSI6ImNvbnNvbGUtc2EiLCJrdWJlcm5ldGVzLmlvL3NlcnZpY2VhY2NvdW50L3NlcnZpY2UtYWNjb3VudC51aWQiOiI4MDJkMmFlZi02ZTQxLTQyMzctYjIyYS04OGVkNjhhNTFkMWMiLCJzdWIiOiJzeXN0ZW06c2VydmljZWFjY291bnQ6bWluaW8tb3BlcmF0b3I6Y29uc29sZS1zYSJ9.CxaS7Xy6l63Z90FLDL0XV0FB4iYYD93-EZ9lT6dUxHTkaYIwGzuVAOVYKclIAslpJqvANzurnuCQv2DSYuptBokqNyJqBZ_Mdfxk_BD8k9LNvvhH2B75FXJOlLUvO43HZp-vWqiBLHvhWD86KI5YdCqgXq0KB2Yuw03pIeAkGhdo-QN7EnTVt-mu6OniB6q_oSC61wUoToHCZKbq7OLeg2zzwqo9JGCBvghBbiVFzeMTYAQHdad69PsWjBRBlUKbG7v5eNWiVPiV44r0-fUZxdCr-1JEP9e4Ag-8J2GzIU1-yBIc_Yn1ok59HxXwiT-_fmp2tpe2WsArY7Hwzza2qLoVSkITzPX6eMVbGfRdzbcxd396LcQfg8GJn4Rbs1Z4YCRqMK_DpoQqYOFf-pjZ6Oa91GlZpMVSH_6_H4xxBuuobyn3WK7XyuBxJuFcl7KoIKoa4qwi87eUE139RXPOZKsCrMX-YmKxTAixKlGux2U4jRaN2lav6_y-ayUvHt0syEJqu0uhqdPNVxGIWW0sabJJ0sSfQdacmrBY1VazIYsN2NAL1N2QCwmQvvjRlqpEAWPF_uhuVwGtgcDX8-CxRKtfoY-8gn7ujwCKl1GMpyr-nE8p88eMIxEkaXqBia0erRLwUGTHrS2ymGN0Ii85_2wRZmDuCGA9QiQ01r89ZXU

Forwarding from 0.0.0.0:9090 -> 9090

Now let's go into http://localhost:9090 and log in using the suggested token.

After logging in we'll be greeted with an empty list of Tenants, let's create one by clicking on + Create Tenant on the top right.

In order to keep things simple during this setup, we are going to create a tenant called machine-learning-cluster on the default namespace of our cluster. Of course you can change this to any namespace that suits your needs.  Then we will  choose a storage class, and since we are aiming for a high-performance data repository we will use Azure's Managed Premium Storage to get the best performance for our Kubeflow pipelines. After completing these fields, select Advanced. Here is where you can configure advanced features such as Custom Docker Registries, Identity Providers, Encryption and Pod Placement. For now, we are going to click Next until we reach the Security step and turn off TLS so we can complete this guide without needing to setup a domain and an external TLS certificate.

Turn off TLS for this tenant.

Now, we will tell the MinIO Operator how big we want our tenant. I'm going to go for 4 nodes to match our current setup and 1 Terabyte of capacity, but you can adjust this to whatever fits your needs.

The last step is a review of what's going to happen. simply click Create and MinIO does the rest!

Write down the auto generated credentials to access your object storage, we will use these to access the underlying storage.

That's it! You've provisioned a high performance object storage and it took just a few minutes. After another few minutes you'll see the tenant Initialized and it's ready to go.

The Tenant details are where you can update your Object Storage and expand it. We can also see that there's a public IP for our object storage and for managing our object storage. We are not going to use that in this guide, but that's what you could use to start consuming the object storage from outside this cluster.

We are ready to go on the object storage front - we've setup a high performance cluster and now we need to leverage it within our Kubeflow pipelines.

Setting up Kubeflow

To set up Kubeflow on AKS we are going to use the command line utility kfctl which can be downloaded from the kfctl release page. There are binaries for Mac and Linux, but if you are on Windows, you'll have to compile that binary from the source. Just make sure the kfctl binary is in your PATH.

Run the following commands, taken from the Kubeflow on Azure installation documentation.

# Set KF_NAME to the name of your Kubeflow deployment. You also use this
# value as directory name when creating your configuration directory.
# For example, your deployment name can be 'my-kubeflow' or 'kf-test'.
export KF_NAME=my-kubeflow

# Set the path to the base directory where you want to store one or more
# Kubeflow deployments. For example, /opt/.
# Then set the Kubeflow application directory for this deployment.
export BASE_DIR=kubeflowsetup
export KF_DIR=${BASE_DIR}/${KF_NAME}

# Set the configuration file to use when deploying Kubeflow.
# The following configuration installs Istio by default. Comment out
# the Istio components in the config file to skip Istio installation.
# See https://github.com/kubeflow/kubeflow/pull/3663
export CONFIG_URI="https://raw.githubusercontent.com/kubeflow/manifests/v1.2-branch/kfdef/kfctl_k8s_istio.v1.2.0.yaml"

mkdir -p ${KF_DIR}
cd ${KF_DIR}
kfctl apply -V -f ${CONFIG_URI}

This process will take about eight minutes as configured, so grab a cup of coffee and monitor the completion with the following command.

kubectl get all -n kubeflow

Once all pods are running, we are ready to move forward with building a Kubeflow pipeline that leverages MinIO.

Open the Kubeflow dashboard by running the following port-foward command and going to http://localhost:8080.

kubectl port-forward svc/istio-ingressgateway -n istio-system 8080:80

Then complete the Kubeflow setup by creating a machine-learning namespace.

The Kubeflow dashboard opens after we configure a namespace.

Let's set up a Jupyter notebook server and configure it from there. Using the Tensorflow 1.15 image, create a notebook called setup-pipeline.

Once the server is ready, connect to it, and then create a Python 3 notebook called Setup Pipeline.

The final step is to configure your Docker account. Kubeflow will push to Docker every new model you build throughout your pipeline and you may hit the 100 request per hour limit pretty quickly. When you use a Docker account, the limit is raised to 200 requests per hour.

USER=<DOCKERUSER>; PASSWORD=<DOCKERPASSWORD>; echo -n $USER:$PASSWORD | base64 |  xargs echo -n |xargs -0 printf '{
    "auths": {
        "https://index.docker.io/v1/": {
            "auth": "%s"
        }
    }
}\n' > /tmp/config.json && kubectl create --namespace ${NAMESPACE} configmap docker-config --from-file=/tmp/config.json && rm /tmp/config.json

Running a Kubeflow Pipeline

Now back to our Notebook. From here on, we will follow the excellent example for vanilla kubernetes that the Kubeflow team provides. We’ll learn how to submit models to Kubeflow for distributed training, as well as how to deploy  and serve them.

You are going to need a few files for this notebook to work, mainly model.py, k8s_util.py, notebook_setup.py, requirements.txt and Dockerfile.model to build your model, submit it to Kubeflow and then deploy it. Let's start with the following snippet to download those files into our notebook.

import urllib.request
import shutil

file_list = ["https://raw.githubusercontent.com/kubeflow/examples/master/mnist/k8s_util.py","https://raw.githubusercontent.com/kubeflow/examples/master/mnist/Dockerfile.model","https://raw.githubusercontent.com/kubeflow/examples/master/mnist/model.py","https://raw.githubusercontent.com/kubeflow/examples/master/mnist/notebook_setup.py","https://raw.githubusercontent.com/kubeflow/examples/master/mnist/requirements.txt"]

for url in file_list:
    file_name = url.split("/").pop()
    with urllib.request.urlopen(url) as response, open(file_name, 'wb') as out_file:
        shutil.copyfileobj(response, out_file)

Now, let's prepare the namespace and configure our MinIO credentials. For our endpoint we are going to use the internal Kubernetes service name minio.default.svc.cluster.local and for the DOCKER_REGISTRY we will enter our Docker username.

from kubernetes import client as k8s_client
from kubernetes.client import rest as k8s_rest
from kubeflow import fairing  
from kubeflow.fairing import utils as fairing_utils
from kubeflow.fairing.builders import append
from kubeflow.fairing.deployers import job
from kubeflow.fairing.preprocessors import base as base_preprocessor

DOCKER_REGISTRY = "miniodev"
namespace = fairing_utils.get_current_k8s_namespace()

from kubernetes import client as k8s_client
from kubernetes.client.rest import ApiException

api_client = k8s_client.CoreV1Api()
minio_service_endpoint = "minio.default.svc.cluster.local"

s3_endpoint = minio_service_endpoint
minio_endpoint = "http://"+s3_endpoint
minio_username = "AXNENHDUBB2LU24Y"
minio_key = "GPONOCU0IDQZBMP55TTELR00D4HGFPJK"
minio_region = "us-east-1"

logging.info(f"Running in namespace {namespace}")
logging.info(f"Using docker registry {DOCKER_REGISTRY}")
logging.info(f"Using minio instance with endpoint '{s3_endpoint}'")

Next we’ll prepare the local notebook by installing dependencies and downloading the required data. All of this can be done in a single block but I used the same separate blocks as the example notebook to make it easier for you to follow.

import logging
import os
import uuid
from importlib import reload
import notebook_setup
reload(notebook_setup)
notebook_setup.notebook_setup(platform='none')

import k8s_util
# Force a reload of kubeflow; since kubeflow is a multi namespace module
# it looks like doing this in notebook_setup may not be sufficient
import kubeflow
reload(kubeflow)
from kubernetes import client as k8s_client
from kubernetes import config as k8s_config
from kubeflow.tfjob.api import tf_job_client as tf_job_client_module
from IPython.core.display import display, HTML
import yaml

# TODO(https://github.com/kubeflow/fairing/issues/426): We should get rid of this once the default
# Kaniko image is updated to a newer image than 0.7.0.
from kubeflow.fairing import constants
constants.constants.KANIKO_IMAGE = "gcr.io/kaniko-project/executor:v0.14.0"

from kubeflow.fairing.builders import cluster

# output_map is a map of extra files to add to the notebook.
# It is a map from source location to the location inside the context.
output_map =  {
    "Dockerfile.model": "Dockerfile",
    "model.py": "model.py"
}

preprocessor = base_preprocessor.BasePreProcessor(
    command=["python"], # The base class will set this.
    input_files=[],
    path_prefix="/app", # irrelevant since we aren't preprocessing any files
    output_map=output_map)

preprocessor.preprocess()

# Use a Tensorflow image as the base image
# We use a custom Dockerfile
from kubeflow.fairing.cloud.k8s import MinioUploader
from kubeflow.fairing.builders.cluster.minio_context import MinioContextSource

minio_uploader = MinioUploader(endpoint_url=minio_endpoint, minio_secret=minio_username, minio_secret_key=minio_key, region_name=minio_region)
minio_context_source = MinioContextSource(endpoint_url=minio_endpoint, minio_secret=minio_username, minio_secret_key=minio_key, region_name=minio_region)

cluster_builder = cluster.cluster.ClusterBuilder(registry=DOCKER_REGISTRY,
                                                base_image="", # base_image is set in the Dockerfile
                                                preprocessor=preprocessor,
                                                image_name="mnist",
                                                dockerfile_path="Dockerfile",
                                                context_source=minio_context_source)
cluster_builder.build()
logging.info(f"Built image {cluster_builder.image_tag}")

At this point, you can go to your personal Docker registry and confirm a new Docker image for the MNIST model was created.

The next step is to create a MinIO Bucket.

mnist_bucket = f"{DOCKER_REGISTRY}-mnist"
minio_uploader.create_bucket(mnist_bucket)
logging.info(f"Bucket {mnist_bucket} created or already exists")

Next we simply build a TFJob and Deployments to train our model, inspect it using TensorBoard and finally to serve it, with all the intermediate steps stored on your MinIO Tenant.

Let's start walking through the blocks, keeping in mind that  these are reproduced verbatim from the kubeflow vanilla kubernete example.

train_name = f"mnist-train-{uuid.uuid4().hex[:4]}"
num_ps = 1
num_workers = 2
model_dir = f"s3://{mnist_bucket}/mnist"
export_path = f"s3://{mnist_bucket}/mnist/export"
train_steps = 200
batch_size = 100
learning_rate = .01
image = cluster_builder.image_tag

train_spec = f"""apiVersion: kubeflow.org/v1
kind: TFJob
metadata:
  name: {train_name} 
spec:
  tfReplicaSpecs:
    Ps:
      replicas: {num_ps}
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          serviceAccount: default-editor
          containers:
          - name: tensorflow
            command:
            - python
            - /opt/model.py
            - --tf-model-dir={model_dir}
            - --tf-export-dir={export_path}
            - --tf-train-steps={train_steps}
            - --tf-batch-size={batch_size}
            - --tf-learning-rate={learning_rate}
            env:
            - name: S3_ENDPOINT
              value: {s3_endpoint}
            - name: AWS_ENDPOINT_URL
              value: {minio_endpoint}
            - name: AWS_REGION
              value: {minio_region}
            - name: BUCKET_NAME
              value: {mnist_bucket}
            - name: S3_USE_HTTPS
              value: "0"
            - name: S3_VERIFY_SSL
              value: "0"
            - name: AWS_ACCESS_KEY_ID
              value: {minio_username}
            - name: AWS_SECRET_ACCESS_KEY
              value: {minio_key}
            image: {image}
            workingDir: /opt
          restartPolicy: OnFailure
    Chief:
      replicas: 1
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          serviceAccount: default-editor
          containers:
          - name: tensorflow
            command:
            - python
            - /opt/model.py
            - --tf-model-dir={model_dir}
            - --tf-export-dir={export_path}
            - --tf-train-steps={train_steps}
            - --tf-batch-size={batch_size}
            - --tf-learning-rate={learning_rate}
            env:
            - name: S3_ENDPOINT
              value: {s3_endpoint}
            - name: AWS_ENDPOINT_URL
              value: {minio_endpoint}
            - name: AWS_REGION
              value: {minio_region}
            - name: BUCKET_NAME
              value: {mnist_bucket}
            - name: S3_USE_HTTPS
              value: "0"
            - name: S3_VERIFY_SSL
              value: "0"
            - name: AWS_ACCESS_KEY_ID
              value: {minio_username}
            - name: AWS_SECRET_ACCESS_KEY
              value: {minio_key}
            image: {image}
            workingDir: /opt
          restartPolicy: OnFailure
    Worker:
      replicas: 1
      template:
        metadata:
          annotations:
            sidecar.istio.io/inject: "false"
        spec:
          serviceAccount: default-editor
          containers:
          - name: tensorflow
            command:
            - python
            - /opt/model.py
            - --tf-model-dir={model_dir}
            - --tf-export-dir={export_path}
            - --tf-train-steps={train_steps}
            - --tf-batch-size={batch_size}
            - --tf-learning-rate={learning_rate}
            env:
            - name: S3_ENDPOINT
              value: {s3_endpoint}
            - name: AWS_ENDPOINT_URL
              value: {minio_endpoint}
            - name: AWS_REGION
              value: {minio_region}
            - name: BUCKET_NAME
              value: {mnist_bucket}
            - name: S3_USE_HTTPS
              value: "0"
            - name: S3_VERIFY_SSL
              value: "0"
            - name: AWS_ACCESS_KEY_ID
              value: {minio_username}
            - name: AWS_SECRET_ACCESS_KEY
              value: {minio_key}
            image: {image}
            workingDir: /opt
          restartPolicy: OnFailure
"""

Next we submit the job via the Kubernetes Python SDK.

tf_job_client = tf_job_client_module.TFJobClient()

tf_job_body = yaml.safe_load(train_spec)
tf_job = tf_job_client.create(tf_job_body, namespace=namespace) 

logging.info(f"Created job {namespace}.{train_name}")

from kubeflow.tfjob import TFJobClient
tfjob_client = TFJobClient()
tfjob_client.wait_for_job(train_name, namespace=namespace, watch=True)

Then we get the logs of the job.

tfjob_client.get_logs(train_name, namespace=namespace)

We’re ready to check the model on MinIO. We can do this via our notebook or through the MinIO Console. First, I’m showing how to do this through a notebook.

from botocore.exceptions import ClientError

try:
    model_response = minio_uploader.client.list_objects(Bucket=mnist_bucket)
    # Minimal check to see if at least the bucket is created
    if model_response["ResponseMetadata"]["HTTPStatusCode"] == 200:
        logging.info(f"{model_dir} found in {mnist_bucket} bucket")
except ClientError as err:
    logging.error(err)

Now I’ll show how to do this using the Operator Console. Go into the Tenant Details In the Operator GUI and click on the console URL.

From here, log in to the MinIO Console, go into the Object Browser and explore the miniodev-mnist bucket where we can see the checkpoints and the model itself.

Let's explore how the training went.  Using TensorBoard, we will create a deployment.

tb_name = "mnist-tensorboard"
tb_deploy = f"""apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: mnist-tensorboard
  name: {tb_name}
  namespace: {namespace}
spec:
  selector:
    matchLabels:
      app: mnist-tensorboard
  template:
    metadata:
      labels:
        app: mnist-tensorboard
        version: v1
    spec:
      serviceAccount: default-editor
      containers:
      - command:
        - /usr/local/bin/tensorboard
        - --logdir={model_dir}
        - --port=80
        image: tensorflow/tensorflow:1.15.2-py3
        env:
        - name: S3_ENDPOINT
          value: {s3_endpoint}
        - name: AWS_ENDPOINT_URL
          value: {minio_endpoint}
        - name: AWS_REGION
          value: {minio_region}
        - name: BUCKET_NAME
          value: {mnist_bucket}
        - name: S3_USE_HTTPS
          value: "0"
        - name: S3_VERIFY_SSL
          value: "0"
        - name: AWS_ACCESS_KEY_ID
          value: {minio_username}
        - name: AWS_SECRET_ACCESS_KEY
          value: {minio_key} 
        name: tensorboard
        ports:
        - containerPort: 80
"""
tb_service = f"""apiVersion: v1
kind: Service
metadata:
  labels:
    app: mnist-tensorboard
  name: {tb_name}
  namespace: {namespace}
spec:
  ports:
  - name: http-tb
    port: 80
    targetPort: 80
  selector:
    app: mnist-tensorboard
  type: ClusterIP
"""

tb_virtual_service = f"""apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: {tb_name}
  namespace: {namespace}
spec:
  gateways:
  - kubeflow/kubeflow-gateway
  hosts:
  - '*'
  http:
  - match:
    - uri:
        prefix: /mnist/{namespace}/tensorboard/
    rewrite:
      uri: /
    route:
    - destination:
        host: {tb_name}.{namespace}.svc.cluster.local
        port:
          number: 80
    timeout: 300s
"""

tb_specs = [tb_deploy, tb_service, tb_virtual_service]

k8s_util.apply_k8s_specs(tb_specs, k8s_util.K8S_CREATE_OR_REPLACE)

Now let's explore the TensorBoard by visiting http://localhost:8080/mnist/machine-learning/tensorboard/

As you can see, the training was short and uneventful, but you learned how to  read training data straight from MinIO.

Lastly, let's deploy this model and play with it a little.

deploy_name = "mnist-model"
model_base_path = export_path

# The web ui defaults to mnist-service so if you change it you will
# need to change it in the UI as well to send predictions to the mode
model_service = "mnist-service"

deploy_spec = f"""apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: mnist
  name: {deploy_name}
  namespace: {namespace}
spec:
  selector:
    matchLabels:
      app: mnist-model
  template:
    metadata:
      # TODO(jlewi): Right now we disable the istio side car because otherwise ISTIO rbac will prevent the
      # UI from sending RPCs to the server. We should create an appropriate ISTIO rbac authorization
      # policy to allow traffic from the UI to the model servier.
      # https://istio.io/docs/concepts/security/#target-selectors
      annotations:       
        sidecar.istio.io/inject: "false"
      labels:
        app: mnist-model
        version: v1
    spec:
      serviceAccount: default-editor
      containers:
      - args:
        - --port=9000
        - --rest_api_port=8500
        - --model_name=mnist
        - --model_base_path={model_base_path}
        command:
        - /usr/bin/tensorflow_model_server
        env:
        - name: modelBasePath
          value: {model_base_path}
        - name: S3_ENDPOINT
          value: {s3_endpoint}
        - name: AWS_ENDPOINT_URL
          value: {minio_endpoint}
        - name: AWS_REGION
          value: {minio_region}
        - name: BUCKET_NAME
          value: {mnist_bucket}
        - name: S3_USE_HTTPS
          value: "0"
        - name: S3_VERIFY_SSL
          value: "0"
        - name: AWS_ACCESS_KEY_ID
          value: {minio_username}
        - name: AWS_SECRET_ACCESS_KEY
          value: {minio_key} 
        image: tensorflow/serving:1.15.0
        imagePullPolicy: IfNotPresent
        livenessProbe:
          initialDelaySeconds: 30
          periodSeconds: 30
          tcpSocket:
            port: 9000
        name: mnist
        ports:
        - containerPort: 9000
        - containerPort: 8500
        resources:
          limits:
            cpu: "4"
            memory: 4Gi
          requests:
            cpu: "1"
            memory: 1Gi
        volumeMounts:
        - mountPath: /var/config/
          name: model-config
      volumes:
      - configMap:
          name: {deploy_name}
        name: model-config
"""

service_spec = f"""apiVersion: v1
kind: Service
metadata:
  annotations:   
    prometheus.io/path: /monitoring/prometheus/metrics
    prometheus.io/port: "8500"
    prometheus.io/scrape: "true"
  labels:
    app: mnist-model
  name: {model_service}
  namespace: {namespace}
spec:
  ports:
  - name: grpc-tf-serving
    port: 9000
    targetPort: 9000
  - name: http-tf-serving
    port: 8500
    targetPort: 8500
  selector:
    app: mnist-model
  type: ClusterIP
"""

monitoring_config = f"""kind: ConfigMap
apiVersion: v1
metadata:
  name: {deploy_name}
  namespace: {namespace}
data:
  monitoring_config.txt: |-
    prometheus_config: {{
      enable: true,
      path: "/monitoring/prometheus/metrics"
    }}
"""

model_specs = [deploy_spec, service_spec, monitoring_config]

k8s_util.apply_k8s_specs(model_specs, k8s_util.K8S_CREATE_OR_REPLACE)

That's it, but the model is not yet being served. Let's deploy a sample UI so we can poke at it.

ui_name = "mnist-ui"
ui_deploy = f"""apiVersion: apps/v1
kind: Deployment
metadata:
  name: {ui_name}
  namespace: {namespace}
spec:
  replicas: 1
  selector:
    matchLabels:
      app: mnist-web-ui
  template:
    metadata:
      labels:
        app: mnist-web-ui
    spec:
      containers:
      - image: gcr.io/kubeflow-examples/mnist/web-ui:v20190112-v0.2-142-g3b38225
        name: web-ui
        ports:
        - containerPort: 5000       
      serviceAccount: default-editor
"""

ui_service = f"""apiVersion: v1
kind: Service
metadata:
  annotations:
  name: {ui_name}
  namespace: {namespace}
spec:
  ports:
  - name: http-mnist-ui
    port: 80
    targetPort: 5000
  selector:
    app: mnist-web-ui
  type: ClusterIP
"""

ui_virtual_service = f"""apiVersion: networking.istio.io/v1alpha3
kind: VirtualService
metadata:
  name: {ui_name}
  namespace: {namespace}
spec:
  gateways:
  - kubeflow/kubeflow-gateway
  hosts:
  - '*'
  http:
  - match:
    - uri:
        prefix: /mnist/{namespace}/ui/
    rewrite:
      uri: /
    route:
    - destination:
        host: {ui_name}.{namespace}.svc.cluster.local
        port:
          number: 80
    timeout: 300s
"""

ui_specs = [ui_deploy, ui_service, ui_virtual_service]

k8s_util.apply_k8s_specs(ui_specs, k8s_util.K8S_CREATE_OR_REPLACE)

Now visit http://localhost:8080/mnist/machine-learning/ui/?ns=machine-learning and you’ll see the nice UI that interacts with the model that is being served straight from MinIO.

MinIO Enables Data Science and DataOps Everywhere

Alright! We reached the end of this large guide that explains how to set up MinIO on Azure Kubernetes Service and then deploy Kubeflow to work with MinIO out of the box. The easiest parts were setting up the building blocks of AKS, MinIO and Kubeflow thanks to their high degree of automation. This frees you to focus on more important tasks such as building your machine learning pipelines to run smoothly on Kubeflow, leveraging large datasets straight from MinIO and storing and deploying the models straight from the object storage as well.


Download MinIO to get started and if you have any questions join our Slack Channel, drop us a note at hello@min.io or use the Ask an Expert button. We are here to help you.

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