cloud native, knative

Knative Eventing: Part 3 – Replying to broker

In part two of my Knative eventing tutorials, we streamed websocket data to a broker and then subscribed to the events with an event display service that displayed the events in real-time via a web UI.

In this post, I am going to show how to create reply events to a broker and then subscribe to them from a different service. Once we are done, we will have something that should look like the following:

In this scenario, we are receiving events from a streaming source (in this case a websocket) and each time we receive an event, we want to send another event as a reply back to the broker. In this example, we are just going to do this very simply in that every time we get an event, we send another.

In the next tutorial, we will look to receive the events and then perform some analysis on the fly, for example analyse the value of a transaction and assign a size variable. We could then implement some logic like if size = XL, send a reply back to the broker, which could then be listened for by an alerting application.

Setup

I am running Kubernetes on Docker Desktop for mac. You will also need Istio and Knative eventing installed.

Deploy a namespace:

kubectl create namespace knative-eventing-websocket-source

apply the knative-eventing label:

kubectl label namespace knative-eventing-websocket-source knative-eventing-injection=enabled

Ensure you have the cluster local gateway set up.

Adding newEvent logic:

In our application code, we are adding some code for sending a new reply event every time an event is received. The code is here:

newEvent := cloudevents.NewEvent()
newEvent.SetSource(fmt.Sprintf("https://knative.dev/jsaladas/transactionClassified"))
newEvent.SetType("dev.knative.eventing.jsaladas.transaction.classify")
newEvent.SetID("1234")
newEvent.SetData("Hi from Knative!")
response.RespondWith(200, &newEvent)

This code creates a new event, with the following information:

source = "https://knative.dev/jsaladas/transactionClassified"

type = "dev.knative.eventing.jsaladas.transaction.classify"

data = "Hi from Knative!"

I can use whichever values I want for the above, this is just the values I decided on, feel free to change them. We can then use these fields later to filter for the reply events only. Our code will now, aside from receiving an event and displaying it, generate a new event that will enter the knative eventing ecosystem.

Initial components to run the example

The main code for this tutorial is already in the github repo for part 2. If you already followed this and have this running then you will need to redeploy the event-display service with a new image. For those who didn’t join for part 2, this part shows you how to deploy the components.

Deploy the websocket source application:

kubectl apply -f 010-deployment.yaml

Here is the yaml below:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: wseventsource
  namespace: knative-eventing-websocket-source
spec:
  replicas: 1
  selector:
    matchLabels: &labels
      app: wseventsource
  template:
    metadata:
      labels: *labels
    spec:
      containers:
        - name: wseventsource
          image: docker.io/josiemundi/wssourcecloudevents:latest
          env:
          - name: SINK
            value: "http://default-broker.knative-eventing-websocket-source.svc.cluster.local"

Next we will apply the trigger that will set up a subscription for the event display service to subscribe to events from the broker that have a source equal to "wss://ws.blockchain.info/inv". We can also filter on type or even another cloudEvent variable or, if we left them both empty, then it would filter on all events.

kubectl apply -f 040-trigger.yaml

Here is the trigger yaml:

apiVersion: eventing.knative.dev/v1alpha1
kind: Trigger
metadata:
  name: wsevent-trigger
  namespace: knative-eventing-websocket-source
spec:
  broker: default
  filter:
    sourceAndType:
      type: ""
      source: "wss://ws.blockchain.info/inv"
  subscriber:    
    ref:
      apiVersion: v1
      kind: Service
      name: event-display

Next we will deploy the event-display service, which is the specified subscriber of the blockchain events in our trigger.yaml. This application is where we create our reply events.

This is a Kubernetes service so we need to apply the following yaml files:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: event-display
  namespace: knative-eventing-websocket-source
spec:
  replicas: 1
  selector:
    matchLabels: &labels
      app: event-display
  template:
    metadata:
      labels: *labels
    spec:
      containers:
        - name: event-display
          image: docker.io/josiemundi/test-reply-broker
apiVersion: v1
kind: Service
metadata:
  name: event-display
  namespace: knative-eventing-websocket-source
spec:
  type: NodePort
  ports:
  - port: 80
    protocol: TCP
    targetPort: 8080
    name: consumer
  - port: 9080
    protocol: TCP
    targetPort: 9080
    nodePort: 31234
    name: dashboard
  selector:
    app: event-display

If you head to localhost:31234, you should see the stream of events.

Subscribe to the reply events

Now we need to add another trigger, this time subscribing only to the reply events (that’s our newEvent that we set up in the go code). You can see that, in this case, we specify the source as "https://knative.dev/jsaladas/transactionClassified".

Here is the trigger yaml we apply:

apiVersion: eventing.knative.dev/v1alpha1
kind: Trigger
metadata:
  name: reply-trigger-test
  namespace: knative-eventing-websocket-source
spec:
  broker: default
  filter:
    sourceAndType:
      type: ""
      source: "https://knative.dev/jsaladas/transactionClassified"
  subscriber:
    ref:
      apiVersion: serving.knative.dev/v1alpha1
      kind: Service
      name: test-display

This time, our subscriber is a Knative service called test-display, which we still need to deploy.

Run the following to deploy the knative service that subscribes to reply events:

kubectl --namespace knative-eventing-websocket-source apply --filename - << END
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: test-display
spec:
  template:
    spec:
      containers:
        - image: gcr.io/knative-releases/github.com/knative/eventing-contrib/cmd/event_display@sha256:1d6ddc00ab3e43634cd16b342f9663f9739ba09bc037d5dea175dc425a1bb955
END

We can now get the logs of the test-display service and you should only see the reply messages:

kubectl logs -l serving.knative.dev/service=test-display -c user-container --tail=100 -n knative-eventing-websocket-source

Next time we will look at classifying the events and use transaction size as a reply to the broker.

knative, kubernetes

Knative Eventing: Part 2 – streaming CloudEvents to a UI

I’ve been looking at Knative eventing a fair bit lately and one of the things I have been doing is building an eventing demo (the first part of which can be found here). As part of this demo, I wanted to understand how I could get CloudEvents that were being sent by my producer to display in real time via a web UI (event display service UI).

Here is a bit of info and an overview of the approach I took. The code to run through this tutorial can be found here.

Prerequisites and set-up

First, you will need to have Knative and your chosen Gateway provider installed (I tried this with both Istio and Gloo, which both worked fine). You can follow the instructions here.

Initially deploy the 001-namespace.yaml by running:

kubectl apply -f 001-namespace.yaml

Verify you have a broker:

kubectl -n knative-eventing-websocket-source get broker default

You will see that the broker has a URL, this is what we will use as our SINK in the next step.

Deploy the Blockchain Events Sender Application

The application that sends the events was discussed in my Knative Eventing: Part 1 post and you can find the repo with all the code for this application here.

To get up and running you can simply run the 010-deployment.yaml file. Here is a reminder of what it looks like:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: wseventsource
  namespace: knative-eventing-websocket-source
spec:
  replicas: 1
  selector:
    matchLabels: &labels
      app: wseventsource
  template:
    metadata:
      labels: *labels
    spec:
      containers:
        - name: wseventsource
          image: docker.io/josiemundi/wssourcecloudevents:latest
          env:
          - name: SINK
            value: "http://default-broker.knative-eventing-websocket-source.svc.cluster.local"

This is a Kubernetes app deployment. The name of the deployment is wseventsource and the namespace is knative-eventing-websocket-source. We have defined an environmental variable of SINK, for which we set the value as the address of our broker.

Verify events are being sent by running:

kubectl --namespace knative-eventing-websocket-source logs -l app=wseventsource --tail=100 

This is what we currently have deployed:

Add a trigger – Send CloudEvents to Event-Display

Now we can deploy our trigger, which will set our event-display service as the subscriber.

# Knative Eventing Trigger to trigger the helloworld-go service
apiVersion: eventing.knative.dev/v1alpha1
kind: Trigger
metadata:
  name: wsevent-trigger
  namespace: knative-eventing-websocket-source
spec:
  broker: default
  filter:
    sourceAndType:
      type: ""
      source: ""
  subscriber:    
    ref:
      apiVersion: v1
      kind: Service
      name: event-display

In the file above, we define our trigger name as wsevent-trigger and the namespace. In spec > filter I am basically specifying for the broker to send all events to the subscriber. The subscriber in this case is a Kubernetes services rather than a Knative Service.

kubectl apply -f 030-trigger.yaml

Now we have the following:

A trigger can exist before the service and vice versa. Let’s set up our event display.

Stream CloudEvents to Event Display service

I used the following packages to build the Event Display service:

Originally I deployed my event-display application as a Knative Service and this was fine but I could only access the events through the logs or by using curl.

Ideally, I wanted to build a stream of events that was push all the way to the UI. However, I discovered that for this use case it wasn’t possible to deploy this way. This is because Knative serving does not allow multiple ports in a service deployment.

I asked the question about it in the Knative Slack channel and the response was mainly to use mux and specify a path (I saw something similar in the sockeye GitHub project).

In the end, I chose to deploy as a native Kubernetes service instead. The reason is that it seemed like the most applicable way to do this, both in terms of functionality and also security. I was a little unsure about the feasibility of using mux in production as you may not want to expose an internal port externally.

For the kncloudevents project, I struggled to find detailed info or examples but the code is built on top of the Go sdk for CloudEvents and there are some detailed docs for the Python version.

We can use it to listen for HTTP cloudevents requests. By default it will listen on port 8080. When we use the StartReceiver function, this is essentially telling our code to start listening. Because this happens on one port, we need another to ListenAndServe.

So here are the two yaml files that we deploy for the event-display.

App Deployment:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: event-display
  namespace: knative-eventing-websocket-source
spec:
  replicas: 1
  selector:
    matchLabels: &labels
      app: event-display
  template:
    metadata:
      labels: *labels
    spec:
      containers:
        - name: event-display
          image: docker.io/josiemundi/bitcoinfrontendnew

Service Deployment:

apiVersion: v1
kind: Service
metadata:
  name: event-display
  namespace: knative-eventing-websocket-source
spec:
  type: NodePort
  ports:
  - port: 80
    protocol: TCP
    targetPort: 8080
    name: consumer
  - port: 9080
    protocol: TCP
    targetPort: 9080
    nodePort: 31234
    name: dashboard
  selector:
    app: event-display

With everything deployed we now have the following:

Now if you head to the nodeport specified in the yaml:

http://localhost:31234

Next time, we will look at how to send a reply event back into the Knative eventing space.

knative, kubernetes

Step by Step: Deploy and interact with a Knative Service

In this post, I will show how to deploy a Knative service and interact with it through curl and via the browser. I’ll go over some of the useful stuff to know as I found this kind of confusing at first.

I’m running this on a mac using the Kubernetes that’s built in to Docker Desktop, so things will be a bit different if you are running another flavor of Kubernetes. You will need Istio and the Knative serving components installed to follow along with this.

For the service, we are deploying a simple web app example from golang.org, which by default prints out “Hi there, I love (word of your choice)”. The code is at the link above, or I have a simple test image on Docker hub, which just prints out “Hi there, I love test” (oh the lack of creativity!)

Deploying a Knative Service

First we need to create a namespace, in which our Knative service will be deployed. For example:

kubectl create namespace web-service

Here is the Knative service deployment, which is a file called service.yaml.

apiVersion: serving.knative.dev/v1
kind: Service
metadata:
  name: event-display
spec:
  template:
    spec:
      containers:
        - image: docker.io/josiemundi/webserversimple

Deploy the service yaml by running the following command:

kubectl apply -f service.yaml -n web-service

Now run the following in order to view the Knative service and some details we will need:

kubectl get ksvc -n web-service

There are a few fields, including:

NAME: The name of the service

URL: The url of the service, which we will need to interact with it. By default the URL will be “<your-service-name>.<namespace>.example.com” however you can also have a custom domain.

READY: This should say “True”, if not it will say “False” and there will be a reason in the REASON field.

After a little while, you might notice the service will disappear as it scales down to zero. More on that in a while.

IngressGateway

To interact with the service we just deployed, we need to understand a bit about the IngressGateway. By default, Knative uses the istio-ingressgateway as its gateway service. We need to understand this in order to expose our service outside of the local cluster.

We can look at the istio-ingressgateway using the following command:

kubectl get service istio-ingressgateway --namespace istio-system

This will return the following:

Within the gateway configuration, there are a number of ports and NodePorts specified as default including the one we will use to communicate with our service:

port: number: 80, name: http2 protocol: HTTP

To find the port for accessing the service you can run the following:

kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.spec.ports[?(@.name=="http2")].port}'   

You can customise the Gateway configuration. Details and the different ports can be found here in the Istio documentation. I’d also recommend running through the Istio httpbin example to understand a bit more about istio and ingressgateway.

To interact with our service we will need to combine both the URL (event-display.web-service.example.com) and the EXTERNAL-IP (localhost) which we saw for the istio-ingressgateway. Depending on your set up, these will not be the same as mine.

It will be something like the following:

curl -H "Host: event-display.web-service.example.com" http://127.0.0.1/test

Scaling our Service

Your initial pod has probably disappeared right now because when a service is idle, it will scale down to zero after around 90 seconds. You should see the pod start ‘Terminating’ and then disappear.

Knative uses the KPA (Knative Pod Autoscaler), which runs as a Kubernetes deployment. The KPA scales based on requests (concurrency), however it is also possible to use the HPA (Horizontal Pod Autoscaler), which allows scaling based on CPU.

You can find out more detailed information about autoscaling here but for now just note that you can change the parameters in the ConfigMap.

To see the autoscaler config you can run the following command:

kubectl describe configmap config-autoscaler -n knative-serving

To edit the ConfigMap:

kubectl edit configmap config-autoscaler -n knative-serving 

In the result you will see some fields including:

scale-to-zero-grace-period: 30s
stable-window: 60s

The scale-to-zero-grace-period specifies how long it will wait until it scales an inactive service down to zero. The autoscaler takes a 60 second window to assess activity. If it is determined that within that 60 seconds stable-window, there are no events, it will then wait another 30 seconds before scaling to zero. This is why it takes around 90 seconds to terminate an inactive service.

If desired, these can be amended so that your service will scale down faster or slower. There is also a field called enable-scale-to-zero, which (if you want to be able to scale to zero) must be set to “true”.

Test using curl

Once you curl the service again you should see the pod spin up again.

curl -H "Host: event-display.web-service.example.com" http://127.0.0.1:80/test

Should return:

Hi there, I love test!

Access Knative Service through browser

If you are using Docker Desktop on a mac, to access through a browser you could add the host to the hostfile on your mac.

sudo vi /etc/hosts

Add 127.0.0.1 event-display.web-service.example.com to the file and save it.

Alternatively, if you don’t want to (or can’t) change the host file, I used the “Simple Modify Headers” browser plugin. Then click on the icon once installed and select ‘configure’. Input the parameters as follows and then click the start button.

Now open http://localhost/test and you should see:

knative, kubernetes

Knative Eventing Example: Part 1

In my last post I shared some methods for getting up and running with Knative eventing. In this post, we are going to step back a little to try and understand how the eventing component works. It gives a high level overview of a demo you can follow along with on GitHub.

This will be the first of a series of posts, which walk through an example, that we will build out in complexity over the coming weeks.

First, lets go over a few reasons why we might look to use eventing.

What capabilities does Knative Eventing bring?

  • Ability to decouple producers and consumers (this means, for example, that consumers can be subscribed to an event type before any of those event types have been produced).
  • Events are published as CloudEvents (this is a topic I would like to cover separately in more detail).
  • Push-based messaging

There are a number of key components that I will describe below, which together will make up the initial example. Channels and subscriptions will not be included in this post, we’ll discuss those another time.

What are we building?

Let’s first take a look at the diagram below to get a picture of how these components fit and interact together. This diagram shows the type of demo scenario we are looking to recreate over the next few posts.

Each of these components are deployed using yaml files, except for the broker, which is automatically created once the knative injection is enabled within the namespace. You can deploy a custom broker if you wish but I won’t include that in this post.

In this simple example, we use a Kubernetes app deployment as the source and a Knative Service as the consumer, which will subscribe to the events.

The code that I use to stream the events to the broker is available here on GitHub and gives more detailed instructions if you want to build it yourself. It also contains the yaml files used in this tutorial.

Source

Our source is the producer of the events. It could be an application, a web socket, a process etc. It produces events that other services may or may not be interested in subscribing to.

There are a number of different types of sources, each one is a custom resource. The range of sources available can be seen in the Knative documentation here. You can create your own event source if you need to.

The following yaml shows a simple Kubernetes app deployment which is our source:

In the above example, the source is a go application, which streams messages via a web socket connection. It sends them as CloudEvents, which our service will consume. It is available to view here.

Broker and Trigger are CRDs, which will manage the delivery of events and abstract away the details of these from the related services.

Broker

The broker is where events get received. It is like a holding area, from where they can be consumed by those interested. As mentioned above, a default broker is automatically created when you label your namespace with kubectl label namespace my-event-namespace knative-eventing-injection=enabled

Trigger

Our trigger provides a filter, by which it is determines which events should be delivered to a given consumer.

Here below is an example trigger, which just defines that the event-display service subscribes to all events from the default broker.

Under spec, I can also add filter: > attributes: and then include some CloudEvent attributes to filter by. We can then filter the CloudEvent fields, such as type or source etc in order to determine those to which a service subscribes. Here is another example, which filters on a specific event type:

You can also filter on an expression such as:

expression: ce.type == "com.github.pull.create"

Consumer

We can have one or multiple consumers. These are the services that are interested (or not) in the events. If you have a single consumer, you can send straight from the source to consumer.

Here is the Knative Service deployment yaml:

Because I want to send events to a Knative Service, I need to have the cluster-local visibility label (this is explained in more detail below). For the rest, I am using a pre-built image from knative for a simple event display, the code for which can be found here.

Once you have all of these initial components, it looks like this:

Issues

I had some issues with getting the events to the consumer at first when trying this initial demo out. In the end I found out that in order to sink to a Knative Service, you need to add the cluster local gateway to the Istio installation. This is somewhat vaguely mentioned in the docs around installing Istio but could probably have been a bit clearer. Luckily, I found this great post, which helped me massively!

When you install Istio you will need to ensure that you see (at least) the following:

Handy tips and stuff I wish I had known before…

If you want to add a sink URL in your source file, see the example below for a broker sink:

http://default-broker.knative-eventing-websocket-source.svc.cluster.local

default is the name of the broker and knative-eventing-websocket-source is the namespace.

In order to verify events are being sent/received/consumed then you can use the following examples as a reference:

//Getting the logs of the source app
kubectl --namespace knative-eventing-websocket-source logs -l app=wseventsource --tail=100 

//Getting the logs of the broker
kubectl --namespace knative-eventing-websocket-source logs -l eventing.knative.dev/broker=default --tail=100 

//Getting the logs of a Knative service
kubectl logs -l serving.knative.dev/service=event-display -c user-container --since=10m -n knative-eventing-websocket-source

You should see something like this when you get the service logs:

Next steps

Next time we will be looking at building out our service and embellishing it a little so we can transform, visualise and send new events back to a broker.

knative, kubernetes

Up and Running with Knative Eventing on Docker desktop

I’ve been playing around with Knative Eventing and wanted to write my own post on how to get it up and running on a Kubernetes cluster. The docs are pretty straight forward but I always like to keep a record for myself, just so that it’s all in one place.

Hopefully this guide will help someone who is new to the world of Knative eventing get up and running on their local machine.

So let’s get started with our install.

Get Docker Desktop

First you will need to install Docker Desktop. I’m using a Mac so I followed the instructions from the Docker website, they also have for Windows.

Once Docker desktop is installed, go to Preferences > Kubernetes > Enable Kubernetes (this post assumes you have kubectl). Then under ‘Advanced’, you will need to change the settings to increase the resources available:

Install an Ingress controller (Istio)

Knative needs Istio in order to run for it’s ingress controller. You can also use Gloo, but in this example we will use the former.

I followed the instructions from the official Istio site:

Install Istio

You can also install a lighter version of Istio for Knative, for which you can find the instructions here. The installation uses Helm, despite Istio starting to move away from Helm. Also, I needed to remove all the comment lines from the helm template command that makes the istio-lean.yaml file, otherwise it wouldn’t run for me.

Install Knative Serving

Then to install Knative, I first installed Knative serving (as recommended for Docker Desktop users) as per the instructions from their site:

Install Knative Serving

Install Knative Eventing CRDs

After this was installed, I then installed the CRDs for Knative eventing as per the instructions at the link below:

Install the CRDs for Knative Eventing

Check Install

Once you have installed everything, run the following command and you should have something like this:

kubectl get pods --all-namespaces

Create a namespace with Knative Eventing

Now create a test namespace:

kubectl create namespace my-event-namespace

Then we need to add the resources that will be needed from Knative to manage events into the namespace we just created. This is done using the following command:

kubectl label namespace my-event-namespace knative-eventing-injection=enabled

Cleaning Up

Rather than deleting everything, you can just scale your pods down to zero when not in use. This way you can spin them back up when you want to use them again. To do this use the following command:

kubectl get deploy -n <namespace> -o name | xargs -I % kubectl scale % --replicas=0 -n <namespace>

So, for example so scale down any Knative-eventing pods I would use:

kubectl get deploy -n knative-eventing -o name | xargs -I % kubectl scale % --replicas=0 -n knative-eventing

Next steps would be to try out some of the examples listed on the Knative website. In the next few weeks I will be posting some more on using Knative eventing so stay tuned 🙂

kubernetes

AKS (Azure Kubernetes Service) through azure cli

You can deploy AKS using the azure-cli. Here is a quick tutorial on how to do it!

First off, ensure you have the azure-cli installed on your machine. If you have a mac it’s pretty simple. You can just do:

brew install azure-cli

If you are using Windows or Linux then follow the instructions on the Microsoft page.

Login to you Azure account using:

az login

Now we are going to create some variables for location, resource and cluster that we will use to set up our Kubernetes cluster:

export LOCATION=uksouth
export RESOURCE_GROUP=aks-project-group
export CLUSTER_NAME=josiemundi-cluster

Now we create a resource group for our cluster:

az group create --name $RESOURCE_GROUP --location $LOCATION

Now lets deploy our cluster:

az aks create --resource-group $RESOURCE_GROUP \
--name $CLUSTER_NAME \
--generate-ssh-keys \
--node-vm-size Standard_DS2_v2

You can actually head over to your Azure portal and within the resource group we set up, you should now see Kubernetes service. If you click on it, you will see it is in the process of deploying (or maybe already has).

We now need to link with our azure account

az aks get-credentials --resource-group $RESOURCE_GROUP --name $CLUSTER_NAME --admin

Now we can see our deployment with the following command:

kubectl get nodes

Here is a link to a list of the cli commands available for aks.

docker, kubernetes, machine learning in production

Deploying an ML model in Kubernetes

A while back I started looking into how to deploy and scale Machine Learning models. I was recommended the book Machine Learning Logistics by Ted Dunning and Ellen Friedman and started to look into their proposed method of deployment. So far, I have only got to the containerisation and orchestration, however there is still a whole lot more to do 🙂 

I thought I would offer and easy tutorial to get started if you want to try this out. I’m not going to talk about a production ready solution as this would need a fair bit of refinement. 

There are various options for doing this (feel free to let me know what you might be implementing) and this is just one possible approach. I guess the key is to do what fits best with your workflow process. 

All of the code is on GitHub, so if you want to follow along then head there for a more detailed run through (including all code and commands to run etc). I’m not going to put it all in this post as it would be very long 🙂 

For this project I decided to run everything from of the Azure DSVM (Data Science Virtual Machine). However, you can run it locally from your own machine. I ran it from the following spec machine:

Standard B2ms (2 vcpus, 8 GB memory) (Linux)

You will need:

  • Jupyter Notebooks (already on the DSVM)
  • Docker (already on the DSVM)
  • A Docker hub account
  • An Azure account with AKS

Building the model

I won’t go much into the model code but basically I built a simple deep learning model using Keras and the open source wine dataset. The model was created by following this awesome tutorial from DataCamp!

I followed the tutorial step by step and then saved the model. Keras has it’s own save function, which is recommended over using pickle. You need to save both the model and the scaler because we will need it to normalise the data afterwards in the flask app.

Building a Web app using Flask and Containerising it

If you are using the DSVM then under the ‘Networking’ options we need to add another option for the ‘Inbound Port Rules’. Add port 5000. This is the port where our flask app will run. 

For building the docker container, I used this easy to follow ‘Hello Whale’ tutorial by Codefresh as a reference. 

I built a simple flask app, which predicts red or white wine by using some sliders to allocate values to the attributes available in the dataset. As I mentioned, the code for the app is on GitHub. It’s not the prettiest app, feel free to beautify it 🙂 

You will also need to create a Dockerfile and a requirements.txt file (both in the GitHub repo linked above). The Dockerfile contains the commands needed to build the image and the requirements.txt file contains all of the components that your app needs in order to run. 

You will need to make a folder called flask-app and inside place your app.py file, your Dockerfile and your requirements.txt file. 

Navigate via the cli to the flask-app folder and then run the following command:

docker build -t flask-app:latest .

Now to run the container you need to do:

docker run -d -p 5000:5000 flask-app

If you want to stop a docker container then you can use the command:

docker stop <container_name>

Be sure to use the name of the container and not the image name, otherwise it won’t stop. Docker assigns it’s own weird and wonderful names unless you specify otherwise using the –name attribute.

Upload the image to Docker hub

You will need a Docker account to do this. Log in to docker hub using the following command:

docker login --username username

You will then be prompted to enter your password. Then run the following commands to tag and push the image into the repo.

docker tag <your image id> <your docker hub username>/<repo name>

docker push <your docker hub name>/<repo name>

We now have our image available in the Docker hub repo.

Deploying on Kubernetes

For this part I used Azure’s AKS service. It simplifies the Kubernetes process and (for the spec I had) costs a few pounds a day. It has a dashboard UI that is launched in the browser, which lets you easily see your deployments and from there you can do most of the stuff you can do from the cli. 

I set a low spec cluster for Kubernetes:

Standard B2s (2 vcpus, 4 GB memory) and with only 1 node (you can scale it down from the default 3). 

To deploy from the docker hub image. 

Log in to your AKS cluster with the following command:

az aks get-credentials --resource-group <your resource group> --name <your aks cluster>

Pull the image and create a container:

kubectl run wine-app --image=josiemundi/flask-app:latest --port 5000

If you type:

kubectl get pods

You can see the status of your pod. Pods are the smallest unit in Kubernetes and what Kubernetes groups containers in. In this case our container is alone in its pod. It can take a couple of a minutes for a pod to get up and running. 

Expose the app so we get an external ip address for it:

kubectl expose deployment wine-app --type=LoadBalancer --port 80 --target-port 5000

You can check the status of the external ip by using the command:

kubectl get service

This can also take a couple of minutes. Once you have an external ip you can head on over to it and see your app running! 

To delete your deployment use:

kubectl delete deployment <name of deployment>