Demos with ACI
Description: Run a facial recognition demo across your AKS cluster and use ACI and the ACI Connector to burst into for on-demand compute.
Contact ria.bhatia@microsoft.com if you need help!
steps to deploy dis demo & talking tips
Part 1 create resource group
$ az group create --name myResourceGroup --location westus2
deploy aks kub cluster
$ az aks create --resource-group myResourceGroup --name myK8sCluster --node-count 1 --generate-ssh-keys
get creds
$ az aks get-credentials --resource-group=myResourceGroup --name=myK8sCluster
make sure you're connected
$ kubectl get nodes
clone this repo
$ git clone https://github.com/rbitia/aci-demos.git
Make sure you have helm installed and initialize this
$ helm init
edit the values.yaml file and replace with your own hosted domain
$ cd aci-demos/charts/fr-demo/
$ vim values.yaml
install kube-lego w/ helm for certs
helm install stable/kube-lego --name kube-lego --namespace kube-system --set config.LEGO_EMAIL=<your email>,config.LEGO_URL=https://acme-v01.api.letsencrypt.org/directory
install ingress controller w/ helm
helm install stable/nginx-ingress --name ingress --namespace kube-system
^set up can be done before you demo Make sure to grab the Public IP of the ingress controller and place it into your dns zone registration record set.
Start at the top of the aci-demos directory and deploy the Facial Recognition application that consists of a frontend, a backend, and a set of image recognizers.
$ helm install charts/fr-demo --name demo
Checkout the UI that's generated in the output and see the pictures start to get processed Right now you first need to reset the demo at fr-backend. Now checkout the UI which is usually at fr. The rate will be super slow because we have a 1 node AKS cluster running 2 pods.
Deploy the ACI connector :
az aks install-connector --resource-group myResourceGroup --name myK8sCluster --connector-name myaciconnector
The connector has been deployed and with a kubectl get nodes you can see that the ACI Connector is a new node in your cluster. Now scale up the image recognizer to 10 using the following command
$ kubectl scale deploy demo-fr-ir-aci --replicas 10
Though we are using kubectl, the ACI Connector is dispatching pods to Azure Container Instances transparently, via the ACI connector node.
This virtual node has unlimited capacity and a per-second billing model, making it perfect for burst compute scenarios like this one.
If we wait a minute or so for the ACI containers to warm up, we should see image recognizer throughput increase dramatically.
Check out the dashboard to see throughput it dramatically increase...
Here we can see throughput really beginning to pick up, thanks to burst capacity provided by ACI. This is powerful stuff. Here we can see AKS and ACI combine to provide the best of “serverless” computing – invisible infrastructure and micro-billing – all managed through the open Kubernetes APIs. This kind of innovation – the marriage of containers and serverless computing -- is important for the industry, and Microsoft is working hard to make it a reality.
Once you've done all the set up you just need these commands during the live demo:
$ helm install charts/fr-demo --name demo
$ az aks install-connector --resource-group myResourceGroup --name myK8sCluster --connector-name myaciconnector
$ kubectl scale deploy demo-fr-ir-aci --replicas 10
To clean up:
$ helm del --purge demo
$ az aks remove-connector --resource-group myResourceGroup --name myAKSCluster --connector-name myaciconnector