Deploy the tensorflow helm chart on Azure Kubernetes Service (AKS)

Question

Pulumi · Accepted Answer

To deploy the TensorFlow Helm chart on Azure Kubernetes Service (AKS), we'll go through a few steps. Firstly, we'll create an AKS cluster if you haven't got one set up already. Then, we'll install the Helm chart for TensorFlow onto our AKS cluster. Pulumi's TypeScript programming language is used in this example.

Here is the outline of what you need to do, step by step:

1. Set up a new Pulumi project and select Azure as the cloud provider.
2. Create a new AKS cluster using the `azure-native` package.
3. Install the TensorFlow Helm chart on the AKS cluster using the `kubernetes` package's `helm.v3.Chart` resource.

Let's start by installing the necessary Pulumi packages:

```bash
# Install necessary Pulumi packages
pulumi plugin install resource azure-native 2.11.0
pulumi plugin install resource kubernetes 4.4.0
```

After installing, make sure to create `index.ts` file in your Pulumi project. You can open this file and paste the following TypeScript program:

```typescript
import * as azure from "@pulumi/azure-native";
import * as pulumi from "@pulumi/pulumi";
import * as k8s from "@pulumi/kubernetes";
import * as k8sInputs from "@pulumi/kubernetes/types/input";

// Create an Azure Resource Group
const resourceGroup = new azure.resources.ResourceGroup("resourceGroup");

// Create an AKS cluster
const cluster = new azure.containerservice.ManagedCluster("cluster", {
    resourceGroupName: resourceGroup.name,
    agentPoolProfiles: [{
        count: 1,
        maxPods: 110,
        mode: "System",
        name: "agentpool",
        osDiskSizeGB: 30,
        osType: "Linux",
        vmSize: "Standard_DS2_v2",
    }],
    dnsPrefix: "dnsprefix",
    enableRBAC: true,
    kubernetesVersion: "1.20.9",
    linuxProfile: {
        adminUsername: "testuser",
        ssh: {
            publicKeys: [{
                keyData: "ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQCs... your ssh key ...3E3xkuM your_email@example.com",
            }],
        },
    },
});

// Export the kubeconfig for the cluster
export const kubeconfig = pulumi.
    all([cluster.name, resourceGroup.name]).
    apply(([clusterName, rgName]) => {
        return azure.containerservice.listManagedClusterUserCredentials({
            resourceGroupName: rgName,
            resourceName: clusterName,
        });
    }).
    apply(creds => Buffer.from(creds.kubeconfigs[0].value, 'base64').toString());

// Create a Kubernetes provider instance that uses our kubeconfig
const k8sProvider = new k8s.Provider("k8sProvider", {
    kubeconfig: kubeconfig,
});

// Deploy the TensorFlow Helm chart
const tensorflowChart = new k8s.helm.v3.Chart("tensorflow", {
    chart: "tensorflow",
    version: "2.4.0", // specify the exact chart version you want to deploy
    fetchOpts: {
        repo: "https://helm.tensorflow.org/", // the repository where the chart is located
    },
}, { provider: k8sProvider });

// Export the TensorFlow service endpoint
export const tensorflowEndpoint = tensorflowChart.getResourceProperty("v1/Service", "tensorflow-tensorflow", "status").apply(s => s.loadBalancer?.ingress[0].ip);
```

This program is doing the following:

- Creating a new Azure Resource Group to contain our resources.
- Provisioning a new AKS cluster with a single node.
- Obtaining the `kubeconfig` from the AKS cluster after it's created, which allows us to interact with the cluster using Kubernetes commands.
- Setting up a Kubernetes provider with the `kubeconfig` to be able to deploy Kubernetes resources.
- Deploying the TensorFlow Helm chart into our AKS cluster using the Pulumi Kubernetes provider. Note that you may need to change the `version` to match the latest or preferred chart version from the TensorFlow Helm repository.
- Exporting the IP address of the TensorFlow service once it's deployed so you can easily access it.

To run this program, after you have set up your Pulumi project, use the following commands in your terminal:

```bash
pulumi up
```

Pulumi will perform the deployment according to the plan shown, and upon confirmation, it creates the necessary resources.

This is a simplified workflow that shows the power of Infrastructure as Code using Pulumi with TypeScript. In production scenarios, you would need to consider aspects like configuring persistent storage, managing secrets securely, and scaling the cluster to meet your workload requirements.