Automated Machine Learning CI/CD Pipelines with AWX on Kubernetes

Question

Pulumi · Accepted Answer

Creating automated machine learning (ML) CI/CD pipelines involves setting up a series of steps that automate the process of integrating code into a code repository, building, testing and deploying ML models. Ansible AWX (the upstream project of Ansible Tower) can be used for automation by providing a web-based user interface, REST API, and task engine to manage Ansible playbooks.

To deploy AWX on Kubernetes, you can use Pulumi to programmatically define the required cloud resources. The following Pulumi Python program sets up a Kubernetes cluster on AWS using Amazon EKS and deploys AWX on this cluster.

In this program, we'll:

1. Create an EKS cluster with all necessary configurations.
2. Deploy AWX to the cluster, typically done through a Helm Chart.
3. Export any outputs to access AWX once it is deployed, such as URLs or IPs.

Here's how you could implement this:

```python
import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as k8s
from pulumi_aws import eks
from pulumi_kubernetes.helm.v3 import Chart, ChartOpts

# Create an EKS cluster.
eks_cluster = eks.Cluster('eks-cluster',
                          role_arn=aws_iam_role.eks_role.arn,
                          vpc_config=eks.ClusterVpcConfigArgs(
                              public_subnet_ids=[aws_subnet.public_subnet.id],
                          ))

# Create a Kubernetes provider instance that uses our EKS cluster.
k8s_provider = k8s.Provider('k8s-provider',
                            kubeconfig=eks_cluster.kubeconfig)

# Deploy AWX using the Helm Chart.
# Helm chart details should be kept up to date with the official AWX Helm chart repository.
awx_chart = Chart('awx',
                  ChartOpts(chart='awx',
                            version='19.3.0',  # This version might change so use the appropriate chart version
                            namespace='default',
                            fetch_opts=k8s.helm.v3.FetchOpts(
                                repo='https://helm-chart-repo-url/where/awx/chart/is/located'
                            )),
                  opts=pulumi.ResourceOptions(provider=k8s_provider))

# Export the cluster's kubeconfig.
pulumi.export('kubeconfig', eks_cluster.kubeconfig)

# Export the external IP or DNS name of the AWX service to access the AWX UI once it's deployed.
# Assuming it's deployed as a LoadBalancer service. If not, you may want to export other relevant output.
awx_service = awx_chart.get_resource('v1/Service', 'awx-demo-awx-service')
pulumi.export('awx_service_url', awx_service.status.load_balancer.ingress[0].hostname)
```

In the program above, here's what we're doing step by step:

- We use `pulumi_aws.eks.Cluster` to create a new EKS cluster. This includes defining the role ARN from an IAM role suitable for EKS and assigning public subnets for the cluster's VPC configuration.
- We create a Kubernetes provider that uses the kubeconfig of our EKS cluster. This allows Pulumi to deploy Kubernetes resources to our cluster.
- We then use Pulumi's Helm Chart resource to deploy AWX. Make sure to replace `'https://helm-chart-repo-url/where/awx/chart/is/located'` with the actual Helm chart repository URL for AWX.
- We export the kubeconfig so that you can interact with the Kubernetes cluster using `kubectl` or other Kubernetes tools.
- Finally, we export the AWX service URL by fetching the LoadBalancer status from the AWX service resource. This URL will allow access to the AWX UI.

This program provides a basic implementation. Depending on your requirements, you may need additional configurations for resources, storage, authentication, etc. When setting up CI/CD pipelines, you would also need to define jobs and workflows in AWX that reflect your machine learning pipeline stages (data preprocessing, training, evaluation, deployment, etc.).

Keep in mind that connecting this setup to an actual code repository and creating a full CI/CD pipeline would require additional steps and configurations inside AWX, reflecting your own ML workflows and infrastructure.