Orchestrating AI Pipelines on Red Hat OpenShift

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Pulumi · Accepted Answer

Orchestrating AI pipelines on Red Hat OpenShift involves a number of components and steps which usually include setting up the OpenShift cluster, defining the AI workflow, using containerized AI applications, and often a continuous integration and continuous deployment (CI/CD) setup. Pulumi, an infrastructure as code tool, can help in provisioning and managing the required cloud infrastructure in a repeatable and predictable manner.

In the case of Red Hat OpenShift on Azure, you can use Pulumi with the `azure-native` provider to orchestrate your AI pipelines. The key resources that will be a part of this orchestration include:

- `OpenShiftCluster`: Represents an Azure Red Hat OpenShift cluster. This will provide the core Kubernetes platform where your AI applications will run.
- `MachinePool`: Represents a set of compute nodes, often used to run containerized applications.
- Other auxiliary resources may include network profiles, identity providers, and storage, depending on the specifics of your pipeline.

Below is a basic Pulumi program in Python that illustrates how to create a Red Hat OpenShift cluster in Azure:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Red Hat OpenShift cluster
# Replace variables with actual values as needed
RESOURCE_GROUP_NAME = 'myResourceGroup'
CLUSTER_NAME = 'myAIOpenShiftCluster'
LOCATION = 'eastus'

# Provision a resource group if it doesn't exist
resource_group = azure_native.resources.ResourceGroup(RESOURCE_GROUP_NAME,
                                                      resource_group_name=RESOURCE_GROUP_NAME,
                                                      location=LOCATION)

# Provision an Azure Red Hat OpenShift cluster
openshift_cluster = azure_native.redhatopenshift.OpenShiftCluster(CLUSTER_NAME,
    resource_group_name=resource_group.name,
    location=LOCATION,
    resource_name=CLUSTER_NAME,
    tags={"Environment": "Production", "Project": "AI-Pipeline"},
    master_profile={
        "vm_size": "Standard_D8s_v3",  # Choose an appropriate VM size
        "subnet_id": "/subscriptions/.../myMasterSubnet",
    },
    # Define network, worker profiles, and other properties as required
    # ...
)

# To enable exporting key data about the OpenShift cluster
pulumi.export('cluster_name', openshift_cluster.name)
pulumi.export('cluster_url', openshift_cluster.console_profile.apply(lambda profile: profile.url))
```

This example is intentionally minimal; a real-world setup will involve additional details such as configuring network resources, specifying multiple worker profiles for different types of workloads, setting up identity management, and more. The specific types of machine pools, size of the VMs, and other configurations will depend on the resource requirements of the AI applications within the pipeline.

It's crucial to note that with OpenShift and Kubernetes, orchestrating AI pipelines often involves creating and managing additional resources such as container images, storage volumes, and possibly operators or Helm charts to deploy complex applications. These would typically be defined in your application configuration files (like Kubernetes deployment YAML files) rather than directly in Pulumi.

Once the infrastructure is set up with Pulumi, you can use OpenShift's own tooling (like `oc` commands) or Kubernetes' tooling (like `kubectl` and Helm) for deploying and managing your AI workloads.

Please remember that this is a simplified example. Before running Pulumi code to provision infrastructure, especially in a production setting, be sure to understand the cost implications and ensure that you have an appropriate cleanup strategy (like Pulumi destroy) to manage and remove resources when they are no longer needed.