Kubernetes-based Distributed Machine Learning on OCI

Question

Pulumi · Accepted Answer

In order to set up a Kubernetes-based distributed machine learning (ML) environment on Oracle Cloud Infrastructure (OCI), we'll use Pulumi to orchestrate the necessary resources. Specifically, we will leverage OCI's Container Engine for Kubernetes (OKE) to manage our Kubernetes cluster, create compute resources for ML workloads, and take advantage of OCI's Data Science service to manage our ML workflows.

Here's a step-by-step guide to what we'll be doing with our Pulumi program:

1. **Create a Kubernetes Cluster**: We'll start by creating an OKE cluster that will orchestrate our containerized machine learning workloads. OKE makes it easy to deploy, manage, and scale Kubernetes clusters.

2. **Setup Node Pools**: Once we have our Kubernetes cluster, we'll need to configure node pools. These are groups of nodes that will run our machine learning containers. We'll set up nodes with the required CPU and memory for our ML tasks.

3. **Configure Data Science Jobs**: We'll utilize OCI's Data Science service to create jobs that define and run our machine learning experiments. This service provides tools and infrastructure that ML teams need to build, train, and manage models.

Below is a Pulumi program in Python that achieves the above steps. This program will lead to the creation of a Kubernetes cluster suitable for distributed ML workloads, with additional OCI resources for managing ML tasks.

```python
import pulumi
import pulumi_oci as oci

# Set up a new OKE Kubernetes cluster
k8s_cluster = oci.containerengine.Cluster("ml-k8s-cluster",
    compartment_id=oci.config.require("compartment_id"), # Specify your compartment ID
    kubernetes_version="v1.21.5", # Specify the version of Kubernetes you want to use
    name="mlCluster",
    options=oci.containerengine.ClusterOptionsArgs(
        service_lb_subnet_ids=[
            oci_core.Subnet.get("lb-subnet-1", "lb-subnet-1").id, # Use appropriate Subnet IDs
            oci_core.Subnet.get("lb-subnet-2", "lb-subnet-2").id,
        ]
    ),
    vcn_id=oci_core.Vcn.get("vcn-id", "vcn-id").id, # Specify the VCN ID for your cluster
)

# Create a node pool for the OKE Kubernetes cluster
node_pool = oci.containerengine.NodePool("ml-node-pool",
    cluster_id=k8s_cluster.id,
    compartment_id=oci.config.require("compartment_id"), # Specify your compartment ID
    kubernetes_version="v1.21.5", # Use the same version as the Kubernetes cluster
    node_shape="VM.Standard2.4", # Choose appropriate VM shape based on your ML workloads
    node_config_details=oci.containerengine.NodePoolNodeConfigDetailsArgs(
        size=3, # Determined by the size of the ML workload
        placement_configs=[oci.containerengine.NodePoolNodeConfigDetailsPlacementConfigArgs(
            availability_domain="documented:availability_domain", # Use the correct availability domain
            subnet_id=oci_core.Subnet.get("node-pool-subnet", "node-pool-subnet").id,
        )],
    ),
    ssh_public_key="ssh-rsa AAAAB3NzaC1yc2...OCIuser", # Use your public SSH key for access
)

# Define a Data Science Job to handle ML tasks
ml_job = oci.datascience.Job("ml-job",
    compartment_id=oci.config.require("compartment_id"), # Specify your compartment ID
    project_id=oci.datascience.Project.get("ml-project", "ml-project").id, # Reference the ML project ID
    job_configuration_details=oci.datascience.JobJobConfigurationDetailsArgs(
        command_line_arguments="--max_epochs 100 --batch_size 32", # Example command line argument you’d use for your ML job
        job_type="DEFAULT",
    ),
    description="Job to run ML training tasks",
    display_name="MachineLearningJob",
)

# Export the Kubernetes cluster's endpoint and the name of the node pool
pulumi.export('kubeconfig', k8s_cluster.kube_config)
pulumi.export('cluster_name', k8s_cluster.name)
pulumi.export('node_pool_name', node_pool.name)
pulumi.export('ml_job_name', ml_job.display_name)

```

### Explanation

**Creating a Kubernetes Cluster**: We create a cluster using `oci.containerengine.Cluster` with the relevant Kubernetes version and attach it to your specified VCN. The `service_lb_subnet_ids` includes subnets that would be used for LoadBalancers, which might be helpful if you wish to expose services externally.

**Setting up Node Pools**: We construct a `NodePool` using the same compartment and Kubernetes version as the cluster. We select an appropriate VM shape for the ML workload and specify the size of the node pool, which represents the number of nodes within the pool.

**Configuring Data Science Jobs**: We utilize the `oci.datascience.Job` resource to create a job within OCI's Data Science service, providing a reference to the ML Project, the compartment, and the job details like command-line arguments used for the ML experiments.

**Exporting Important Information**: We export the cluster's endpoint, node pool name, and machine learning job details to be used for further configuration or connection with CI/CD tools or a local kubeconfig.

Remember to replace placeholder values like `compartment_id`, subnet IDs, availability domains, SSH public key, and others with the actual values from your OCI configuration. The necessary configurations for OCI would need to be set up beforehand using the Pulumi CLI or environment variables.

To run this Pulumi program, you would need to have Pulumi installed and configured to communicate with OCI. You can run `pulumi up` to apply the changes and create the resources. Additionally, you need `pulumi_oci` as part of your program's requirements.