Scalable ML Workflow Scheduling with Nomad

Question

Pulumi · Accepted Answer

To create a scalable Machine Learning (ML) workflow scheduling with HashiCorp Nomad using Pulumi, you will essentially define a set of Nomad jobs that describe your ML workflows. However, directly managing Nomad jobs through Pulumi might not be possible, as Pulumi does not provide a specific provider for Nomad as of my last update.

Instead, you can use a general-purpose provider like `pulumi_command` to execute the Nomad CLI commands, or you can bootstrap a Kubernetes cluster using Pulumi and run Nomad on top of Kubernetes as a set of pods. Note that running Nomad on Kubernetes would be particularly useful if you want to leverage Kubernetes' features like auto-scaling and self-healing alongside Nomad's workflow management.

Here's a high-level example of using Pulumi to deploy a Kubernetes cluster on a cloud provider and installing Nomad onto it:

```python
import pulumi
import pulumi_aws as aws
import pulumi_kubernetes as k8s

# Step 1: Create a new VPC for our cluster
vpc = aws.ec2.Vpc("vpc", cidr_block="10.0.0.0/16")

# Step 2: Create subnets
subnet = aws.ec2.Subnet("subnet",
                         vpc_id=vpc.id,
                         cidr_block="10.0.1.0/24",
                         availability_zone="us-west-2a")

# Step 3: Create an EKS cluster
eks_cluster = aws.eks.Cluster("eks-cluster",
                              role_arn=eks_role.arn,
                              vpc_config=aws.eks.ClusterVpcConfigArgs(
                                  public_access_cidrs=["0.0.0.0/0"],
                                  subnet_ids=[subnet.id]
                              ))

# Step 4: Set up the Kubeconfig
k8s_config = pulumi.Output.all(eks_cluster.endpoint, eks_cluster.certificate_authority, eks_cluster.name).apply(
    lambda args: """apiVersion: v1
clusters:
- cluster:
    server: {endpoint}
    certificate-authority-data: {ca_data}
  name: k8s
contexts:
- context:
    cluster: k8s
    user: admin
  name: k8s
current-context: k8s
kind: Config
preferences: {{}}
users:
- name: admin
  user:
    exec:
      apiVersion: client.authentication.k8s.io/v1alpha1
      command: aws-iam-authenticator
      args:
        - "token"
        - "-i"
        - "{cluster_name}"
""".format(endpoint=args[0], ca_data=args[1]['data'], cluster_name=args[2])
)

# Step 5: Deploy Nomad onto our cluster
nomad_chart = k8s.helm.v3.Chart("nomad",
                                k8s.helm.v3.ChartArgs(
                                    chart="nomad",
                                    version="0.9.3",  # Use the correct chart version
                                    fetch_opts=k8s.helm.v3.FetchOptsArgs(
                                        repo="https://helm.releases.hashicorp.com",
                                    ),
                                    values={
                                        "replicas": 3,  # We want our Nomad cluster to be highly available
                                        # Configure additional Nomad settings as needed
                                    },
                                ),
                                opts=pulumi.ResourceOptions(provider=k8s.Provider("k8s-provider", kubeconfig=k8s_config))
                                )

# Export the cluster name and kubeconfig
pulumi.export("cluster_name", eks_cluster.name)
pulumi.export("kubeconfig", k8s_config)
```

In this program, we perform the following steps:

1. Define a new VPC (Virtual Private Cloud) to provide an isolated network environment for our EKS (Elastic Kubernetes Service) cluster.
2. Create a subnet within our VPC. Subnets define the IP address range and region where our EKS instances will be located.
3. Deploy an EKS cluster which will serve as the underlying platform for running Nomad. The `eks_cluster` resource defines the cluster configuration, including the VPC subnets it should use.
4. Generate a kubeconfig file which is required to interact with the EKS cluster using `kubectl`.
5. Deploy Nomad to the EKS cluster using the official Nomad Helm chart. The Helm chart manages the deployment of Nomad and sets it up in high-availability mode with three replicas.

Remember to replace placeholder values (like the ARN of the IAM role `eks_role.arn`) with the required actual values.

Finally, we export the cluster name and the kubeconfig content so that we can easily access our running cluster.

This is a basic example to get you started with scheduling ML workflows on a Nomad cluster, but it's not a complete machine learning pipeline. You would need to define your specific Nomad job files based on your ML workloads and submit them to the Nomad server after it's running. If you need specific guidance on integrating ML workflows with Nomad or details on setting up Nomad job files for ML tasks, it would be beneficial to refer to Nomad's own documentation or ML workflow tools that integrate with Nomad.