Multi-tenant Machine Learning Environments with Node Groups

Question

Pulumi · Accepted Answer

Creating multi-tenant Machine Learning (ML) environments involves setting up infrastructure that can host and isolate different tenants' workloads securely and efficiently. When utilizing Kubernetes for such environments, node groups are a common way to segregate workloads between tenants. Node groups allow you to assign different pods to different groups of nodes, which can be used to enforce separation and manage resources.

Pulumi provides an easy way to define, deploy, and manage Kubernetes clusters and their resources using infrastructure as code. For a multi-tenant ML environment, you will likely need:

- A Kubernetes cluster, to run your ML workloads.
- Node groups within that cluster, which can be managed as separate pools of resources.
- Access controls and network policies, to ensure isolated multi-tenancy.

In the following Pulumi program written in Python, we'll set up the infrastructure for a multi-tenant ML environment using Amazon Elastic Kubernetes Service (EKS) because it provides managed Kubernetes clusters and integrates well with AWS services for machine learning, such as Amazon SageMaker. We will create an EKS cluster and define a `ManagedNodeGroup` as an example of creating node groups. Each node group could be associated with a particular tenant.

For the sake of brevity, this code will set up a simple EKS cluster with a single managed node group. In a production environment, you would extend this to include multiple node groups, possibly with various instance types, sizes, or other properties according to your specific ML workloads' requirements.

```python
import pulumi
import pulumi_eks as eks

# Create an EKS cluster with default settings.
# This will create all the required resources for an EKS cluster like VPC, subnets, and the IAM roles.
cluster = eks.Cluster("ml-cluster")

# Define a managed node group for the EKS cluster. A managed node group is a set of EC2 instances that
# Pulumi will automatically manage for this cluster. These instances will serve as worker nodes,
# where your Kubernetes pods (containers) will be scheduled and run.
node_group = eks.ManagedNodeGroup(
    "ml-node-group",
    cluster=cluster.core,  # The EKS cluster object for which to create the node group.
    node_role=cluster.instance_roles[0],  # Use the instance role associated with the EKS cluster.
    subnet_ids=cluster.core.subnet_ids,  # Use the same subnets as the EKS cluster for the node group.
    scaling_config=eks.ManagedNodeGroupScalingConfigArgs(
        min_size=1,  # Minimum number of nodes in the node group.
        max_size=3,  # Maximum number of nodes in the node group.
        desired_capacity=2  # Desired number of nodes at creation time.
    ),
    instance_types=["t3.medium"],  # Instance types to use for the nodes.
    disk_size=20  # Size in GiB for root block device attached to each instance in the node group.
)

# Export the cluster's kubeconfig and the endpoint. The kubeconfig is needed to access and manage
# the Kubernetes cluster with tools like `kubectl`.
pulumi.export("kubeconfig", cluster.kubeconfig)
pulumi.export("cluster_endpoint", cluster.core.endpoint)
```

In this program:

- `eks.Cluster` creates a new EKS cluster. This is the foundation of the multi-tenant ML environment.
- `eks.ManagedNodeGroup` defines a group of worker nodes that are managed by EKS and are used to run the Kubernetes Pods where the ML workloads will execute.

Once this code is deployed using Pulumi, you will have a basic multi-tenant ML environment. From here, you can extend it by adding more node groups and defining Kubernetes resources such as namespaces, resource quotas, and network policies to enforce separation and resource limitations for each tenant.

You would also need to set up proper authentication and authorization mechanisms for tenants to access only their resources. For example, Kubernetes Role-Based Access Control (RBAC) can be used to control users' access to Kubernetes resources based on their role in the organization.

Finally, for a real-world ML workload, you might need to integrate with data storage services, monitoring and logging solutions, and ML pipeline tools. Pulumi can manage all these additional resources and services as part of extended infrastructure as code.

To apply this configuration, make sure you have Pulumi CLI installed and configured with appropriate AWS credentials. Run `pulumi up` in the directory containing this code to create the resources on AWS.