Deploying GPU-enabled EKS Nodegroups for Deep Learning

Question

Pulumi · Accepted Answer

To deploy GPU-enabled EKS Nodegroups for deep learning, you'll need to create an Amazon EKS (Elastic Kubernetes Service) cluster and then configure node groups with GPU instances. Below is a program that sets up an EKS cluster and a node group with GPU support in AWS using Pulumi with Python.

Firstly, we're going to define the EKS cluster itself. We're using Pulumi's EKS package, which provides higher-level abstractions that make it easier to define and manage EKS clusters. The `eks.Cluster` class allows us to create an EKS cluster without needing to worry about details such as the Kubernetes version or VPC configuration, as Pulumi will choose sensible defaults or can derive the necessary information from the ambient AWS environment or Pulumi configuration.

Next, we define the node group. We'll set up an EKS-managed node group with GPU support using the `eks.NodeGroup` class. For the node group, we need to specify the instance types that support GPUs; `p2.xlarge` or `p3.2xlarge` are common choices for deep learning. AWS also requires specific AMI types for GPU-enabled instances (`AL2_x86_64_GPU`), so we'll set that as well.

You may also need additional IAM permissions for the node role so that the nodes can interact with AWS services. This can be achieved by attaching policies or creating an IAM role with the required permissions and supplying its ARN as the `nodeRoleArn`.

Besides these core resources, deep learning applications often benefit from additional infrastructure, such as storage for datasets and model checkpoints, or databases for experiment tracking. You'd add additional Pulumi resources for these as needed.

Please make sure you've AWS CLI setup and Pulumi CLI installed and configured with AWS credentials before running this Pulumi program.

Here is the complete Pulumi program to deploy GPU-enabled EKS Nodegroups for deep learning:

```python
import pulumi
import pulumi_eks as eks

# Specify the desired size of the node group
desired_node_group_size = 2

# Create an EKS cluster with default configuration
cluster = eks.Cluster('gpu-cluster')

# Define the node group with GPU support
gpu_node_group = eks.NodeGroup('gpu-node-group',
    cluster=cluster.core,  # Associate with our created cluster
    instance_type='p2.xlarge',  # This is an example, choose based on your needs
    desired_capacity=desired_node_group_size,
    min_size=1,
    max_size=3,
    labels={'ondemand': 'true'},  # Custom labels can be provided
    taints=[{
        'key': 'nvidia.com/gpu',
        'value': 'true',
        'effect': 'NoSchedule',
    }],
    # Specify an AMI type optimized for GPU-enabled instances
    ami_type='AL2_x86_64_GPU'  # AWS's Amazon Linux 2 AMI optimized for GPU instances
)

# The Kubeconfig to access the cluster
pulumi.export('kubeconfig', cluster.kubeconfig)

# The node group output
pulumi.export('nodeGroupName', gpu_node_group.node_group_name)
```

In this program:

- We've declared an EKS cluster with default configurations. This abstracts away a lot of the boilerplate needed when setting up EKS.
- We've then declared a GPU-enabled node group that will enable us to run GPU workloads. The node group is told to use an instance type that is GPU-capable (`p2.xlarge`), and communicates that it has GPU with Kubernetes-specific taints and labels.
- The `desired_capacity`, `min_size`, and `max_size` parameters manage the scaling properties of the node group. The `desired_capacity` is the initial number of nodes that should be started.
- Exporting `kubeconfig` gives you the access configuration for kubectl to interact with your cluster.
- Exporting `nodeGroupName` allows the user to identify the created node group in the AWS EKS console or in any AWS CLI command outputs.

Make sure you adjust the instance type, desired capacity, minimum size, and maximum size based on your specific workload requirements and budget. Additional configurations like storage, networking, or IAM roles can be added according to your needs.

You would run the Pulumi program as follows:

1. Save the code above in a file named `__main__.py`.
2. Run `pulumi up` in the same directory as your file, and Pulumi will execute the code to create the resources.
3. To access the Kubernetes cluster once it is created, use the exported `kubeconfig`.

Before running this in a production setting, you would want to review the AWS pricing for the specific instance types you are using to ensure it fits within your budget. Also, always review IAM permissions for the principle of least privilege.