Distributed Training of Machine Learning Models on EKS.

Question

Pulumi · Accepted Answer

Distributed training of machine learning models is a process where the training of a large model is split across multiple machines, or nodes, to speed up the process. In this context, we're going to set up a Kubernetes cluster on AWS using Amazon Elastic Kubernetes Service (EKS) that will allow us to distribute the training of a machine learning model across multiple nodes.

To achieve this, we first need to create an EKS cluster. EKS abstracts away the complexity of setting up a Kubernetes cluster, making it easier to manage and scale. Once the cluster is up, we will need to configure our Kubernetes nodes with the necessary machine learning libraries and tools required for training, such as TensorFlow or PyTorch.

In this Pulumi program, we will create an EKS cluster with the required IAM roles and then we'll configure node groups that will contain the compute instances to run our ML training jobs. The EKS cluster is the foundation, and the node groups are where the distributed training will actually occur.

Here's a Pulumi program that sets up an EKS cluster for distributed training of machine learning models:

```python
import pulumi
import pulumi_aws as aws
from pulumi_aws import iam, eks

# Create an EKS Role for the cluster.
# This role is used by Kubernetes master to create AWS resources such as EC2 instances or EBS volumes.
eks_role = iam.Role("eksRole", assume_role_policy="""{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Principal": {"Service": "eks.amazonaws.com"},
    "Action": "sts:AssumeRole"
  }]
}""")

# Attach the Amazon EKS Cluster Policy to the role created.
eks_policy_attachment = iam.RolePolicyAttachment("eksPolicyAttachment",
    policy_arn="arn:aws:iam::aws:policy/AmazonEKSClusterPolicy",
    role=eks_role.name)

# Create an EKS Node Group Role - This role is used by Kubernetes worker nodes.
node_role = iam.Role("eksNodeGroupRole", assume_role_policy="""{
  "Version": "2012-10-17",
  "Statement": [{
    "Effect": "Allow",
    "Principal": {"Service": "ec2.amazonaws.com"},
    "Action": "sts:AssumeRole"
  }]
}""")

# Attach the Amazon EKS Worker Node Policy to the node role.
worker_role_policy_attachment = iam.RolePolicyAttachment("workerRolePolicyAttachment",
    policy_arn="arn:aws:iam::aws:policy/AmazonEKSWorkerNodePolicy",
    role=node_role.name)

# Attach the Amazon EC2 Container Registry Read Only Policy to the node role.
worker_role_policy_attachment_ecr = iam.RolePolicyAttachment("workerRolePolicyAttachmentEcr",
    policy_arn="arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryReadOnly",
    role=node_role.name)

# Create an EKS cluster.
eks_cluster = eks.Cluster("eksCluster",
    role_arn=eks_role.arn,
    version="1.21",
    vpc_config=eks.ClusterVpcConfigArgs(
        public_access_cidrs=["0.0.0.0/0"],
    ))

# Create a node group for the EKS cluster.
node_group = eks.NodeGroup("eksNodeGroup",
    cluster_name=eks_cluster.name,
    node_group_name="pulumi-eks-nodegroup",
    node_role_arn=node_role.arn,
    subnet_ids=eks_cluster.vpc_config.subnet_ids,
    scaling_config=eks.NodeGroupScalingConfigArgs(
        desired_size=2,
        max_size=3,
        min_size=1,
    ))

# Export the cluster name and kubeconfig.
pulumi.export("cluster_name", eks_cluster.name)
pulumi.export("kubeconfig", eks_cluster.kubeconfig.apply(lambda kc: kc))
```
This program performs the following actions:

1. It creates an IAM role `eksRole` that Kubernetes can assume to create AWS resources.
2. It attaches the necessary AWS policies to the `eksRole` for the EKS cluster to interact with other AWS services.
3. It creates a second IAM role `eksNodeGroupRole` for the EKS worker nodes, and attaches policies that allow the worker nodes to interact with EKS and ECR (Elastic Container Registry).
4. An EKS cluster `eksCluster` is created using the role we just set up.
5. A node group `nodeGroup` is created to hold the worker nodes, and it's configured to autoscale between 1 and 3 instances as needed.
6. Finally, we export the EKS cluster name and the kubeconfig required to interact with the Kubernetes cluster from outside.

With this setup, you can now configure your machine learning environment (like setting up TensorFlow, PyTorch, etc.) as Docker containers and deploy them to the EKS cluster. The training workload can be distributed across the nodes in the node group to leverage parallel processing, which tends to speed up the overall training process.

Please make sure to replace any placeholder or variable with the actual values required for your project, and ensure that you have the appropriate AWS permissions to create these resources.