Machine learning training using Elastic Fabric Adapter - Amazon EKS
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Machine learning training using Elastic Fabric Adapter


This capability is not available in China Amazon Web Services Regions.

This topic describes how to integrate Elastic Fabric Adapter (EFA) with Pods deployed in your Amazon EKS cluster. Elastic Fabric Adapter (EFA) is a network interface for Amazon EC2 instances that enables you to run applications requiring high levels of inter-node communications at scale on Amazon. Its custom-built operating system bypass hardware interface enhances the performance of inter-instance communications, which is critical to scaling these applications. With EFA, High Performance Computing (HPC) applications using the Message Passing Interface (MPI) and Machine Learning (ML) applications using NVIDIA Collective Communications Library (NCCL) can scale to thousands of CPUs or GPUs. As a result, you get the application performance of on-premises HPC clusters with the on-demand elasticity and flexibility of the Amazon cloud. Integrating EFA with applications running on Amazon EKS clusters can reduce the time to complete large scale distributed training workloads without having to add additional instances to your cluster. For more information about EFA, Elastic Fabric Adapter.

The EFA plugin described in this topic fully supports Amazon EC2 P4d instances, which represent the current state of the art in distributed machine learning in the cloud. Each p4d.24xlarge instance has eight NVIDIA A100 GPUs, and 400 Gbps GPUDirectRDMA over EFA. GPUDirectRDMA enables you to have direct GPU-to-GPU communication across nodes with CPU bypass, increasing collective communication bandwidth and lowering latency. Amazon EKS and EFA integration with P4d instances provides a seamless method to take advantage of the highest performing Amazon EC2 computing instance for distributed machine learning training.

  • An existing Amazon EKS cluster. If you don't have an existing cluster, use one of our Getting started with Amazon EKS guides to create one. Your cluster must be deployed in a VPC that has at least one private subnet with enough available IP addresses to deploy nodes in. The private subnet must have outbound internet access provided by an external device, such as a NAT gateway.

    If you plan to use eksctl to create your node group, eksctl can also create a cluster for you.

  • Version 2.12.3 or later or version 1.27.160 or later of the Amazon Command Line Interface (Amazon CLI) installed and configured on your device or Amazon CloudShell. To check your current version, use aws --version | cut -d / -f2 | cut -d ' ' -f1. Package managers such yum, apt-get, or Homebrew for macOS are often several versions behind the latest version of the Amazon CLI. To install the latest version, see Installing, updating, and uninstalling the Amazon CLI and Quick configuration with aws configure in the Amazon Command Line Interface User Guide. The Amazon CLI version that is installed in Amazon CloudShell might also be several versions behind the latest version. To update it, see Installing Amazon CLI to your home directory in the Amazon CloudShell User Guide.

  • The kubectl command line tool is installed on your device or Amazon CloudShell. The version can be the same as or up to one minor version earlier or later than the Kubernetes version of your cluster. For example, if your cluster version is 1.28, you can use kubectl version 1.27, 1.28, or 1.29 with it. To install or upgrade kubectl, see Installing or updating kubectl.

  • You must have the Amazon VPC CNI plugin for Kubernetes version 1.7.10 or later installed before launching worker nodes that support multiple Elastic Fabric Adapters, such as the p4d.24xlarge. For more information about updating your Amazon VPC CNI plugin for Kubernetes version, see Working with the Amazon VPC CNI plugin for Kubernetes Amazon EKS add-on.

Create node group

The following procedure helps you create a node group with a p4d.24xlarge backed node group with EFA interfaces and GPUDirect RDMA, and run an example NVIDIA Collective Communications Library (NCCL) test for multi-node NCCL Performance using EFAs. The example can be used a template for distributed deep learning training on Amazon EKS using EFAs.

  1. Determine which Amazon EC2 instance types that support EFA are available in the Amazon Web Services Region that you want to deploy nodes in. Replace region-code with the Amazon Web Services Region that you want to deploy your node group in.

    aws ec2 describe-instance-types --region region-code --filters Name=network-info.efa-supported,Values=true \ --query "InstanceTypes[*].[InstanceType]" --output text

    When you deploy nodes, the instance type that you want to deploy must be available in the Amazon Web Services Region that your cluster is in.

  2. Determine which Availability Zones that the instance type that you want to deploy is available in. In this tutorial, the p4d.24xlarge instance type is used and must be returned in the output for the Amazon Web Services Region that you specified in the previous step. When you deploy nodes in a production cluster, replace p4d.24xlarge with any instance type returned in the previous step.

    aws ec2 describe-instance-type-offerings --region region-code --location-type availability-zone --filters Name=instance-type,Values=p4d.24xlarge \ --query 'InstanceTypeOfferings[*].Location' --output text

    An example output is as follows.

    cn-north-1a    cn-north-1c    cn-north-1b

    Note the Availability Zones returned for use in later steps. When you deploy nodes to a cluster, your VPC must have subnets with available IP addresses in one of the Availability Zones returned in the output.

  3. Create a node group using either eksctl or the Amazon CLI and Amazon CloudFormation.


    Version 0.177.0 or later of the eksctl command line tool installed on your device or Amazon CloudShell. To install or update eksctl, see Installation in the eksctl documentation.

    1. Copy the following contents to a file named efa-cluster.yaml. Replace the example values with your own. You can replace p4d.24xlarge with a different instance, but if you do, make sure that the values for availabilityZones are Availability Zones that were returned for the instance type in step 1.

      apiVersion: kind: ClusterConfig metadata: name: my-efa-cluster region: region-code version: "1.XX" iam: withOIDC: true availabilityZones: ["cn-north-1", "cn-north-1c"] managedNodeGroups: - name: my-efa-ng instanceType: p4d.24xlarge minSize: 1 desiredCapacity: 2 maxSize: 3 availabilityZones: ["cn-north-1"] volumeSize: 300 privateNetworking: true efaEnabled: true
    2. Create a managed node group in an existing cluster.

      eksctl create nodegroup -f efa-cluster.yaml

      If you don't have an existing cluster, you can run the following command to create a cluster and the node group.

      eksctl create cluster -f efa-cluster.yaml

      Because the instance type used in this example has GPUs, eksctl automatically installs the NVIDIA Kubernetes device plugin on each instance for you.

    Amazon CLI and Amazon CloudFormation

    There are several requirements for EFA networking, including creating an EFA specific security group, creating an Amazon EC2 placement group, and creating a launch template that specifies one or more EFA interfaces, and includes EFA driver installation as part of Amazon EC2 user data. To learn more about EFA requirements, see Get started with EFA and MPI in the Amazon EC2 User Guide for Linux Instances. The following steps create all of this for you. Replace all example values with your own.

    1. Set a few variables used in later steps. Replace all of the example values with your own. Replace my-cluster with the name of your existing cluster. The value for node_group_resources_name is later used to create an Amazon CloudFormation stack. The value for node_group_name is later used to create the node group in your cluster.

      cluster_name="my-cluster" cluster_region="region-code" node_group_resources_name="my-efa-nodegroup-resources" node_group_name="my-efa-nodegroup"
    2. Identify a private subnet in your VPC that is in the same Availability Zone as the instance type that you want to deploy is available in.

      1. Retrieve the version of your cluster and store it in a variable for use in a later step.

        cluster_version=$(aws eks describe-cluster \ --name $cluster_name \ --query "cluster.version" \ --output text)
      2. Retrieve the VPC ID that your cluster is in and store it in a variable for use in a later step.

        vpc_id=$(aws eks describe-cluster \ --name $cluster_name \ --query "cluster.resourcesVpcConfig.vpcId" \ --output text)
      3. Retrieve the ID of the control plane security group for your cluster and store it in a variable for use in a later step.

        control_plane_security_group=$(aws eks describe-cluster \ --name $cluster_name \ --query "cluster.resourcesVpcConfig.clusterSecurityGroupId" \ --output text)
      4. Get the list of subnet IDs in your VPC that are in an Availability Zone returned in step 1.

        aws ec2 describe-subnets \ --filters "Name=vpc-id,Values=$vpc_id" "Name=availability-zone,Values=cn-north-1" \ --query 'Subnets[*].SubnetId' \ --output text

        If no output is returned, try a different Availability Zone returned in step 1. If none of your subnets are in an Availability Zone returned in step 1, then you need to create a subnet in an Availability Zone returned in step 1. If you have no room in your VPC to create another subnet, then you can add a CIDR block to the VPC and create subnets in the new CIDR block, or create a new cluster in a new VPC.

      5. Determine whether the subnet is a private subnet by checking the route table for the subnet.

        aws ec2 describe-route-tables \ --filter Name=association.subnet-id,Values=subnet-0d403852a65210a29 \ --query "RouteTables[].Routes[].GatewayId" \ --output text

        An example output is as follows.


        If the output is local igw-02adc64c1b72722e2, then the subnet is a public subnet. You must select a private subnet in an Availability Zone returned in step 1. Once you've identified a private subnet, note its ID for use in a later step.

      6. Set a variable with the private subnet ID from the previous step for use in later steps.

    3. Download the Amazon CloudFormation template.

      curl -O
    4. Copy the following text to your computer. Replace p4d.24xlarge with an instance type from step 1. Replace subnet-0d403852a65210a29 with the ID of the private subnet that you identified in step 2.b.v. Replace path-to-downloaded-cfn-template with the path to the efa-p4d-managed-nodegroup.yaml that you downloaded in the previous step. Replace your-public-key-name with the name of your public key. Once you've made the replacements, run the modified command.

      aws cloudformation create-stack \ --stack-name ${node_group_resources_name} \ --capabilities CAPABILITY_IAM \ --template-body file://path-to-downloaded-cfn-template \ --parameters \ ParameterKey=ClusterName,ParameterValue=${cluster_name} \ ParameterKey=ClusterControlPlaneSecurityGroup,ParameterValue=${control_plane_security_group} \ ParameterKey=VpcId,ParameterValue=${vpc_id} \ ParameterKey=SubnetId,ParameterValue=${subnet_id} \ ParameterKey=NodeGroupName,ParameterValue=${node_group_name} \ ParameterKey=NodeImageIdSSMParam,ParameterValue=/aws/service/eks/optimized-ami/${cluster_version}/amazon-linux-2-gpu/recommended/image_id \ ParameterKey=KeyName,ParameterValue=your-public-key-name \ ParameterKey=NodeInstanceType,ParameterValue=p4d.24xlarge
    5. Determine when the stack that you deployed in the previous step is deployed.

      aws cloudformation wait stack-create-complete --stack-name $node_group_resources_name

      There is no output from the previous command, but your shell prompt doesn't return until the stack is created.

    6. Create your node group using the resources created by the Amazon CloudFormation stack in the previous step.

      1. Retrieve information from the deployed Amazon CloudFormation stack and store it in variables.

        node_instance_role=$(aws cloudformation describe-stacks \ --stack-name $node_group_resources_name \ --query='Stacks[].Outputs[?OutputKey==`NodeInstanceRole`].OutputValue' \ --output text) launch_template=$(aws cloudformation describe-stacks \ --stack-name $node_group_resources_name \ --query='Stacks[].Outputs[?OutputKey==`LaunchTemplateID`].OutputValue' \ --output text)
      2. Create a managed node group that uses the launch template and node IAM role that were created in the previous step.

        aws eks create-nodegroup \ --cluster-name $cluster_name \ --nodegroup-name $node_group_name \ --node-role $node_instance_role \ --subnets $subnet_id \ --launch-template id=$launch_template,version=1
      3. Confirm that the nodes were created.

        aws eks describe-nodegroup \ --cluster-name ${cluster_name} \ --nodegroup-name ${node_group_name} | jq -r .nodegroup.status

        Don't continue until the status returned from the previous command is ACTIVE. It can take several minutes for the nodes to become ready.

    7. If you chose a GPU instance type, you must deploy the NVIDIA device plugin for Kubernetes. Replace vX.X.X with your desired NVIDIA/k8s-device-plugin version before running the following command.

      kubectl apply -f
  4. Deploy the EFA Kubernetes device plugin.

    The EFA Kubernetes device plugin detects and advertises EFA interfaces as allocatable resources to Kubernetes. An application can consume the extended resource type in a Pod request spec just like CPU and memory. For more information, see Consuming extended resources in the Kubernetes documentation. Once requested, the plugin automatically assigns and mounts an EFA interface to the Pod. Using the device plugin simplifies EFA setup and does not require a Pod to run in privileged mode.

    kubectl apply -f

(Optional) Deploy a sample EFA compatible application

Deploy the Kubeflow MPI Operator

For the NCCL tests you can apply the Kubeflow MPI Operator. The MPI Operator makes it easy to run Allreduce-style distributed training on Kubernetes. For more information, see MPI Operator on GitHub.

kubectl apply -f
Run the multi-node NCCL Performance Test to verify GPUDirectRDMA/EFA

To verify NCCL Performance with GPUDirectRDMA over EFA, run the standard NCCL Performance test. For more information, see the official NCCL-Tests repo on GitHub. You can use the sample Dockerfile that comes with this test already built for both NVIDIA CUDA 11.2 and the latest version of EFA.

Alternately, you can download an Amazon Docker image available from an Amazon ECR repo.


An important consideration required for adopting EFA with Kubernetes is configuring and managing Huge Pages as a resource in the cluster. For more information, see Manage Huge Pages in the Kubernetes documentation. Amazon EC2 instances with the EFA driver installed pre-allocate 5128 2M Huge Pages, which you can request as resources to consume in your job specifications.

Complete the following steps to run a two node NCCL Performance Test. In the example NCCL test job, each worker requests eight GPUs, 5210Mi of hugepages-2Mi, four EFAs, and 8000Mi of memory, which effectively means each worker consumes all the resources of a p4d.24xlarge instance.

  1. Create the NCCL-tests job.

    kubectl apply -f

    An example output is as follows. created

  2. View your running Pods.

    kubectl get pods

    An example output is as follows.

    NAME READY STATUS RESTARTS AGE nccl-tests-efa-launcher-nbql9 0/1 Init:0/1 0 2m49s nccl-tests-efa-worker-0 1/1 Running 0 2m49s nccl-tests-efa-worker-1 1/1 Running 0 2m49s

    The MPI Operator creates a launcher Pod and 2 worker Pods (one on each node).

  3. View the log for the efa-launcher Pod. Replace wzr8j with the value from your output.

    kubectl logs -f nccl-tests-efa-launcher-nbql9

For more examples, see the Amazon EKS EFA samples repository on GitHub.