Inferentia support - Amazon EKS

Inferentia support

This topic describes how to create an Amazon EKS cluster with nodes running Amazon EC2 Inf1 instances and (optionally) deploy a sample application. Amazon EC2 Inf1 instances are powered by AWS Inferentia chips, which are custom built by AWS to provide high performance and lowest cost inference in the cloud. Machine learning models are deployed to containers using AWS Neuron, a specialized software development kit (SDK) consisting of a compiler, run-time, and profiling tools that optimize the machine learning inference performance of Inferentia chips. AWS Neuron supports popular machine learning frameworks such as TensorFlow, PyTorch, and MXNet.


  • Inf1 instances are supported on Amazon EKS clusters running Kubernetes version 1.14 and later.

  • Neuron device logical IDs must be contiguous. If a pod requesting multiple Neuron devices is scheduled on an inf1.6xlarge or inf1.24xlarge instance type (which have more than one Neuron device), that pod will fail to start if the Kubernetes scheduler selects non-contiguous device IDs. For more information, see Device logical IDs must be contiguous on GitHub.

  • Amazon EC2 Inf1 instances are not currently supported with managed node groups.


  • Have eksctl installed on your computer. If you don't have it installed, see Install eksctl for installation instructions.

  • Have kubectl installed on your computer. For more information, see Installing kubectl.

  • (Optional) Have python3 installed on your computer. If you don't have it installed, then see Python downloads for installation instructions.

Create a cluster

To create a cluster with Inf1 Amazon EC2 instance nodes

  1. Create a cluster with Inf1 Amazon EC2 instance nodes. You can replace inf1.2xlarge with any Inf1 instance type. eksctl detects that you are launching a node group with an Inf1 instance type and will start your nodes using the EKS-optimized accelerated AMI.


    You can't use IAM roles for service accounts with TensorFlow Serving.

    eksctl create cluster \ --name inferentia \ --version 1.16 \ --region region-code \ --nodegroup-name ng-inf1 \ --node-type inf1.2xlarge \ --nodes 2 \ --nodes-min 1 \ --nodes-max 4

    Note the value of the following line of the output. It's used in a later (optional) step.

    [ℹ] adding identity "arn:aws:iam::111122223333:role/eksctl-inferentia-nodegroup-ng-in-NodeInstanceRole-FI7HIYS3BS09" to auth ConfigMap

    When launching a node group with Inf1 instances, eksctl automatically installs the AWS Neuron Kubernetes device plugin. This plugin advertises Neuron devices as a system resource to the Kubernetes scheduler, which can be requested by a container. In addition to the default Amazon EKS node IAM policies, the Amazon S3 read only access policy is added so that the sample application, covered in a later step, can load a trained model from Amazon S3.

  2. Make sure that all pods have started correctly.

    kubectl get pods -n kube-system


    NAME READY STATUS RESTARTS AGE aws-node-kx2m8 1/1 Running 0 5m aws-node-q57pf 1/1 Running 0 5m coredns-86d5cbb4bd-56dz2 1/1 Running 0 5m coredns-86d5cbb4bd-d6n4z 1/1 Running 0 5m kube-proxy-75zx6 1/1 Running 0 5m kube-proxy-plkfq 1/1 Running 0 5m neuron-device-plugin-daemonset-6djhp 1/1 Running 0 5m neuron-device-plugin-daemonset-hwjsj 1/1 Running 0 5m

(Optional) Create a Neuron TensorFlow Serving application image


Neuron will soon be available pre-installed in AWS Deep Learning Containers. For updates, check AWS Neuron.

  1. Create an Amazon ECR repository to store your application image.

    aws ecr create-repository --repository-name tensorflow-model-server-neuron

    Note the repositoryUri returned in the output for use in a later step.

  2. Create a Dockerfile named with the following contents. The Dockerfile contains the commands to build a Neuron optimized TensorFlow Serving application image. Neuron TensorFlow Serving uses the same API as normal TensorFlow Serving. The only differences are that the saved model must be compiled for Inferentia and the entry point is a different binary.

    FROM amazonlinux:2 RUN yum install -y awscli RUN echo $'[neuron] \n\ name=Neuron YUM Repository \n\ baseurl= \n\ enabled=1' > /etc/yum.repos.d/neuron.repo RUN rpm --import RUN yum install -y tensorflow-model-server-neuron
  3. Log your Docker client into your ECR repository.

    aws ecr get-login-password \ --region region-code \ | docker login \ --username AWS \ --password-stdin
  4. Build the Docker image and upload it to the Amazon ECR repository created in a previous step.

    docker build . -f -t tensorflow-model-server-neuron docker tag tensorflow-model-server-neuron:latest docker push

    If you receive permission related issues from Docker, then you may need to configure Docker for non-root user use. For more information, see Manage Docker as a non-root user in the Docker documentation.

(Optional) Deploy a TensorFlow Serving application image

A trained model must be compiled to an Inferentia target before it can be deployed on Inferentia instances. To continue, you will need a Neuron optimized TensorFlow model saved in Amazon S3. If you don’t already have a saved model, then you can follow the tutorial in the AWS Neuron documentation to create a Neuron compatible BERT-Large model and upload it to S3. BERT is a popular machine learning technique used for understanding natural language tasks. For more information about compiling Neuron models, see The AWS Inferentia Chip With DLAMI in the AWS Deep Learning AMI Developer Guide.

The sample deployment manifest manages two containers: The Neuron runtime container image and the TensorFlow Serving application. For more information about the Neuron container image, see Tutorial: Neuron container tools on GitHub. The Neuron runtime runs as a sidecar container image and is used to interact with the Inferentia chips on your nodes. The two containers communicate over a Unix domain socket placed in a shared mounted volume. At start-up, the application image will fetch your model from Amazon S3, launch Neuron TensorFlow Serving with the saved model, and wait for prediction requests.

The number of Inferentia devices can be adjusted using the resource in the Neuron runtime container specification. The runtime expects 128 2-MB pages per Inferentia device, therefore, hugepages-2Mi has to be set to 256 x the number of Inferentia devices. In order to access Inferentia devices, the Neuron runtime requires SYS_ADMIN and IPC_LOCK capabilities, however, the runtime drops these capabilities at initialization, before opening a gRPC socket.

  1. Add the AmazonS3ReadOnlyAccess IAM policy to the node instance role that was created in step 1 of Create a cluster. This is necessary so that the sample application can load a trained model from Amazon S3.

    aws iam attach-role-policy \ --policy-arn arn:aws:iam::aws:policy/AmazonS3ReadOnlyAccess \ --role-name eksctl-inferentia-nodegroup-ng-in-NodeInstanceRole-FI7HIYS3BS09
  2. Create a file named bert_deployment.yaml with the contents below. Update 111122223333, region-code, and bert/saved_model with your account ID, Region code, and saved model name and location. The model name is for identification purposes when a client makes a request to the TensorFlow server. This example uses a model name to match a sample BERT client script that will be used in a later step for sending prediction requests. You can also replace 1.0.7865.0 with a later version. For the latest version, see Neuron Runtime Release Notes on GitHub or enter the following command.

    aws ecr list-images --repository-name neuron-rtd --registry-id 790709498068 --region us-west-2
    kind: Deployment apiVersion: apps/v1 metadata: name: eks-neuron-test labels: app: eks-neuron-test role: master spec: replicas: 2 selector: matchLabels: app: eks-neuron-test role: master template: metadata: labels: app: eks-neuron-test role: master spec: volumes: - name: sock emptyDir: {} containers: - name: eks-neuron-test image: command: - /usr/local/bin/tensorflow_model_server_neuron args: - --port=9000 - --rest_api_port=8500 - --model_name=bert_mrpc_hc_gelus_b4_l24_0926_02 - --model_base_path=s3://bert/saved_model ports: - containerPort: 8500 - containerPort: 9000 imagePullPolicy: IfNotPresent env: - name: AWS_REGION value: "region-code" - name: S3_USE_HTTPS value: "1" - name: S3_VERIFY_SSL value: "0" - name: AWS_LOG_LEVEL value: "3" - name: NEURON_RTD_ADDRESS value: unix:/sock/neuron.sock resources: limits: cpu: 4 memory: 4Gi requests: cpu: "1" memory: 1Gi volumeMounts: - name: sock mountPath: /sock - name: neuron-rtd image: securityContext: capabilities: add: - SYS_ADMIN - IPC_LOCK volumeMounts: - name: sock mountPath: /sock resources: limits: hugepages-2Mi: 256Mi 1 requests: memory: 1024Mi
  3. Deploy the model.

    kubectl apply -f bert_deployment.yaml
  4. Create a file named bert_service.yaml with the following contents. The HTTP and gRPC ports are opened for accepting prediction requests.

    kind: Service apiVersion: v1 metadata: name: eks-neuron-test labels: app: eks-neuron-test spec: type: ClusterIP ports: - name: http-tf-serving port: 8500 targetPort: 8500 - name: grpc-tf-serving port: 9000 targetPort: 9000 selector: app: eks-neuron-test role: master
  5. Create a Kubernetes service for your TensorFlow model Serving application.

    kubectl apply -f bert_service.yaml

(Optional) Make predictions against your TensorFlow Serving service

  1. To test locally, forward the gRPC port to the eks-neuron-test service.

    kubectl port-forward svc/eks-neuron-test 9000:9000 &
  2. Download the sample BERT client from the Neuron GitHub repository.

    curl >
  3. Run the script to submit predictions to your service.



    ... Inference successful: 0 Inference successful: 1 Inference successful: 2 Inference successful: 3 Inference successful: 4 Inference successful: 5 Inference successful: 6 Inference successful: 7 Inference successful: 8 Inference successful: 9 ... Inference successful: 91 Inference successful: 92 Inference successful: 93 Inference successful: 94 Inference successful: 95 Inference successful: 96 Inference successful: 97 Inference successful: 98 Inference successful: 99 Ran 100 inferences successfully. Latency