Machine learning inference using AWS Inferentia - Amazon EKS

Machine learning inference using AWS Inferentia

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, runtime, 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.


  • 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 The eksctl command line utility 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 one of the Amazon EKS optimized accelerated Amazon Linux AMI.


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

    eksctl create cluster \ --name <inferentia> \ --region <region-code> \ --nodegroup-name <ng-inf1> \ --node-type <inf1.2xlarge> \ --nodes <2> \ --nodes-min <1> \ --nodes-max <4> \ --ssh-access \ --ssh-public-key <your-key> \ --with-oidc \ --managed

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

    [9] 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

    Abbreviated output

    NAME READY STATUS RESTARTS AGE ... neuron-device-plugin-daemonset-6djhp 1/1 Running 0 5m neuron-device-plugin-daemonset-hwjsj 1/1 Running 0 5m

(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 SavedModel, please follow the tutorial for creating a Neuron compatible ResNet50 model and upload the resulting SavedModel to S3. ResNet-50 is a popular machine learning model used for image recognition 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 a pre-built inference serving container for TensorFlow provided by AWS Deep Learning Containers. Inside the container is the AWS Neuron Runtime and the TensorFlow Serving application. A complete list of pre-built Deep Learning Containers optimized for Neuron is maintained on GitHub under Available Images. At start-up, the DLC will fetch your model from Amazon S3, launch Neuron TensorFlow Serving with the saved model, and wait for prediction requests.

The number of Neuron devices allocated to your serving application can be adjusted by changing the resource in the deployment yaml. Please note that communication between TensorFlow Serving and the Neuron runtime happens over GRPC, which requires passing the IPC_LOCK capability to the container.

  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 rn50_deployment.yaml with the contents below. Update the region-code and model path to match your desired settings. 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 ResNet50 client script that will be used in a later step for sending prediction requests.

    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: containers: - name: eks-neuron-test image: command: - /usr/local/bin/ args: - --port=8500 - --rest_api_port=9000 - --model_name=resnet50_neuron - --model_base_path=s3://<your-bucket-of-models>/resnet50_neuron/ ports: - containerPort: 8500 - containerPort: 9000 imagePullPolicy: IfNotPresent env: - name: AWS_REGION value: "us-east-1" - name: S3_USE_HTTPS value: "1" - name: S3_VERIFY_SSL value: "0" - name: S3_ENDPOINT value: - name: AWS_LOG_LEVEL value: "3" resources: limits: cpu: 4 memory: 4Gi 1 requests: cpu: "1" memory: 1Gi securityContext: capabilities: add: - IPC_LOCK
  3. Deploy the model.

    kubectl apply -f rn50_deployment.yaml
  4. Create a file named rn50_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 rn50_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 service/eks-neuron-test 8500:8500 &
  2. Create a Python script called with the following content. This script runs inference via gRPC, which is service framework.

    import numpy as np import grpc import tensorflow as tf from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.resnet50 import preprocess_input from tensorflow_serving.apis import predict_pb2 from tensorflow_serving.apis import prediction_service_pb2_grpc from tensorflow.keras.applications.resnet50 import decode_predictions if __name__ == '__main__': channel = grpc.insecure_channel('localhost:8500') stub = prediction_service_pb2_grpc.PredictionServiceStub(channel) img_file = tf.keras.utils.get_file( "./kitten_small.jpg", "") img = image.load_img(img_file, target_size=(224, 224)) img_array = preprocess_input(image.img_to_array(img)[None, ...]) request = predict_pb2.PredictRequest() = 'resnet50_inf1' request.inputs['input'].CopyFrom( tf.make_tensor_proto(img_array, shape=img_array.shape)) result = stub.Predict(request) prediction = tf.make_ndarray(result.outputs['output']) print(decode_predictions(prediction))
  3. Run the script to submit predictions to your service.



    [[(u'n02123045', u'tabby', 0.68817204), (u'n02127052', u'lynx', 0.12701613), (u'n02123159', u'tiger_cat', 0.08736559), (u'n02124075', u'Egyptian_cat', 0.063844085), (u'n02128757', u'snow_leopard', 0.009240591)]]