Deploying to AWS EC2

AWS EC2 is a great choice for deploying containerized and load balanced services in the cloud. It’s ability to autoscale and automated health checking features make it attractive to users who want to reduce cost and want to horizontally scale base on traffic.

Prerequisites

AWS EC2 deployment and management with BentoML

This guide uses the IrisClassifier BentoService from the Quick start guide:

git clone git@github.com:bentoml/BentoML.git
pip install -r ./bentoml/guides/quick-start/requirements.txt
python ./bentoml/guides/quick-start/main.py

Use bentoml list to get the BentoService name:version tag.

> bentoml list

BentoService                          CREATED_AT                  APIS                        ARTIFACTS
IrisClassifier:20200121141808_FE78B5  2020-01-21 22:18:25.079723  predict(DataframeInput)  model(SklearnModelArtifact)

BentoML has great support for AWS EC2. You can deploy, update and delete deployment with single command, and customize deployment to fit your needs with parameters such as instance type,`scaling capacities`

To deploy BentoService to AWS EC2, use bentoml lambda deploy command. Deployment name and bento service name:version tag is required.

> bentoml ec2 deploy my-first-ec2-deployment -b IrisClassifier:20200121141808_FE78B5

Deploying EC2 deployment -[2020-01-21 14:37:16,838] INFO - Building project
[2020-01-21 14:38:52,826] INFO - Containerzing service
[2020-01-21 14:39:18,834] INFO - Deploying
[2020-01-21 14:40:09,265] INFO - ApplyDeployment (my-first-ec2-deployment, namespace dev) succeeded
Successfully created AWS EC2 deployment my-first-ec2-deployment
{
"namespace": "dev",
"name": "my-first-ec2-deployment",
"spec": {
    "bentoName": "IrisClassifier",
    "bentoVersion": "20200121141808_FE78B5",
    "operator": "AWS_EC2",
    "awsEc2OperatorConfig": {
    "region": "ap-south-1",
    "instanceType": "t2.micro",
    "amiId": "/aws/service/ami-amazon-linux-latest/amzn2-ami-hvm-x86_64-gp2",
    "autoscaleMinCapacity": 1,
    "autoscaleDesiredCapacity": 1,
    "autoscaleMaxCapacity": 1
    }
},
"state": {},
"createdAt": "2020-10-24T06:52:18.003580Z",
"lastUpdatedAt": "2020-10-24T06:52:18.003626Z"
}

BentoML helps you manage the entire process of deploying your BentoService bundle to EC2. Verify the deployed resources with AWS CLI tool:

> aws cloudformation describe-stacks

{
    "Stacks": [
        {
          "StackId": "arn:aws:cloudformation:ap-south-1:752014255238:stack/btml-stack-dev-my-first-ec2-deployment/a9d08770-1d10-11eb-bc31-028b9ab9a492",
          "StackName": "btml-stack-dev-my-first-ec2-deployment",
          "ChangeSetId": "arn:aws:cloudformation:ap-south-1:752014255238:changeSet/samcli-deploy1604324294/ac735ad1-6080-43d2-9e9f-2484563d31c8",
          "Description": "BentoML load balanced template",
          "Parameters": [
              {
                  "ParameterKey": "AmazonLinux2LatestAmiId",
                  "ParameterValue": "/aws/service/ami-amazon-linux-latest/amzn2-ami-hvm-x86_64-gp2",
                  "ResolvedValue": "ami-0e306788ff2473ccb"
              }
          ],
          "CreationTime": "2020-11-02T13:38:17.257000+00:00",
          "LastUpdatedTime": "2020-11-02T13:38:22.926000+00:00",
          "RollbackConfiguration": {},
          "StackStatus": "CREATE_COMPLETE",
          "DisableRollback": false,
          "NotificationARNs": [],
          "Capabilities": [
              "CAPABILITY_IAM"
          ],
          "Outputs": [
              {
                  "OutputKey": "AutoScalingGroup",
                  "OutputValue": "btml-stack-dev-my-first-ec2-deployment-AutoScalingGroup-GTO3DXSAZSWK",
                  "Description": "Autoscaling group name"
              },
              {
                  "OutputKey": "S3Bucket",
                  "OutputValue": "btml-752014255238-dev",
                  "Description": "Bucket to store sam artifacts"
              },
              {
                  "OutputKey": "TargetGroup",
                  "OutputValue": "arn:aws:elasticloadbalancing:ap-south-1:752014255238:targetgroup/btml-Targe-1PBR6D87075CO/b3f6c6296ee51758",
                  "Description": "Target group for load balancer"
              },
              {
                  "OutputKey": "Url",
                  "OutputValue": "http://btml-LoadB-1QA80SD51INOM-516888199.ap-south-1.elb.amazonaws.com",
                  "Description": "URL of the bento service"
              }
          ],
          "Tags": [],
          "DriftInformation": {
              "StackDriftStatus": "NOT_CHECKED"
          }
      },

    ]
}

Tests the deployed service with sample dataset:

> curl -i \
  --header "Content-Type: application/json" \
  --request POST \
  --data '[[5.1, 3.5, 1.4, 0.2]]' \
  https://ps6f0sizt8.execute-api.us-west-2.amazonaws.com/predict

HTTP/1.1 200 OK
Content-Type: application/json
Content-Length: 3
Connection: keep-alive
Date: Tue, 21 Jan 2020 22:43:17 GMT
x-amzn-RequestId: f49d29ed-c09c-4870-b362-4cf493556cf4
x-amz-apigw-id: GrC0AEHYPHcF3aA=
X-Amzn-Trace-Id: Root=1-5e277e7f-e9c0e4c0796bc6f4c36af98c;Sampled=0
X-Cache: Miss from cloudfront
Via: 1.1 bb248e7fabd9781d3ed921f068507334.cloudfront.net (CloudFront)
X-Amz-Cf-Pop: SFO5-C1
X-Amz-Cf-Id: HZzIJUcEUL8aBI0KcmG35rsG-71KSOcLUNmuYR4wdRb6MZupv9IOpA==

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Get the latest state and deployment information such as endpoint and s3 bucket name by using bentoml lambda get command

> bentoml ec2 get my-first-ec2-deployment

{
    "namespace": "dev",
    "name": "deploy-103",
    "spec": {
        "bentoName": "IrisClassifier",
        "bentoVersion": "20201015064204_282D00",
        "operator": "AWS_EC2",
        "awsEc2OperatorConfig": {
        "region": "ap-south-1",
        "instanceType": "t2.micro",
        "amiId": "/aws/service/ami-amazon-linux-latest/amzn2-ami-hvm-x86_64-gp2",
        "autoscaleMinCapacity": 1,
        "autoscaleDesiredCapacity": 1,
        "autoscaleMaxCapacity": 1
        }
    },
    "state": {
        "state": "RUNNING",
        "infoJson": {
        "InstanceDetails": [
            {
            "instance_id": "i-0a8ebeb105e941257",
            "endpoint": "65.0.11.248",
            "state": "InService",
            "health_status": "Healthy"
            }
        ],
        "Endpoints": [
            "65.0.11.248:5000/predict"
        ],
        "S3Bucket": "btml-752014255238-dev",
        "TargetGroup": "arn:aws:elasticloadbalancing:ap-south-1:752014255238:targetgroup/btml-Targe-II1UG5WJJVPV/b2d6137a7485a45e",
        "Url": "http://btml-LoadB-9K2SGQEFUKFK-432766095.ap-south-1.elb.amazonaws.com"
        }
    },
    "createdAt": "2020-10-24T06:56:08.974179Z",
    "lastUpdatedAt": "2020-10-24T06:56:08.974212Z"
    }

Use bentoml ec2 list to have a quick glance of all of the AWS EC2 deployments

> bentoml ec2 list

NAME                        NAMESPACE    LABELS    PLATFORM                               STATUS    AGE
my-first-ec2-deployment     dev          aws-ec2   IrisClassifier:20201015064204_282D00   running   10 minutes and 3.72 seconds

Removing a EC2 deployment is also very easy. Calling bentoml ec2 delete command will delete the all resources from aws.

> bentoml ec2 delete my-first-ec2-deployment

Successfully deleted AWS EC2 deployment "my-first-ec2-deployment"

Deploy and manage AWS EC2 deployments with remote YataiService

BentoML recommends to use remote YataiService for managing and deploying BentoService when you are working in a team. To deploy AWS EC2 deployments with remote YataiService, you need to provide the AWS credentials.

After signed in and configured your AWS CLI in your local machine, you can find the credentials in your aws directory, ~/.aws/credentials as key value pairs, with key name as aws_access_key_id and aws_secret_access_key

Starts a BentoML YataiService docker image and set the credentials found in ~/.aws/credentials as environment variables to the running container.

$ docker run -e AWS_ACCESS_KEY_ID=MY-ACCESS-KEY-ID \
    -e AWS_SECRET_ACCESS_KEY=MY_SECRET-ACCESS-KEY \
    -e AWS_DEFAULT_REGION=MY-DEFAULT-REGION \
    -p 50051:50051 -p 3000:3000 bentoml/yatai-service:latest

After the YataiService docker container is running, in another terminal window, set yatai service address with bentoml config set

$ bentoml config set yatai_service.url=127.0.0.1:50051

Deploy and manage AWS EC2 deployments with Kubernetes

Create a Kubernetes secret with the the AWS credentials.

Generate base64 strings from the AWS credentials from your AWS config file.

$ echo $AWS_ACCESS_KEY_ID | base64
$ echo $AWS_SECRET_KEY | base64
$ echo $AWS_DEFAULT_REGION | base64

Save the following Kubernetes secret definition into a file name aws-secret.yaml and replace {access_key_id}, {secret_access_key} and {default_region} with the values generated above,

apiVersion: v1
kind: Secret
metadata:
    name: my-aws-secret
type: Opaque
data:
    access_key_id: {access_key_id}
    secret_access_key: {secret_access_key}
    default_region: {default_region}

$ kubectl apply -f aws-secret.yaml

Confirm the secrete is created successfully by using kubectl describe command

$kubectl describe secret aws-secret

Copy and paste the code below into a file named yatai-service.yaml

apiVersion: v1
kind: Service
metadata:
  labels:
    app: yatai-service
  name: yatai-service
spec:
  ports:
  - name: grpc
    port: 50051
    targetPort: 50051
  - name: web
    port: 3000
    targetPort: 3000
  selector:
    app: yatai-service
  type: LoadBalancer
---
apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: yatai-service
  name: yatai-service
spec:
  selector:
    matchLabels:
      app: yatai-service
  template:
    metadata:
      labels:
        app: yatai-service
    spec:
      containers:
      - image: bentoml/yatai-service
        imagePullPolicy: IfNotPresent
        name: yatai-service
        ports:
        - containerPort: 50051
        - containerPort: 3000
        env:
        - name: AWS_ACCESS_KEY_ID
          valueFrom:
            secretKeyRef:
              name: aws-secret
              key: access_key_id
        - name: AWS_SECRET_ACCESS_KEY
          valueFrom:
            secretKeyRef:
              name: aws-secret
              key: secret_access_key
        - name: AWS_DEFAULT_REGION
          valueFrom:
            secretKeyRef:
              name: aws-secret
              key: default_region

Run kubectl apply command to deploy Yatai service to the Kubernetes cluster

$ kubectl apply -f yatai-service.yaml

Permissions required on AWS for deployment

  • SSM:GetParameters

  • AmazonEC2FullAccess

  • AmazonEC2ContainerRegistryFullAccess

  • AmazonS3FullAccess

  • IAMFullAccess

  • AmazonVPCFullAccess

  • AWSCloudFormationFullAccess

  • CloudWatchFullAccess

  • ElasticLoadBalancingFullAccess

  • AutoScalingFullAccess