Deploying to AWS SageMaker¶
AWS Sagemaker is a fully managed services for quickly building ML models. BentoML provides great support for deploying BentoService to AWS Sagemaker without additional process and work from user. With BentoML, users can enjoy the great system performance from Sagemaker with any popular ML frameworks.
Prerequisites¶
An active AWS account configured on the machine with AWS CLI installed and configured
Install instruction: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html
Configure AWS account instruction: https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
Docker is installed and running on the machine.
Install instruction: https://docs.docker.com/install
AWS Sagemaker 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 bento 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)
Deploy to Sagemaker requires a deployment name, BentoService name:version tag, and api name from the BentoService bundle. We apply those values to bentoml sagemaker deploy.
> bentoml sagemaker deploy my-first-sagemaker-deployment -b IrisClassifier:20200121141808_FE78B5 --api-name predict
Deploying Sagemaker deployment \[2020-01-21 15:26:43,548] INFO - Step 1/11 : FROM continuumio/miniconda3:4.7.12
...
...
[2020-01-21 15:27:49,201] INFO - Successfully built d72c7deafa31
[2020-01-21 15:27:49,212] INFO - Successfully tagged 192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker:20200121141808_FE78B5
[2020-01-21 15:29:31,814] INFO - ApplyDeployment (my-first-sagemaker-deployment, namespace dev) succeeded
Successfully created AWS Sagemaker deployment my-first-sagemaker-deployment
{
"namespace": "dev",
"name": "my-first-sagemaker-deployment",
"spec": {
"bentoName": "IrisClassifier",
"bentoVersion": "20200121141808_FE78B5",
"operator": "AWS_SAGEMAKER",
"sagemakerOperatorConfig": {
"region": "us-west-2",
"instanceType": "ml.m4.xlarge",
"instanceCount": 1,
"apiName": "predict"
}
},
"state": {
"state": "RUNNING",
"infoJson": {
"EndpointName": "dev-my-first-sagemaker-deployment",
"EndpointArn": "arn:aws:sagemaker:us-west-2:192023623294:endpoint/dev-my-first-sagemaker-deployment",
"EndpointConfigName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"ProductionVariants": [
{
"VariantName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"DeployedImages": [
{
"SpecifiedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker:20200121141808_FE78B5",
"ResolvedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker@sha256:cd723a363bcbad75c090b21575b96879861a69bf00daa1a84515112e8571fc0c",
"ResolutionTime": "2020-01-21 15:29:33.654000-08:00"
}
],
"CurrentWeight": 1.0,
"DesiredWeight": 1.0,
"CurrentInstanceCount": 1,
"DesiredInstanceCount": 1
}
],
"EndpointStatus": "InService",
"CreationTime": "2020-01-21 15:29:31.760000-08:00",
"LastModifiedTime": "2020-01-21 15:38:44.080000-08:00",
"ResponseMetadata": {
"RequestId": "6e946239-1aa3-4a8c-9803-226f6d19b0c7",
"HTTPStatusCode": 200,
"HTTPHeaders": {
"x-amzn-requestid": "6e946239-1aa3-4a8c-9803-226f6d19b0c7",
"content-type": "application/x-amz-json-1.1",
"content-length": "835",
"date": "Tue, 21 Jan 2020 23:38:44 GMT"
},
"RetryAttempts": 0
}
},
"timestamp": "2020-01-21T23:38:44.586400Z"
},
"createdAt": "2020-01-21T23:26:41.575952Z",
"lastUpdatedAt": "2020-01-21T23:26:41.576004Z"
}
After deploy to Sagemaker, use bentoml sagemaker get to return and display the latest status and other information about the deployment
> bentoml sagemaker get my-first-sagemaker-deployment
{
"namespace": "dev",
"name": "my-first-sagemaker-deployment",
"spec": {
"bentoName": "IrisClassifier",
"bentoVersion": "20200121141808_FE78B5",
"operator": "AWS_SAGEMAKER",
"sagemakerOperatorConfig": {
"region": "us-west-2",
"instanceType": "ml.m4.xlarge",
"instanceCount": 1,
"apiName": "predict"
}
},
"state": {
"state": "RUNNING",
"infoJson": {
"EndpointName": "dev-my-first-sagemaker-deployment",
"EndpointArn": "arn:aws:sagemaker:us-west-2:192023623294:endpoint/dev-my-first-sagemaker-deployment",
"EndpointConfigName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"ProductionVariants": [
{
"VariantName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"DeployedImages": [
{
"SpecifiedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker:20200121141808_FE78B5",
"ResolvedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker@sha256:cd723a363bcbad75c090b21575b96879861a69bf00daa1a84515112e8571fc0c",
"ResolutionTime": "2020-01-21 15:29:33.654000-08:00"
}
],
"CurrentWeight": 1.0,
"DesiredWeight": 1.0,
"CurrentInstanceCount": 1,
"DesiredInstanceCount": 1
}
],
"EndpointStatus": "InService",
"CreationTime": "2020-01-21 15:29:31.760000-08:00",
"LastModifiedTime": "2020-01-21 15:38:44.080000-08:00",
"ResponseMetadata": {
"RequestId": "2a2ac5bc-8381-4d34-b283-a48b401f0955",
"HTTPStatusCode": 200,
"HTTPHeaders": {
"x-amzn-requestid": "2a2ac5bc-8381-4d34-b283-a48b401f0955",
"content-type": "application/x-amz-json-1.1",
"content-length": "835",
"date": "Tue, 21 Jan 2020 23:40:54 GMT"
},
"RetryAttempts": 0
}
},
"timestamp": "2020-01-21T23:40:55.332500Z"
},
"createdAt": "2020-01-21T23:26:41.575952Z",
"lastUpdatedAt": "2020-01-21T23:26:41.576004Z"
}
Use AWS CLI to verify that the BentoService is properly deployed to Sagemaker and is ready to inference
> aws sagemaker describe-endpoint --endpoint-name dev-my-first-sagemaker-deployment
{
"EndpointName": "dev-my-first-sagemaker-deployment",
"EndpointArn": "arn:aws:sagemaker:us-west-2:192023623294:endpoint/dev-my-first-sagemaker-deployment",
"EndpointConfigName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"ProductionVariants": [
{
"VariantName": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5",
"DeployedImages": [
{
"SpecifiedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker:20200121141808_FE78B5",
"ResolvedImage": "192023623294.dkr.ecr.us-west-2.amazonaws.com/irisclassifier-sagemaker@sha256:cd723a363bcbad75c090b21575b96879861a69bf00daa1a84515112e8571fc0c",
"ResolutionTime": 1579649373.654
}
],
"CurrentWeight": 1.0,
"DesiredWeight": 1.0,
"CurrentInstanceCount": 1,
"DesiredInstanceCount": 1
}
],
"EndpointStatus": "InService",
"CreationTime": 1579649371.76,
"LastModifiedTime": 1579649924.08
}
Use the sample data to verify the predict result from the Sagemaker deployment
> aws sagemaker-runtime invoke-endpoint \
--endpoint-name dev-my-first-sagemaker-deployment \
--body '[[5.1, 3.5, 1.4, 0.2]]' \
--content-type "application/json" \
>(cat) 1>/dev/null | jq .
[0]{
"ContentType": "application/json",
"InvokedProductionVariant": "dev-my-first-sag-IrisClassifier-20200121141808-FE78B5"
}
Use bentoml sagemaker list to display all sagemaker deployments managed by BentoML
> bentoml sagemaker list
NAME NAMESPACE LABELS PLATFORM STATUS AGE
my-first-sagemaker-deployment dev aws-sagemaker running 15 minutes and 21.14 seconds
Removing Sagemaker deployment is as easy as deploying one. BentoML will remove any related resources from AWS as well.
> bentoml sagemaker delete my-first-sagemaker-deployment
Successfully deleted AWS Sagemaker deployment "my-first-sagemaker-deployment"
Deploy and manage AWS Sagemaker 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 Sagemaker 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 Sagemaker deployments with Kubernetes¶
Create a Kubernetes secret with the the AWS credentials.
Generate bas64 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