Deploying to AWS SageMaker¶
AWS Sagemaker is a fully managed services for quickly building ML models. BentoML provides great sujpport 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.
Prerequisits¶
An active AWS account configured on the machine with AWS CLI installed and configurated
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¶
We will use the IrisClassifier BentoService from the getting started guide(https://docs.bentoml.org/en/latest/quickstart.html). 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(DataframeHandler) 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/[email protected]: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 we deploy to Sagemaker, we can 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/[email protected]: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"
}
Using AWS CLI, we can 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/[email protected]:cd723a363bcbad75c090b21575b96879861a69bf00daa1a84515112e8571fc0c",
"ResolutionTime": 1579649373.654
}
],
"CurrentWeight": 1.0,
"DesiredWeight": 1.0,
"CurrentInstanceCount": 1,
"DesiredInstanceCount": 1
}
],
"EndpointStatus": "InService",
"CreationTime": 1579649371.76,
"LastModifiedTime": 1579649924.08
}
We will use the same 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"