Amazon SageMaker Operators for Kubernetes

Introduction

Amazon SageMaker Operators for Kubernetes are operators that can be used to train machine learning models, optimize hyperparameters for a given model, run batch transform jobs over existing models, and set up inference endpoints. With these operators, users can manage their jobs in Amazon SageMaker from their Kubernetes cluster in Amazon Elastic Kubernetes Service EKS.

Usage

First, you must install the operators. After installation is complete, create a TrainingJob YAML specification by following one of the samples, like samples/xgboost-mnist-trainingjob.yaml. Then, use kubectl to create and monitor the progress of your job:

$ kubectl apply -f xgboost-mnist-trainingjob.yaml
trainingjob.sagemaker.aws.amazon.com/xgboost-mnist created

$ kubectl get trainingjob
NAME            STATUS       SECONDARY-STATUS   CREATION-TIME          SAGEMAKER-JOB-NAME
xgboost-mnist   InProgress   Starting           2019-11-26T23:38:11Z   xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06

Once the job starts training, you can use a kubectl plugin to stream training logs:

$ kubectl get trainingjob
NAME            STATUS       SECONDARY-STATUS   CREATION-TIME          SAGEMAKER-JOB-NAME
xgboost-mnist   InProgress   Training           2019-11-26T23:38:11Z   xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06

$ kubectl smlogs trainingjob xgboost-mnist | head -n 5
"xgboost-mnist" has SageMaker TrainingJobName "xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06" in region "us-east-2", status "InProgress" and secondary status "Training"
xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06/algo-1-1574811611 2019-11-26 15:41:13.449 -0800 PST Arguments: train
xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06/algo-1-1574811611 2019-11-26 15:41:13.449 -0800 PST [2019-11-26:23:41:10:INFO] Running standalone xgboost training.
xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06/algo-1-1574811611 2019-11-26 15:41:13.45 -0800 PST [2019-11-26:23:41:10:INFO] File size need to be processed in the node: 1122.95mb. Available memory size in the node: 8501.08mb
xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06/algo-1-1574811611 2019-11-26 15:41:13.45 -0800 PST [2019-11-26:23:41:10:INFO] Determined delimiter of CSV input is ','
xgboost-mnist-cf1e16fb10a511eaaa450a350733ba06/algo-1-1574811611 2019-11-26 15:41:13.45 -0800 PST [23:41:10] S3DistributionType set as FullyReplicated

The Amazon SageMaker Operators for Kubernetes enable management of SageMaker TrainingJobs, HyperParameterTuningJobs, BatchTransformJobs and HostingDeployments (Endpoints). Create and monitor them using the same kubectl tool as above.

To install the operators onto your Kubernetes cluster, follow our User Guide.

YAML Examples

To make a YAML spec, follow one of the below examples as a guide. Replace values like RoleARN, S3 input buckets and S3 output buckets with values that correspond to your account.

• BatchTransformJob
• HostingDeployment (Endpoint)
• HyperParameterTuningJob
• TrainingJob

Releases

Amazon SageMaker Operator for Kubernetes adheres to the SemVer specification. Each release updates the major version tag (eg. vX), a major/minor version tag (eg. vX.Y) and a major/minor/patch version tag (eg. vX.Y.Z), as well as new versions of the smlogs binary with URLs of the same versioning formats. To see a full list of all releases, refer to our Github releases page.

We also maintain a latest tag, which is updated to stay in line with the master branch. We do not recommend installing this on any production cluster, as any new major versions updated on the master branch will introduce breaking changes.

Contributing

amazon-sagemaker-operator-for-k8s is an open source project. See CONTRIBUTING for details.

License

This project is distributed under the Apache License, Version 2.0, see LICENSE and NOTICE for more information.