Anyone reading this thread and wanting to use SageMaker and Java V2 (not V1), see this GitHub location:
https://github.com/aws-doc-sdk-examples/tree/master/javav2/example_code/sagemaker
There are many examples of how to work with this API. For example, the following code example demonstrates how to start a model training job for Amazon SageMaker.
import software.amazon.awssdk.regions.Region;
import software.amazon.awssdk.services.sagemaker.SageMakerClient;
import software.amazon.awssdk.services.sagemaker.model.S3DataSource;
import software.amazon.awssdk.services.sagemaker.model.DataSource;
import software.amazon.awssdk.services.sagemaker.model.Channel;
import software.amazon.awssdk.services.sagemaker.model.ResourceConfig;
import software.amazon.awssdk.services.sagemaker.model.TrainingInstanceType;
import software.amazon.awssdk.services.sagemaker.model.CheckpointConfig;
import software.amazon.awssdk.services.sagemaker.model.OutputDataConfig;
import software.amazon.awssdk.services.sagemaker.model.StoppingCondition;
import software.amazon.awssdk.services.sagemaker.model.AlgorithmSpecification;
import software.amazon.awssdk.services.sagemaker.model.TrainingInputMode;
import software.amazon.awssdk.services.sagemaker.model.CreateTrainingJobRequest;
import software.amazon.awssdk.services.sagemaker.model.CreateTrainingJobResponse;
import software.amazon.awssdk.services.sagemaker.model.SageMakerException;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
//snippet-end:[sagemaker.java2.train_job.import]
/**
* To setup the model data and other requirements to make this Java V2 example work, follow this AWS tutorial prior to running this Java code example.
* https://aws.amazon.com/getting-started/hands-on/build-train-deploy-machine-learning-model-sagemaker/
*/
public class CreateTrainingJob {
public static void main(String[] args) {
final String USAGE = "\n" +
"Usage:\n" +
" CreateTrainingJob <s3UriData><s3Uri><trainingJobName><roleArn><s3OutputPath><channelName><trainingImage>\n\n" +
"Where:\n" +
" s3UriData - The location where the training data is located (ie, s3://trainbucket/train.csv).\n\n" +
" s3Uri - The S3 path where you want Amazon SageMaker to store checkpoints (ie, s3://trainbucket).\n\n" +
" trainingJobName - The name of the training job. \n\n" +
" roleArn - The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker uses.\n\n" +
" s3OutputPath - The output path located in a S3 bucket (i.e., s3://trainbucket/sagemaker).\n\n" +
" channelName - The channel name \n\n" +
" trainingImage - The training image.";
if (args.length < 7) {
System.out.println(USAGE);
System.exit(1);
}
/* Read the name from command args */
String s3UriData = args[0];
String s3Uri = args[1];
String trainingJobName = args[2];
String roleArn = args[3];
String s3OutputPath = args[4];
String channelName = args[5];
String trainingImage = args[6];
Region region = Region.US_WEST_2;
SageMakerClient sageMakerClient = SageMakerClient.builder()
.region(region)
.build();
trainJob(sageMakerClient, s3UriData, s3Uri, trainingJobName, roleArn, s3OutputPath, channelName, trainingImage);
}
//snippet-start:[sagemaker.java2.train_job.main]
public static void trainJob(SageMakerClient sageMakerClient,
String s3UriData,
String s3Uri,
String trainingJobName,
String roleArn,
String s3OutputPath,
String channelName,
String trainingImage) {
try {
S3DataSource s3DataSource = S3DataSource.builder()
.s3Uri(s3UriData)
.s3DataType("S3Prefix")
.s3DataDistributionType("FullyReplicated")
.build();
DataSource dataSource = DataSource.builder()
.s3DataSource(s3DataSource)
.build();
Channel channel = Channel.builder()
.channelName(channelName)
.contentType("csv")
.dataSource(dataSource)
.build();
// Build a LIST of CHannels
List<Channel> myChannel = new ArrayList();
myChannel.add(channel);
ResourceConfig resourceConfig = ResourceConfig.builder()
.instanceType(TrainingInstanceType.ML_M5_2_XLARGE) // ml.c5.2xlarge
.instanceCount(10)
.volumeSizeInGB(1)
.build();
CheckpointConfig checkpointConfig = CheckpointConfig.builder()
.s3Uri(s3Uri)
.build();
OutputDataConfig outputDataConfig = OutputDataConfig.builder()
.s3OutputPath(s3OutputPath)
.build();
StoppingCondition stoppingCondition = StoppingCondition.builder()
.maxRuntimeInSeconds(1200)
.build();
AlgorithmSpecification algorithmSpecification = AlgorithmSpecification.builder()
.trainingImage(trainingImage)
.trainingInputMode(TrainingInputMode.FILE)
.build();
// Set hyper parameters
Map<String,String> hyperParameters = new HashMap<String, String>();
hyperParameters.put("num_round", "100");
hyperParameters.put("eta", "0.2");
hyperParameters.put("gamma", "4");
hyperParameters.put("max_depth", "5");
hyperParameters.put("min_child_weight", "6");
hyperParameters.put("objective", "binary:logistic");
hyperParameters.put("silent", "0");
hyperParameters.put("subsample", "0.8");
CreateTrainingJobRequest trainingJobRequest = CreateTrainingJobRequest.builder()
.trainingJobName(trainingJobName)
.algorithmSpecification(algorithmSpecification)
.roleArn(roleArn)
.resourceConfig(resourceConfig)
.checkpointConfig(checkpointConfig)
.inputDataConfig(myChannel)
.outputDataConfig(outputDataConfig)
.stoppingCondition(stoppingCondition)
.hyperParameters(hyperParameters)
.build();
CreateTrainingJobResponse jobResponse = sageMakerClient.createTrainingJob(trainingJobRequest);
System.out.println("The Amazon Resource Name (ARN) of the training job is "+ jobResponse.trainingJobArn());
} catch (SageMakerException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
}
}
//snippet-end:[sagemaker.java2.train_job.main]
}
