import { SageMakerClient, CreateTrainingJobCommand } from "@aws-sdk/client-sagemaker"; // ES Modules import
// const { SageMakerClient, CreateTrainingJobCommand } = require("@aws-sdk/client-sagemaker"); // CommonJS import
const client = new SageMakerClient(config);
const command = new CreateTrainingJobCommand(input);
const response = await client.send(command);

new tasks.SageMakerCreateModel(this, 'Sagemaker', {
  modelName: 'MyModel',
  primaryContainer: new tasks.ContainerDefinition({
    image: tasks.DockerImage.fromJsonExpression(sfn.JsonPath.stringAt('$.Model.imageName')),
    mode: tasks.Mode.SINGLE_MODEL,
    modelS3Location: tasks.S3Location.fromJsonExpression('$.TrainingJob.ModelArtifacts.S3ModelArtifacts'),
  }),
});

new tasks.SageMakerCreateTrainingJob(this, 'TrainSagemaker', {
  trainingJobName: sfn.JsonPath.stringAt('$.JobName'),
  algorithmSpecification: {
    algorithmName: 'BlazingText',
    trainingInputMode: tasks.InputMode.FILE,
  },
  inputDataConfig: [{
    channelName: 'train',
    dataSource: {
      s3DataSource: {
        s3DataType: tasks.S3DataType.S3_PREFIX,
        s3Location: tasks.S3Location.fromJsonExpression('$.S3Bucket'),
      },
    },
  }],
  outputDataConfig: {
    s3OutputLocation: tasks.S3Location.fromBucket(s3.Bucket.fromBucketName(this, 'Bucket', 'mybucket'), 'myoutputpath'),
  },
  resourceConfig: {
    instanceCount: 1,
    instanceType: new ec2.InstanceType(sfn.JsonPath.stringAt('$.InstanceType')),
    volumeSize: Size.gibibytes(50),
  }, // optional: default is 1 instance of EC2 `M4.XLarge` with `10GB` volume
  stoppingCondition: {
    maxRuntime: Duration.hours(2),
  }, // optional: default is 1 hour
});