Inputs#

With SageMaker training jobs, you specify an “input channel” and configure it in the job definition. Each channel is a dictionary that specifies the data split. Each of these items will be made available on the instance in /opt/ml/input/data/<stage>.

JSON Example# 

{
 "train": {
 "ContentType": "trainingContentType",
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 },
 "evaluation": {
 "ContentType": "evalContentType",
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 },
 "validation": {
 "TrainingInputMode": "File",
 "S3DistributionType": "FullyReplicated",
 "RecordWrapperType": "None"
 }
}

CDK Example#

Outputs#

With SageMaker training jobs, model artifacts should be saved to /opt/ml/model/ so that, with the training job output configuration, they will be zipped and added to S3. Failures can be written to the file /opt/ml/output/failure and the first 1024 characters will be returned as FailureReason.