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NAMEPaws::SageMaker::S3DataSource USAGEThis class represents one of two things: Arguments in a call to a service Use the attributes of this class as arguments to methods. You shouldn't make instances of this class. Each attribute should be used as a named argument in the calls that expect this type of object. As an example, if Att1 is expected to be a Paws::SageMaker::S3DataSource object: $service_obj->Method(Att1 => { AttributeNames => $value, ..., S3Uri => $value }); Results returned from an API call Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::S3DataSource object: $result = $service_obj->Method(...); $result->Att1->AttributeNames DESCRIPTIONDescribes the S3 data source. ATTRIBUTESAttributeNames => ArrayRef[Str|Undef]A list of one or more attribute names to use that are found in a specified augmented manifest file. S3DataDistributionType => StrIf you want Amazon SageMaker to replicate the entire dataset on each ML compute instance that is launched for model training, specify "FullyReplicated". If you want Amazon SageMaker to replicate a subset of data on each ML compute instance that is launched for model training, specify "ShardedByS3Key". If there are n ML compute instances launched for a training job, each instance gets approximately 1/n of the number of S3 objects. In this case, model training on each machine uses only the subset of training data. Don't choose more ML compute instances for training than available S3 objects. If you do, some nodes won't get any data and you will pay for nodes that aren't getting any training data. This applies in both File and Pipe modes. Keep this in mind when developing algorithms. In distributed training, where you use multiple ML compute EC2 instances, you might choose "ShardedByS3Key". If the algorithm requires copying training data to the ML storage volume (when "TrainingInputMode" is set to "File"), this copies 1/n of the number of objects. REQUIRED S3DataType => StrIf you choose "S3Prefix", "S3Uri" identifies a key name prefix. Amazon SageMaker uses all objects that match the specified key name prefix for model training. If you choose "ManifestFile", "S3Uri" identifies an object that is a manifest file containing a list of object keys that you want Amazon SageMaker to use for model training. If you choose "AugmentedManifestFile", S3Uri identifies an object that is an augmented manifest file in JSON lines format. This file contains the data you want to use for model training. "AugmentedManifestFile" can only be used if the Channel's input mode is "Pipe". REQUIRED S3Uri => StrDepending on the value specified for the "S3DataType", identifies either a key name prefix or a manifest. For example:
SEE ALSOThis class forms part of Paws, describing an object used in Paws::SageMaker BUGS and CONTRIBUTIONSThe source code is located here: <https://github.com/pplu/aws-sdk-perl> Please report bugs to: <https://github.com/pplu/aws-sdk-perl/issues>
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