You are viewing documentation for version 3 of the AWS SDK for Ruby. Version 2 documentation can be found here.

Class: Aws::SageMaker::Types::S3DataSource

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

Note:

When making an API call, you may pass S3DataSource data as a hash:

{
  s3_data_type: "ManifestFile", # required, accepts ManifestFile, S3Prefix
  s3_uri: "S3Uri", # required
  s3_data_distribution_type: "FullyReplicated", # accepts FullyReplicated, ShardedByS3Key
}

Describes the S3 data source.

Instance Attribute Summary collapse

Instance Attribute Details

#s3_data_distribution_typeString

If 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.

Returns:

  • (String)


2240
2241
2242
2243
2244
2245
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2240

class S3DataSource < Struct.new(
  :s3_data_type,
  :s3_uri,
  :s3_data_distribution_type)
  include Aws::Structure
end

#s3_data_typeString

If you choose S3Prefix, S3Uri identifies a key name prefix. Amazon SageMaker uses all objects with 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.

Returns:

  • (String)


2240
2241
2242
2243
2244
2245
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2240

class S3DataSource < Struct.new(
  :s3_data_type,
  :s3_uri,
  :s3_data_distribution_type)
  include Aws::Structure
end

#s3_uriString

Depending on the value specified for the S3DataType, identifies either a key name prefix or a manifest. For example:

  • A key name prefix might look like this: s3://bucketname/exampleprefix.

  • A manifest might look like this: s3://bucketname/example.manifest

    The manifest is an S3 object which is a JSON file with the following format:

    [

    \{"prefix": "s3://customer_bucket/some/prefix/"\},

    "relative/path/to/custdata-1",

    "relative/path/custdata-2",

    ...

    ]

    The preceding JSON matches the following s3Uris:

    s3://customer_bucket/some/prefix/relative/path/to/custdata-1

    s3://customer_bucket/some/prefix/relative/path/custdata-1

    ...

    The complete set of s3uris in this manifest constitutes the input data for the channel for this datasource. The object that each s3uris points to must readable by the IAM role that Amazon SageMaker uses to perform tasks on your behalf.

Returns:

  • (String)


2240
2241
2242
2243
2244
2245
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2240

class S3DataSource < Struct.new(
  :s3_data_type,
  :s3_uri,
  :s3_data_distribution_type)
  include Aws::Structure
end