Class: Aws::SageMaker::Types::S3ModelDataSource

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

Overview

Specifies the S3 location of ML model data to deploy.

See Also:

Constant Summary collapse

SENSITIVE = 
[]

Instance Attribute Summary collapse

Instance Attribute Details

#compression_type ⇒ String

Specifies how the ML model data is prepared.

If you choose ‘Gzip` and choose `S3Object` as the value of `S3DataType`, `S3Uri` identifies an object that is a gzip-compressed TAR archive. SageMaker will attempt to decompress and untar the object during model deployment.

If you choose ‘None` and chooose `S3Object` as the value of `S3DataType`, `S3Uri` identifies an object that represents an uncompressed ML model to deploy.

If you choose None and choose ‘S3Prefix` as the value of `S3DataType`, `S3Uri` identifies a key name prefix, under which all objects represents the uncompressed ML model to deploy.

If you choose None, then SageMaker will follow rules below when creating model data files under /opt/ml/model directory for use by your inference code:

  • If you choose ‘S3Object` as the value of `S3DataType`, then SageMaker will split the key of the S3 object referenced by `S3Uri` by slash (/), and use the last part as the filename of the file holding the content of the S3 object.

  • If you choose ‘S3Prefix` as the value of `S3DataType`, then for each S3 object under the key name pefix referenced by `S3Uri`, SageMaker will trim its key by the prefix, and use the remainder as the path (relative to `/opt/ml/model`) of the file holding the content of the S3 object. SageMaker will split the remainder by slash (/), using intermediate parts as directory names and the last part as filename of the file holding the content of the S3 object.

  • Do not use any of the following as file names or directory names:

    • An empty or blank string

    • A string which contains null bytes

    • A string longer than 255 bytes

    • A single dot (‘.`)

    • A double dot (‘..`)

  • Ambiguous file names will result in model deployment failure. For example, if your uncompressed ML model consists of two S3 objects ‘s3://mybucket/model/weights` and `s3://mybucket/model/weights/part1` and you specify `s3://mybucket/model/` as the value of `S3Uri` and `S3Prefix` as the value of `S3DataType`, then it will result in name clash between `/opt/ml/model/weights` (a regular file) and `/opt/ml/model/weights/` (a directory).

  • Do not organize the model artifacts in [S3 console using folders]. When you create a folder in S3 console, S3 creates a 0-byte object with a key set to the folder name you provide. They key of the 0-byte object ends with a slash (/) which violates SageMaker restrictions on model artifact file names, leading to model deployment failure.

[1]: docs.aws.amazon.com/AmazonS3/latest/userguide/using-folders.html

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 38103

class S3ModelDataSource < Struct.new(
  :s3_uri,
  :s3_data_type,
  :compression_type,
  :model_access_config)
  SENSITIVE = []
  include Aws::Structure
end

#model_access_config ⇒ Types::ModelAccessConfig

Specifies the access configuration file for the ML model. You can explicitly accept the model end-user license agreement (EULA) within the ‘ModelAccessConfig`. You are responsible for reviewing and complying with any applicable license terms and making sure they are acceptable for your use case before downloading or using a model.

Returns:



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# File 'lib/aws-sdk-sagemaker/types.rb', line 38103

class S3ModelDataSource < Struct.new(
  :s3_uri,
  :s3_data_type,
  :compression_type,
  :model_access_config)
  SENSITIVE = []
  include Aws::Structure
end

#s3_data_type ⇒ String

Specifies the type of ML model data to deploy.

If you choose ‘S3Prefix`, `S3Uri` identifies a key name prefix. SageMaker uses all objects that match the specified key name prefix as part of the ML model data to deploy. A valid key name prefix identified by `S3Uri` always ends with a forward slash (/).

If you choose ‘S3Object`, `S3Uri` identifies an object that is the ML model data to deploy.

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 38103

class S3ModelDataSource < Struct.new(
  :s3_uri,
  :s3_data_type,
  :compression_type,
  :model_access_config)
  SENSITIVE = []
  include Aws::Structure
end

#s3_uri ⇒ String

Specifies the S3 path of ML model data to deploy.

Returns:

  • (String) 


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# File 'lib/aws-sdk-sagemaker/types.rb', line 38103

class S3ModelDataSource < Struct.new(
  :s3_uri,
  :s3_data_type,
  :compression_type,
  :model_access_config)
  SENSITIVE = []
  include Aws::Structure
end