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Class: Aws::SageMaker::Types::InputConfig

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 InputConfig data as a hash:

{
  s3_uri: "S3Uri", # required
  data_input_config: "DataInputConfig", # required
  framework: "TENSORFLOW", # required, accepts TENSORFLOW, MXNET, ONNX, PYTORCH, XGBOOST
}

Contains information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

See Also:

Instance Attribute Summary collapse

Instance Attribute Details

#data_input_config ⇒ String

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.

  • TensorFlow: You must specify the name and shape (NHWC format) of the expected data inputs using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, \{"input":[1,1024,1024,3]\}

      • If using the CLI, \{"input":[1,1024,1024,3]\}

    • Examples for two inputs:

      • If using the console, \{"data1": [1,28,28,1], "data2":[1,28,28,1]\}

      • If using the CLI, \{"data1": [1,28,28,1], "data2":[1,28,28,1]\}

  • MXNET/ONNX: You must specify the name and shape (NCHW format) of the expected data inputs in order using a dictionary format for your trained model. The dictionary formats required for the console and CLI are different.

    • Examples for one input:

      • If using the console, \{"data":[1,3,1024,1024]\}

      • If using the CLI, \{"data":[1,3,1024,1024]\}

    • Examples for two inputs:

      • If using the console, \{"var1": [1,1,28,28], "var2":[1,1,28,28]\}

      • If using the CLI, \{"var1": [1,1,28,28], "var2":[1,1,28,28]\}

  • PyTorch: You can either specify the name and shape (NCHW format) of expected data inputs in order using a dictionary format for your trained model or you can specify the shape only using a list format. The dictionary formats required for the console and CLI are different. The list formats for the console and CLI are the same.

    • Examples for one input in dictionary format:

      • If using the console, \{"input0":[1,3,224,224]\}

      • If using the CLI, \{"input0":[1,3,224,224]\}

    • Example for one input in list format: [[1,3,224,224]]

    • Examples for two inputs in dictionary format:

      • If using the console, \{"input0":[1,3,224,224], "input1":[1,3,224,224]\}

      • If using the CLI, \{"input0":[1,3,224,224], "input1":[1,3,224,224]\}

    • Example for two inputs in list format: [[1,3,224,224], [1,3,224,224]]

  • XGBOOST: input data name and shape are not needed.

Returns:

  • (String) 


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6643

class InputConfig < Struct.new(
  :s3_uri,
  :data_input_config,
  :framework)
  include Aws::Structure
end

#framework ⇒ String

Identifies the framework in which the model was trained. For example: TENSORFLOW.

Returns:

  • (String) 


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6643

class InputConfig < Struct.new(
  :s3_uri,
  :data_input_config,
  :framework)
  include Aws::Structure
end

#s3_uri ⇒ String

The S3 path where the model artifacts, which result from model training, are stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix).

Returns:

  • (String) 


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# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 6643

class InputConfig < Struct.new(
  :s3_uri,
  :data_input_config,
  :framework)
  include Aws::Structure
end