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

Inherits:
Struct
  • Object
show all
Defined in:
(unknown)

Overview

Note:

When passing InputConfig as input to an Aws::Client method, you can use a vanilla 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.

Returned by:

Instance Attribute Summary collapse

Instance Attribute Details

#data_input_configString

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)

    Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.

#frameworkString

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

Possible values:

  • TENSORFLOW
  • MXNET
  • ONNX
  • PYTORCH
  • XGBOOST

Returns:

  • (String)

    Identifies the framework in which the model was trained.

#s3_uriString

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)

    The S3 path where the model artifacts, which result from model training, are stored.