NAME

Paws::SageMaker::InputConfig

USAGE

This 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::InputConfig object:

  $service_obj->Method(Att1 => { DataInputConfig => $value, ..., S3Uri => $value  });

Results returned from an API call

Use accessors for each attribute. If Att1 is expected to be an Paws::SageMaker::InputConfig object:

  $result = $service_obj->Method(...);
  $result->Att1->DataInputConfig

DESCRIPTION

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.

ATTRIBUTES

REQUIRED DataInputConfig => Str

  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.

REQUIRED Framework => Str

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

REQUIRED S3Uri => Str

  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).

SEE ALSO

This class forms part of Paws, describing an object used in Paws::SageMaker

BUGS and CONTRIBUTIONS

The source code is located here: https://github.com/pplu/aws-sdk-perl

Please report bugs to: https://github.com/pplu/aws-sdk-perl/issues