Class: Aws::SageMaker::Types::InputConfig
- Inherits:
-
Struct
- Object
- Struct
- Aws::SageMaker::Types::InputConfig
- Includes:
- Aws::Structure
- Defined in:
- lib/aws-sdk-sagemaker/types.rb
Overview
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.
Instance Attribute Summary collapse
-
#data_input_config ⇒ String
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
-
#framework ⇒ String
Identifies the framework in which the model was trained.
-
#s3_uri ⇒ String
The S3 path where the model artifacts, which result from model training, are stored.
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.
-
‘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.
-
‘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, ‘href="1,3,224,224">input0”:`
-
If using the CLI, ‘href="1,3,224,224">input0”:`
-
-
Example for one input in list format: ‘[[1,3,224,224]]`
-
Examples for two inputs in dictionary format:
-
Example for two inputs in list format: ‘[[1,3,224,224], [1,3,224,224]]`
-
-
‘XGBOOST`: input data name and shape are not needed.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 6007 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.
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# File 'lib/aws-sdk-sagemaker/types.rb', line 6007 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).
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# File 'lib/aws-sdk-sagemaker/types.rb', line 6007 class InputConfig < Struct.new( :s3_uri, :data_input_config, :framework) include Aws::Structure end |