public static interface InputConfig.Builder extends SdkPojo, CopyableBuilder<InputConfig.Builder,InputConfig>
| Modifier and Type | Method and Description |
|---|---|
InputConfig.Builder |
dataInputConfig(String dataInputConfig)
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
|
InputConfig.Builder |
framework(Framework framework)
Identifies the framework in which the model was trained.
|
InputConfig.Builder |
framework(String framework)
Identifies the framework in which the model was trained.
|
InputConfig.Builder |
s3Uri(String s3Uri)
The S3 path where the model artifacts, which result from model training, are stored.
|
copyapplyMutation, buildInputConfig.Builder s3Uri(String s3Uri)
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).
s3Uri - 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).InputConfig.Builder dataInputConfig(String dataInputConfig)
Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. The data inputs are InputConfig$Framework specific.
TENSORFLOW, MXNET and ONNX: You must specify the name and shape of the
expected data inputs in order using a dictionary format for your trained model.
Example of one input: {‘data’:[1,3,1024,1024]}}
Example for two inputs: {‘var1’: [1,1,28,28], ‘var2’:[1,1,28,28]}
PYTORCH: You can either specify the name and shape of expected data inputs in order using a
dictionary format for your trained model or you can specify the shape only using a list format.
Example of one input in dictionary format: {‘input0’:[1,3,224,234]}
Example of one input in list format: [1,3,224,224]
Example of two inputs in dictionary format: {‘input0’:[1,3,224,234], 'input1':[1,3,224,224]}
Example of two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
dataInputConfig - Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary
form. The data inputs are InputConfig$Framework specific.
TENSORFLOW, MXNET and ONNX: You must specify the name and shape
of the expected data inputs in order using a dictionary format for your trained model.
Example of one input: {‘data’:[1,3,1024,1024]}}
Example for two inputs: {‘var1’: [1,1,28,28], ‘var2’:[1,1,28,28]}
PYTORCH: You can either specify the name and shape 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.
Example of one input in dictionary format: {‘input0’:[1,3,224,234]}
Example of one input in list format: [1,3,224,224]
Example of two inputs in dictionary format:
{‘input0’:[1,3,224,234], 'input1':[1,3,224,224]}
Example of two inputs in list format: [[1,3,224,224], [1,3,224,224]]
XGBOOST: input data name and shape are not needed.
InputConfig.Builder framework(String framework)
Identifies the framework in which the model was trained. For example: TENSORFLOW.
InputConfig.Builder framework(Framework framework)
Identifies the framework in which the model was trained. For example: TENSORFLOW.
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