InputConfig (AWS Java SDK for Amazon SageMaker 1.11.468 API)

java.lang.Object
- com.amazonaws.services.sagemaker.model.InputConfig

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class InputConfig
extends Object
implements Serializable, Cloneable, StructuredPojo
```
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:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

InputConfig()

Constructors
Constructor and Description
`InputConfig()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`InputConfig`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getDataInputConfig()` Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
`String`	`getFramework()` Identifies the framework in which the model was trained.
`String`	`getS3Uri()` The S3 path where the model artifacts, which result from model training, are stored.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)`
`void`	`setDataInputConfig(String dataInputConfig)` Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
`void`	`setFramework(String framework)` Identifies the framework in which the model was trained.
`void`	`setS3Uri(String s3Uri)` The S3 path where the model artifacts, which result from model training, are stored.
`String`	`toString()` Returns a string representation of this object.
`InputConfig`	`withDataInputConfig(String dataInputConfig)` Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form.
`InputConfig`	`withFramework(Framework framework)` Identifies the framework in which the model was trained.
`InputConfig`	`withFramework(String framework)` Identifies the framework in which the model was trained.
`InputConfig`	`withS3Uri(String s3Uri)` The S3 path where the model artifacts, which result from model training, are stored.

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - InputConfig
```
public InputConfig()
```
- Method Detail
  - setS3Uri
```
public void setS3Uri(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).
    
    Parameters:
    
    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).
  - getS3Uri
```
public String getS3Uri()
```
    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:
    
    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).
  - withS3Uri
```
public InputConfig withS3Uri(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).
    
    Parameters:
    
    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).
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setDataInputConfig
```
public void setDataInputConfig(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.
    Parameters:
    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.
  - getDataInputConfig
```
public String getDataInputConfig()
```
    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.
    Returns:
    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.
  - withDataInputConfig
```
public InputConfig withDataInputConfig(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.
    Parameters:
    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.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setFramework
```
public void setFramework(String framework)
```
    Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Parameters:
    
    framework - Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    See Also:
    
    Framework
  - getFramework
```
public String getFramework()
```
    Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Returns:
    
    Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    See Also:
    
    Framework
  - withFramework
```
public InputConfig withFramework(String framework)
```
    Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Parameters:
    
    framework - Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Framework
  - withFramework
```
public InputConfig withFramework(Framework framework)
```
    Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Parameters:
    
    framework - Identifies the framework in which the model was trained. For example: TENSORFLOW.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Framework
  - toString
```
public String toString()
```
    Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    
    A string representation of this object.
    
    See Also:
    
    Object.toString()
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - clone
```
public InputConfig clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Specified by:
    
    marshall in interface StructuredPojo

Class InputConfig

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

InputConfig

Method Detail

setS3Uri

getS3Uri

withS3Uri

setDataInputConfig

getDataInputConfig

withDataInputConfig

setFramework

getFramework

withFramework

withFramework

toString

equals

hashCode

clone

marshall