OutputConfig (AWS SDK for Java

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

All Implemented Interfaces:

StructuredPojo, Serializable, Cloneable
```
@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class OutputConfig
extends Object
implements Serializable, Cloneable, StructuredPojo
```
Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.

See Also:

AWS API Documentation, Serialized Form

Constructor Summary

Constructors
Constructor and Description

OutputConfig()

Constructors
Constructor and Description
`OutputConfig()`

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`OutputConfig`	`clone()`
`boolean`	`equals(Object obj)`
`String`	`getCompilerOptions()` Specifies additional parameters for compiler options in JSON format.
`String`	`getKmsKeyId()` The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job.
`String`	`getS3OutputLocation()` Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
`String`	`getTargetDevice()` Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
`TargetPlatform`	`getTargetPlatform()` Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.
`int`	`hashCode()`
`void`	`marshall(ProtocolMarshaller protocolMarshaller)` Marshalls this structured data using the given `ProtocolMarshaller`.
`void`	`setCompilerOptions(String compilerOptions)` Specifies additional parameters for compiler options in JSON format.
`void`	`setKmsKeyId(String kmsKeyId)` The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job.
`void`	`setS3OutputLocation(String s3OutputLocation)` Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
`void`	`setTargetDevice(String targetDevice)` Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
`void`	`setTargetPlatform(TargetPlatform targetPlatform)` Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.
`String`	`toString()` Returns a string representation of this object.
`OutputConfig`	`withCompilerOptions(String compilerOptions)` Specifies additional parameters for compiler options in JSON format.
`OutputConfig`	`withKmsKeyId(String kmsKeyId)` The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job.
`OutputConfig`	`withS3OutputLocation(String s3OutputLocation)` Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.
`OutputConfig`	`withTargetDevice(String targetDevice)` Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
`OutputConfig`	`withTargetDevice(TargetDevice targetDevice)` Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
`OutputConfig`	`withTargetPlatform(TargetPlatform targetPlatform)` Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.

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

- Constructor Detail
  - OutputConfig
```
public OutputConfig()
```
- Method Detail
  - setS3OutputLocation
```
public void setS3OutputLocation(String s3OutputLocation)
```
    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
    
    Parameters:
    
    s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
  - getS3OutputLocation
```
public String getS3OutputLocation()
```
    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
    
    Returns:
    
    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
  - withS3OutputLocation
```
public OutputConfig withS3OutputLocation(String s3OutputLocation)
```
    Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
    
    Parameters:
    
    s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setTargetDevice
```
public void setTargetDevice(String targetDevice)
```
    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Parameters:
    
    targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    See Also:
    
    TargetDevice
  - getTargetDevice
```
public String getTargetDevice()
```
    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Returns:
    
    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    See Also:
    
    TargetDevice
  - withTargetDevice
```
public OutputConfig withTargetDevice(String targetDevice)
```
    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Parameters:
    
    targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TargetDevice
  - withTargetDevice
```
public OutputConfig withTargetDevice(TargetDevice targetDevice)
```
    Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Parameters:
    
    targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    TargetDevice
  - setTargetPlatform
```
public void setTargetPlatform(TargetPlatform targetPlatform)
```
    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    - Raspberry Pi 3 Model B+
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
      
      "CompilerOptions": {'mattr': ['+neon']}
    - Jetson TX2
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    - EC2 m5.2xlarge instance OS
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'mcpu': 'skylake-avx512'}
    - RK3399
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    - ARMv7 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    - ARMv8 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 29}
    Parameters:
    targetPlatform - Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    
    Raspberry Pi 3 Model B+
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
    
    "CompilerOptions": {'mattr': ['+neon']}
    
    Jetson TX2
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    
    EC2 m5.2xlarge instance OS
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'mcpu': 'skylake-avx512'}
    
    RK3399
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    
    ARMv7 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    
    ARMv8 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 29}
  - getTargetPlatform
```
public TargetPlatform getTargetPlatform()
```
    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    - Raspberry Pi 3 Model B+
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
      
      "CompilerOptions": {'mattr': ['+neon']}
    - Jetson TX2
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    - EC2 m5.2xlarge instance OS
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'mcpu': 'skylake-avx512'}
    - RK3399
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    - ARMv7 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    - ARMv8 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 29}
    Returns:
    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    
    Raspberry Pi 3 Model B+
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
    
    "CompilerOptions": {'mattr': ['+neon']}
    
    Jetson TX2
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    
    EC2 m5.2xlarge instance OS
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'mcpu': 'skylake-avx512'}
    
    RK3399
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    
    ARMv7 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    
    ARMv8 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 29}
  - withTargetPlatform
```
public OutputConfig withTargetPlatform(TargetPlatform targetPlatform)
```
    Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    - Raspberry Pi 3 Model B+
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
      
      "CompilerOptions": {'mattr': ['+neon']}
    - Jetson TX2
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    - EC2 m5.2xlarge instance OS
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
      
      "CompilerOptions": {'mcpu': 'skylake-avx512'}
    - RK3399
      
      "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    - ARMv7 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    - ARMv8 phone (CPU)
      
      "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
      
      "CompilerOptions": {'ANDROID_PLATFORM': 29}
    Parameters:
    targetPlatform - Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.
    
    The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:
    
    Raspberry Pi 3 Model B+
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},
    
    "CompilerOptions": {'mattr': ['+neon']}
    
    Jetson TX2
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}
    
    EC2 m5.2xlarge instance OS
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},
    
    "CompilerOptions": {'mcpu': 'skylake-avx512'}
    
    RK3399
    
    "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}
    
    ARMv7 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}
    
    ARMv8 phone (CPU)
    
    "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},
    
    "CompilerOptions": {'ANDROID_PLATFORM': 29}
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setCompilerOptions
```
public void setCompilerOptions(String compilerOptions)
```
    Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    - DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
      
      float32: Use either "float" or "float32".
      
      int64: Use either "int64" or "long".
      
      For example, {"dtype" : "float32"}.
    - CPU: Compilation for CPU supports the following compiler options.
      
      mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
      
      mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    - ARM: Details of ARM CPU compilations.
      
      NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
      
      For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    - NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
      
      gpu_code: Specifies the targeted architecture.
      
      trt-ver: Specifies the TensorRT versions in x.y.z. format.
      
      cuda-ver: Specifies the CUDA version in x.y format.
      
      For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    - ANDROID: Compilation for the Android OS supports the following compiler options:
      
      ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
      
      mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    - INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
      
      For information about supported compiler options, see Neuron Compiler CLI.
    - CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
      
      class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    - EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
      
      precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
      
      signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
      
      output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
      
      For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
    Parameters:
    compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    
    DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
    
    float32: Use either "float" or "float32".
    
    int64: Use either "int64" or "long".
    
    For example, {"dtype" : "float32"}.
    
    CPU: Compilation for CPU supports the following compiler options.
    
    mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
    
    mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    
    ARM: Details of ARM CPU compilations.
    
    NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
    
    For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    
    NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
    
    gpu_code: Specifies the targeted architecture.
    
    trt-ver: Specifies the TensorRT versions in x.y.z. format.
    
    cuda-ver: Specifies the CUDA version in x.y format.
    
    For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    
    ANDROID: Compilation for the Android OS supports the following compiler options:
    
    ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
    
    mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    
    INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
    
    For information about supported compiler options, see Neuron Compiler CLI.
    
    CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
    
    class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    
    EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
    
    precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
    
    signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
    
    output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
    
    For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
  - getCompilerOptions
```
public String getCompilerOptions()
```
    Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    - DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
      
      float32: Use either "float" or "float32".
      
      int64: Use either "int64" or "long".
      
      For example, {"dtype" : "float32"}.
    - CPU: Compilation for CPU supports the following compiler options.
      
      mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
      
      mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    - ARM: Details of ARM CPU compilations.
      
      NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
      
      For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    - NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
      
      gpu_code: Specifies the targeted architecture.
      
      trt-ver: Specifies the TensorRT versions in x.y.z. format.
      
      cuda-ver: Specifies the CUDA version in x.y format.
      
      For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    - ANDROID: Compilation for the Android OS supports the following compiler options:
      
      ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
      
      mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    - INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
      
      For information about supported compiler options, see Neuron Compiler CLI.
    - CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
      
      class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    - EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
      
      precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
      
      signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
      
      output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
      
      For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
    Returns:
    Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    
    DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
    
    float32: Use either "float" or "float32".
    
    int64: Use either "int64" or "long".
    
    For example, {"dtype" : "float32"}.
    
    CPU: Compilation for CPU supports the following compiler options.
    
    mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
    
    mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    
    ARM: Details of ARM CPU compilations.
    
    NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
    
    For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    
    NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
    
    gpu_code: Specifies the targeted architecture.
    
    trt-ver: Specifies the TensorRT versions in x.y.z. format.
    
    cuda-ver: Specifies the CUDA version in x.y format.
    
    For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    
    ANDROID: Compilation for the Android OS supports the following compiler options:
    
    ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
    
    mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    
    INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
    
    For information about supported compiler options, see Neuron Compiler CLI.
    
    CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
    
    class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    
    EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
    
    precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
    
    signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
    
    output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
    
    For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
  - withCompilerOptions
```
public OutputConfig withCompilerOptions(String compilerOptions)
```
    Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    - DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
      
      float32: Use either "float" or "float32".
      
      int64: Use either "int64" or "long".
      
      For example, {"dtype" : "float32"}.
    - CPU: Compilation for CPU supports the following compiler options.
      
      mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
      
      mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    - ARM: Details of ARM CPU compilations.
      
      NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
      
      For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    - NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
      
      gpu_code: Specifies the targeted architecture.
      
      trt-ver: Specifies the TensorRT versions in x.y.z. format.
      
      cuda-ver: Specifies the CUDA version in x.y format.
      
      For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    - ANDROID: Compilation for the Android OS supports the following compiler options:
      
      ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
      
      mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    - INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
      
      For information about supported compiler options, see Neuron Compiler CLI.
    - CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
      
      class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    - EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
      
      precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
      
      signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
      
      output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
      
      For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
    Parameters:
    compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.
    
    DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:
    
    float32: Use either "float" or "float32".
    
    int64: Use either "int64" or "long".
    
    For example, {"dtype" : "float32"}.
    
    CPU: Compilation for CPU supports the following compiler options.
    
    mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}
    
    mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}
    
    ARM: Details of ARM CPU compilations.
    
    NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
    
    For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.
    
    NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.
    
    gpu_code: Specifies the targeted architecture.
    
    trt-ver: Specifies the TensorRT versions in x.y.z. format.
    
    cuda-ver: Specifies the CUDA version in x.y format.
    
    For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}
    
    ANDROID: Compilation for the Android OS supports the following compiler options:
    
    ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.
    
    mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.
    
    INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".
    
    For information about supported compiler options, see Neuron Compiler CLI.
    
    CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:
    
    class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.
    
    EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:
    
    precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".
    
    signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.
    
    output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.
    
    For example: {"precision_mode": "FP32", "output_names": ["output:0"]}
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - setKmsKeyId
```
public void setKmsKeyId(String kmsKeyId)
```
    The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    - Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    - Alias name: alias/ExampleAlias
    - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
    Parameters:
    kmsKeyId - The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    
    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    
    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    
    Alias name: alias/ExampleAlias
    
    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
  - getKmsKeyId
```
public String getKmsKeyId()
```
    The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    - Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    - Alias name: alias/ExampleAlias
    - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
    Returns:
    The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    
    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    
    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    
    Alias name: alias/ExampleAlias
    
    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
  - withKmsKeyId
```
public OutputConfig withKmsKeyId(String kmsKeyId)
```
    The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    - Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    - Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    - Alias name: alias/ExampleAlias
    - Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
    Parameters:
    kmsKeyId - The AWS Key Management Service (AWS KMS) key that Amazon SageMaker uses to encrypt data on the storage volume after compilation job. If you don't provide a KMS key ID, Amazon SageMaker uses the default KMS key for Amazon S3 for your role's account
    
    The KmsKeyId can be any of the following formats:
    
    Key ID: 1234abcd-12ab-34cd-56ef-1234567890ab
    
    Key ARN: arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab
    
    Alias name: alias/ExampleAlias
    
    Alias name ARN: arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - 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 OutputConfig clone()
```
    Overrides:
    
    clone in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

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Class OutputConfig

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

OutputConfig

Method Detail

setS3OutputLocation

getS3OutputLocation

withS3OutputLocation

setTargetDevice

getTargetDevice

withTargetDevice

withTargetDevice

setTargetPlatform

getTargetPlatform

withTargetPlatform

setCompilerOptions

getCompilerOptions

withCompilerOptions

setKmsKeyId

getKmsKeyId

withKmsKeyId

toString

equals

hashCode

clone

marshall