software.amazon.awssdk.services.sagemaker.model.OutputConfig

All Implemented Interfaces:: Serializable, SdkPojo, ToCopyableBuilder<OutputConfig.Builder,OutputConfig>

@Generated("software.amazon.awssdk:codegen") public final class OutputConfig extends Object implements SdkPojo, Serializable, ToCopyableBuilder<OutputConfig.Builder,OutputConfig>

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:

Nested Class Summary

Nested Classes

Modifier and Type

Class

Description

static interface

OutputConfig.Builder
Method Summary

Modifier and Type

Method

Description

static OutputConfig.Builder

builder()

final String

compilerOptions()

Specifies additional parameters for compiler options in JSON format.

final boolean

equals(Object obj)

final boolean

equalsBySdkFields(Object obj)

Indicates whether some other object is "equal to" this one by SDK fields.

final <T> Optional<T>

getValueForField(String fieldName, Class<T> clazz)

final int

hashCode()

final String

kmsKeyId()

The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption after compilation job.

final String

s3OutputLocation()

Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts.

final List<SdkField<?>>

sdkFields()

static Class<? extends OutputConfig.Builder>

serializableBuilderClass()

final TargetDevice

targetDevice()

Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.

final String

targetDeviceAsString()

Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.

final TargetPlatform

targetPlatform()

Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators.

OutputConfig.Builder

toBuilder()

Take this object and create a builder that contains all of the current property values of this object.

final String

toString()

Returns a string representation of this object.

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

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder
copy

Method Details
- s3OutputLocation
  
  public final String s3OutputLocation()
  
  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.
- targetDevice
  public final 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.
  
  Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
  
  If the service returns an enum value that is not available in the current SDK version, targetDevice will return TargetDevice.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from targetDeviceAsString().
  
  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.
  
  Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
  
  See Also:
  
  TargetDevice
- targetDeviceAsString
  public final String targetDeviceAsString()
  
  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.
  
  Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
  
  If the service returns an enum value that is not available in the current SDK version, targetDevice will return TargetDevice.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from targetDeviceAsString().
  
  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.
  
  Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.
  
  See Also:
  
  TargetDevice
- targetPlatform
  public final 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}
  
  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}
- compilerOptions
  public final 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 Reference Guide.
  
  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 Reference Guide.
  
  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"]}
- kmsKeyId
  public final String kmsKeyId()
  
  The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption 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. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
  
  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 Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker uses to encrypt your output models with Amazon S3 server-side encryption 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. For more information, see KMS-Managed Encryption Keys in the Amazon Simple Storage Service Developer Guide.
  
  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
- toBuilder
  
  public OutputConfig.Builder toBuilder()
  
  Description copied from interface: ToCopyableBuilder
  
  Take this object and create a builder that contains all of the current property values of this object.
  
  Specified by:
  
  toBuilder in interface ToCopyableBuilder<OutputConfig.Builder,OutputConfig>
  
  Returns:
  
  a builder for type T
- builder
  
  public static OutputConfig.Builder builder()
- serializableBuilderClass
  
  public static Class<? extends OutputConfig.Builder> serializableBuilderClass()
- hashCode
  
  public final int hashCode()
  
  Overrides:
  
  hashCode in class Object
- equals
  
  public final boolean equals(Object obj)
  
  Overrides:
  
  equals in class Object
- equalsBySdkFields
  
  public final boolean equalsBySdkFields(Object obj)
  
  Description copied from interface: SdkPojo
  
  Indicates whether some other object is "equal to" this one by SDK fields. An SDK field is a modeled, non-inherited field in an SdkPojo class, and is generated based on a service model.
  If an SdkPojo class does not have any inherited fields, equalsBySdkFields and equals are essentially the same.
  
  Specified by:
  
  equalsBySdkFields in interface SdkPojo
  
  Parameters:
  
  obj - the object to be compared with
  
  Returns:
  
  true if the other object equals to this object by sdk fields, false otherwise.
- toString
  
  public final 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
- getValueForField
  
  public final <T> Optional<T> getValueForField(String fieldName, Class<T> clazz)
- sdkFields
  
  public final List<SdkField<?>> sdkFields()
  
  Specified by:
  
  sdkFields in interface SdkPojo
  
  Returns:
  
  List of SdkField in this POJO. May be empty list but should never be null.

Class OutputConfig

Nested Class Summary

Method Summary

Methods inherited from class java.lang.Object

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

Method Details

s3OutputLocation

targetDevice

targetDeviceAsString

targetPlatform

compilerOptions

kmsKeyId

toBuilder

builder

serializableBuilderClass

hashCode

equals

equalsBySdkFields

toString

getValueForField

sdkFields