Package software.amazon.awssdk.services.sagemaker.model

Class OutputConfig

java.lang.Object

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:

Serialized Form

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()
String	compilerOptions()	Specifies additional parameters for compiler options in JSON format.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
String	kmsKeyId()	The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker AI uses to encrypt your output models with Amazon S3 server-side encryption after compilation job.
String	s3OutputLocation()	Identifies the S3 bucket where you want Amazon SageMaker AI to store the model artifacts.
Map<String,SdkField<?>>	sdkFieldNameToField()
List<SdkField<?>>	sdkFields()
static Class<? extends OutputConfig.Builder>	serializableBuilderClass()
TargetDevice	targetDevice()	Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
String	targetDeviceAsString()	Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed.
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()
String	toString()	Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

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

copy

Method Detail

s3OutputLocation

public final String s3OutputLocation()

Identifies the S3 bucket where you want Amazon SageMaker AI to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

Returns:

Identifies the S3 bucket where you want Amazon SageMaker AI 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.

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.

kmsKeyId

public final String kmsKeyId()

The Amazon Web Services Key Management Service key (Amazon Web Services KMS) that Amazon SageMaker AI 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 AI 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 AI 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 AI 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()

Specified by:

toBuilder in interface ToCopyableBuilder<OutputConfig.Builder,OutputConfig>

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)

Specified by:

equalsBySdkFields in interface SdkPojo

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

sdkFieldNameToField

public final Map<String,SdkField<?>> sdkFieldNameToField()

Specified by:

sdkFieldNameToField in interface SdkPojo