/*
* Copyright 2018-2023 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance with
* the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0
*
* or in the "license" file accompanying this file. This file is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
* CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions
* and limitations under the License.
*/
package com.amazonaws.services.sagemaker.model;
import java.io.Serializable;
import javax.annotation.Generated;
import com.amazonaws.protocol.StructuredPojo;
import com.amazonaws.protocol.ProtocolMarshaller;
/**
* <p>
* Contains information about the output location for the compiled model and the target device that the model runs on.
* <code>TargetDevice</code> and <code>TargetPlatform</code> 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
* <code>TargetDevice</code> list, use <code>TargetPlatform</code> to describe the platform of your edge device and
* <code>CompilerOptions</code> if there are specific settings that are required or recommended to use for particular
* TargetPlatform.
* </p>
*
* @see <a href="http://docs.aws.amazon.com/goto/WebAPI/sagemaker-2017-07-24/OutputConfig" target="_top">AWS API
* Documentation</a>
*/
@Generated("com.amazonaws:aws-java-sdk-code-generator")
public class OutputConfig implements Serializable, Cloneable, StructuredPojo {
/**
* <p>
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
* </p>
*/
private String s3OutputLocation;
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.
* </p>
* <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code> is
* available only in US East (Ohio) Region.
* </p>
* </note>
*/
private String targetDevice;
/**
* <p>
* 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 <code>TargetDevice</code>.
* </p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and <code>CompilerOptions</code>
* JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
* </ul>
*/
private TargetPlatform targetPlatform;
/**
* <p>
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for CPU
* compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code>
* </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except for
* <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's input.
* <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit platform
* with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For
* example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For example,
* <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be separated by
* newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults is
* TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set at most
* one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
* </ul>
*/
private String compilerOptions;
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed Encryption
* Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i>
* </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
* </ul>
*/
private String kmsKeyId;
/**
* <p>
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
* </p>
*
* @param s3OutputLocation
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
*/
public void setS3OutputLocation(String s3OutputLocation) {
this.s3OutputLocation = s3OutputLocation;
}
/**
* <p>
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
* </p>
*
* @return Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
*/
public String getS3OutputLocation() {
return this.s3OutputLocation;
}
/**
* <p>
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
* </p>
*
* @param s3OutputLocation
* Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example,
* <code>s3://bucket-name/key-name-prefix</code>.
* @return Returns a reference to this object so that method calls can be chained together.
*/
public OutputConfig withS3OutputLocation(String s3OutputLocation) {
setS3OutputLocation(s3OutputLocation);
return this;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.
* </p>
* <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code> is
* available only in US East (Ohio) Region.
* </p>
* </note>
*
* @param 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html"
* >TargetPlatform</a> fields. It can be used instead of <code>TargetPlatform</code>.</p> <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code>
* is available only in US East (Ohio) Region.
* </p>
* @see TargetDevice
*/
public void setTargetDevice(String targetDevice) {
this.targetDevice = targetDevice;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.
* </p>
* <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code> is
* available only in US East (Ohio) Region.
* </p>
* </note>
*
* @return 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 <a
* href
* ="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.</p> <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and
* <code>ml_inf2</code> is available only in US East (Ohio) Region.
* </p>
* @see TargetDevice
*/
public String getTargetDevice() {
return this.targetDevice;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.
* </p>
* <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code> is
* available only in US East (Ohio) Region.
* </p>
* </note>
*
* @param 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html"
* >TargetPlatform</a> fields. It can be used instead of <code>TargetPlatform</code>.</p> <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code>
* is available only in US East (Ohio) Region.
* </p>
* @return Returns a reference to this object so that method calls can be chained together.
* @see TargetDevice
*/
public OutputConfig withTargetDevice(String targetDevice) {
setTargetDevice(targetDevice);
return this;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html">TargetPlatform</a>
* fields. It can be used instead of <code>TargetPlatform</code>.
* </p>
* <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code> is
* available only in US East (Ohio) Region.
* </p>
* </note>
*
* @param 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 <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_TargetPlatform.html"
* >TargetPlatform</a> fields. It can be used instead of <code>TargetPlatform</code>.</p> <note>
* <p>
* Currently <code>ml_trn1</code> is available only in US East (N. Virginia) Region, and <code>ml_inf2</code>
* is available only in US East (Ohio) Region.
* </p>
* @return Returns a reference to this object so that method calls can be chained together.
* @see TargetDevice
*/
public OutputConfig withTargetDevice(TargetDevice targetDevice) {
this.targetDevice = targetDevice.toString();
return this;
}
/**
* <p>
* 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 <code>TargetDevice</code>.
* </p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and <code>CompilerOptions</code>
* JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
* </ul>
*
* @param 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 <code>TargetDevice</code>.</p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and
* <code>CompilerOptions</code> JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
*/
public void setTargetPlatform(TargetPlatform targetPlatform) {
this.targetPlatform = targetPlatform;
}
/**
* <p>
* 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 <code>TargetDevice</code>.
* </p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and <code>CompilerOptions</code>
* JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
* </ul>
*
* @return 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 <code>TargetDevice</code>.</p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and
* <code>CompilerOptions</code> JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
*/
public TargetPlatform getTargetPlatform() {
return this.targetPlatform;
}
/**
* <p>
* 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 <code>TargetDevice</code>.
* </p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and <code>CompilerOptions</code>
* JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
* </ul>
*
* @param 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 <code>TargetDevice</code>.</p>
* <p>
* The following examples show how to configure the <code>TargetPlatform</code> and
* <code>CompilerOptions</code> JSON strings for popular target platforms:
* </p>
* <ul>
* <li>
* <p>
* Raspberry Pi 3 Model B+
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* Jetson TX2
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}</code>
* </p>
* </li>
* <li>
* <p>
* EC2 m5.2xlarge instance OS
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* RK3399
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv7 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}</code>
* </p>
* </li>
* <li>
* <p>
* ARMv8 phone (CPU)
* </p>
* <p>
* <code>"TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},</code>
* </p>
* <p>
* <code> "CompilerOptions": {'ANDROID_PLATFORM': 29}</code>
* </p>
* </li>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public OutputConfig withTargetPlatform(TargetPlatform targetPlatform) {
setTargetPlatform(targetPlatform);
return this;
}
/**
* <p>
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for CPU
* compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code>
* </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except for
* <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's input.
* <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit platform
* with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For
* example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For example,
* <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be separated by
* newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults is
* TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set at most
* one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
* </ul>
*
* @param compilerOptions
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for
* CPU compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code> </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except
* for <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's
* input. <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit
* platform with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
* For example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For
* example, <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be
* separated by newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler
* options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults
* is TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set
* at most one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
*/
public void setCompilerOptions(String compilerOptions) {
this.compilerOptions = compilerOptions;
}
/**
* <p>
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for CPU
* compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code>
* </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except for
* <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's input.
* <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit platform
* with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For
* example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For example,
* <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be separated by
* newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults is
* TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set at most
* one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
* </ul>
*
* @return Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for
* CPU compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code> </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except
* for <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's
* input. <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type
* are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit
* platform with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29.
* For example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
* For example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For
* example, <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be
* separated by newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler
* options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults
* is TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set
* at most one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
*/
public String getCompilerOptions() {
return this.compilerOptions;
}
/**
* <p>
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for CPU
* compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code>
* </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except for
* <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's input.
* <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit platform
* with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For
* example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For example,
* <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be separated by
* newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults is
* TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set at most
* one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
* </ul>
*
* @param compilerOptions
* Specifies additional parameters for compiler options in JSON format. The compiler options are
* <code>TargetPlatform</code> specific. It is required for NVIDIA accelerators and highly recommended for
* CPU compilations. For any other cases, it is optional to specify <code>CompilerOptions.</code> </p>
* <ul>
* <li>
* <p>
* <code>DTYPE</code>: Specifies the data type for the input. When compiling for <code>ml_*</code> (except
* for <code>ml_inf</code>) instances using PyTorch framework, provide the data type (dtype) of the model's
* input. <code>"float32"</code> is used if <code>"DTYPE"</code> is not specified. Options for data type are:
* </p>
* <ul>
* <li>
* <p>
* float32: Use either <code>"float"</code> or <code>"float32"</code>.
* </p>
* </li>
* <li>
* <p>
* int64: Use either <code>"int64"</code> or <code>"long"</code>.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{"dtype" : "float32"}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>CPU</code>: Compilation for CPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>mcpu</code>: CPU micro-architecture. For example, <code>{'mcpu': 'skylake-avx512'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: CPU flags. For example, <code>{'mattr': ['+neon', '+vfpv4']}</code>
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>ARM</code>: Details of ARM CPU compilations.
* </p>
* <ul>
* <li>
* <p>
* <code>NEON</code>: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.
* </p>
* <p>
* For example, add <code>{'mattr': ['+neon']}</code> to the compiler options if compiling for ARM 32-bit
* platform with the NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>NVIDIA</code>: Compilation for NVIDIA GPU supports the following compiler options.
* </p>
* <ul>
* <li>
* <p>
* <code>gpu_code</code>: Specifies the targeted architecture.
* </p>
* </li>
* <li>
* <p>
* <code>trt-ver</code>: Specifies the TensorRT versions in x.y.z. format.
* </p>
* </li>
* <li>
* <p>
* <code>cuda-ver</code>: Specifies the CUDA version in x.y format.
* </p>
* </li>
* </ul>
* <p>
* For example, <code>{'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}</code>
* </p>
* </li>
* <li>
* <p>
* <code>ANDROID</code>: Compilation for the Android OS supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>ANDROID_PLATFORM</code>: Specifies the Android API levels. Available levels range from 21 to 29. For
* example, <code>{'ANDROID_PLATFORM': 28}</code>.
* </p>
* </li>
* <li>
* <p>
* <code>mattr</code>: Add <code>{'mattr': ['+neon']}</code> to compiler options if compiling for ARM 32-bit
* platform with NEON support.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>INFERENTIA</code>: Compilation for target ml_inf1 uses compiler options passed in as a JSON string.
* For example, <code>"CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\""</code>.
* </p>
* <p>
* For information about supported compiler options, see <a
* href="https://github.com/aws/aws-neuron-sdk/blob/master/docs/neuron-cc/command-line-reference.md"> Neuron
* Compiler CLI</a>.
* </p>
* </li>
* <li>
* <p>
* <code>CoreML</code>: Compilation for the CoreML <a
* href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_OutputConfig.html">OutputConfig</a>
* <code>TargetDevice</code> supports the following compiler options:
* </p>
* <ul>
* <li>
* <p>
* <code>class_labels</code>: Specifies the classification labels file name inside input tar.gz file. For
* example, <code>{"class_labels": "imagenet_labels_1000.txt"}</code>. Labels inside the txt file should be
* separated by newlines.
* </p>
* </li>
* </ul>
* </li>
* <li>
* <p>
* <code>EIA</code>: Compilation for the Elastic Inference Accelerator supports the following compiler
* options:
* </p>
* <ul>
* <li>
* <p>
* <code>precision_mode</code>: Specifies the precision of compiled artifacts. Supported values are
* <code>"FP16"</code> and <code>"FP32"</code>. Default is <code>"FP32"</code>.
* </p>
* </li>
* <li>
* <p>
* <code>signature_def_key</code>: Specifies the signature to use for models in SavedModel format. Defaults
* is TensorFlow's default signature def key.
* </p>
* </li>
* <li>
* <p>
* <code>output_names</code>: Specifies a list of output tensor names for models in FrozenGraph format. Set
* at most one API field, either: <code>signature_def_key</code> or <code>output_names</code>.
* </p>
* </li>
* </ul>
* <p>
* For example: <code>{"precision_mode": "FP32", "output_names": ["output:0"]}</code>
* </p>
* </li>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public OutputConfig withCompilerOptions(String compilerOptions) {
setCompilerOptions(compilerOptions);
return this;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed Encryption
* Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i>
* </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
* </ul>
*
* @param 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed
* Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
*/
public void setKmsKeyId(String kmsKeyId) {
this.kmsKeyId = kmsKeyId;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed Encryption
* Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i>
* </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
* </ul>
*
* @return 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed
* Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
*/
public String getKmsKeyId() {
return this.kmsKeyId;
}
/**
* <p>
* 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed Encryption
* Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i>
* </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
* </ul>
*
* @param 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 <a
* href="https://docs.aws.amazon.com/AmazonS3/latest/userguide/UsingKMSEncryption.html">KMS-Managed
* Encryption Keys</a> in the <i>Amazon Simple Storage Service Developer Guide.</i> </p>
* <p>
* The KmsKeyId can be any of the following formats:
* </p>
* <ul>
* <li>
* <p>
* Key ID: <code>1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Key ARN: <code>arn:aws:kms:us-west-2:111122223333:key/1234abcd-12ab-34cd-56ef-1234567890ab</code>
* </p>
* </li>
* <li>
* <p>
* Alias name: <code>alias/ExampleAlias</code>
* </p>
* </li>
* <li>
* <p>
* Alias name ARN: <code>arn:aws:kms:us-west-2:111122223333:alias/ExampleAlias</code>
* </p>
* </li>
* @return Returns a reference to this object so that method calls can be chained together.
*/
public OutputConfig withKmsKeyId(String kmsKeyId) {
setKmsKeyId(kmsKeyId);
return this;
}
/**
* 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.
*
* @return A string representation of this object.
*
* @see java.lang.Object#toString()
*/
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append("{");
if (getS3OutputLocation() != null)
sb.append("S3OutputLocation: ").append(getS3OutputLocation()).append(",");
if (getTargetDevice() != null)
sb.append("TargetDevice: ").append(getTargetDevice()).append(",");
if (getTargetPlatform() != null)
sb.append("TargetPlatform: ").append(getTargetPlatform()).append(",");
if (getCompilerOptions() != null)
sb.append("CompilerOptions: ").append(getCompilerOptions()).append(",");
if (getKmsKeyId() != null)
sb.append("KmsKeyId: ").append(getKmsKeyId());
sb.append("}");
return sb.toString();
}
@Override
public boolean equals(Object obj) {
if (this == obj)
return true;
if (obj == null)
return false;
if (obj instanceof OutputConfig == false)
return false;
OutputConfig other = (OutputConfig) obj;
if (other.getS3OutputLocation() == null ^ this.getS3OutputLocation() == null)
return false;
if (other.getS3OutputLocation() != null && other.getS3OutputLocation().equals(this.getS3OutputLocation()) == false)
return false;
if (other.getTargetDevice() == null ^ this.getTargetDevice() == null)
return false;
if (other.getTargetDevice() != null && other.getTargetDevice().equals(this.getTargetDevice()) == false)
return false;
if (other.getTargetPlatform() == null ^ this.getTargetPlatform() == null)
return false;
if (other.getTargetPlatform() != null && other.getTargetPlatform().equals(this.getTargetPlatform()) == false)
return false;
if (other.getCompilerOptions() == null ^ this.getCompilerOptions() == null)
return false;
if (other.getCompilerOptions() != null && other.getCompilerOptions().equals(this.getCompilerOptions()) == false)
return false;
if (other.getKmsKeyId() == null ^ this.getKmsKeyId() == null)
return false;
if (other.getKmsKeyId() != null && other.getKmsKeyId().equals(this.getKmsKeyId()) == false)
return false;
return true;
}
@Override
public int hashCode() {
final int prime = 31;
int hashCode = 1;
hashCode = prime * hashCode + ((getS3OutputLocation() == null) ? 0 : getS3OutputLocation().hashCode());
hashCode = prime * hashCode + ((getTargetDevice() == null) ? 0 : getTargetDevice().hashCode());
hashCode = prime * hashCode + ((getTargetPlatform() == null) ? 0 : getTargetPlatform().hashCode());
hashCode = prime * hashCode + ((getCompilerOptions() == null) ? 0 : getCompilerOptions().hashCode());
hashCode = prime * hashCode + ((getKmsKeyId() == null) ? 0 : getKmsKeyId().hashCode());
return hashCode;
}
@Override
public OutputConfig clone() {
try {
return (OutputConfig) super.clone();
} catch (CloneNotSupportedException e) {
throw new IllegalStateException("Got a CloneNotSupportedException from Object.clone() " + "even though we're Cloneable!", e);
}
}
@com.amazonaws.annotation.SdkInternalApi
@Override
public void marshall(ProtocolMarshaller protocolMarshaller) {
com.amazonaws.services.sagemaker.model.transform.OutputConfigMarshaller.getInstance().marshall(this, protocolMarshaller);
}
}