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AWS SDK for Java 1.x API Reference - 1.12.795

We announced the upcoming end-of-support for AWS SDK for Java (v1). We recommend that you migrate to AWS SDK for Java v2. For dates, additional details, and information on how to migrate, please refer to the linked announcement.
com.amazonaws.services.sagemaker.model

Class OutputConfig

  • All Implemented Interfaces:
    StructuredPojo, Serializable, Cloneable 

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

    Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.

    See Also:
    AWS API Documentation, Serialized Form

    • Constructor Summary

      Constructors
      Constructor and Description
      OutputConfig ()

    • Method Summary

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

    • Constructor Detail

      • OutputConfig

        public OutputConfig()

    • Method Detail

      • setS3OutputLocation

        public void setS3OutputLocation(String s3OutputLocation)

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

        Parameters:
        s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.

      • getS3OutputLocation

        public String getS3OutputLocation()

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

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

      • withS3OutputLocation

        public OutputConfig withS3OutputLocation(String s3OutputLocation)

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

        Parameters:
        s3OutputLocation - Identifies the S3 bucket where you want Amazon SageMaker to store the model artifacts. For example, s3://bucket-name/key-name-prefix.
        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • setTargetDevice

        public void setTargetDevice(String targetDevice)

        Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        Parameters:
        targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        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

      • getTargetDevice

        public String getTargetDevice()

        Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        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

      • withTargetDevice

        public OutputConfig withTargetDevice(String targetDevice)

        Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        Parameters:
        targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        TargetDevice

      • withTargetDevice

        public OutputConfig withTargetDevice(TargetDevice targetDevice)

        Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        Parameters:
        targetDevice - Identifies the target device or the machine learning instance that you want to run your model on after the compilation has completed. Alternatively, you can specify OS, architecture, and accelerator using TargetPlatform fields. It can be used instead of TargetPlatform.

        Currently ml_trn1 is available only in US East (N. Virginia) Region, and ml_inf2 is available only in US East (Ohio) Region.

        Returns:
        Returns a reference to this object so that method calls can be chained together.
        See Also:
        TargetDevice

      • setTargetPlatform

        public void setTargetPlatform(TargetPlatform targetPlatform)

        Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

        Parameters:
        targetPlatform - Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

      • getTargetPlatform

        public TargetPlatform getTargetPlatform()

        Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

        Returns:
        Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

      • withTargetPlatform

        public OutputConfig withTargetPlatform(TargetPlatform targetPlatform)

        Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

        Parameters:
        targetPlatform - Contains information about a target platform that you want your model to run on, such as OS, architecture, and accelerators. It is an alternative of TargetDevice.

        The following examples show how to configure the TargetPlatform and CompilerOptions JSON strings for popular target platforms:

        • Raspberry Pi 3 Model B+

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM_EABIHF"},

          "CompilerOptions": {'mattr': ['+neon']}

        • Jetson TX2

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'gpu-code': 'sm_62', 'trt-ver': '6.0.1', 'cuda-ver': '10.0'}

        • EC2 m5.2xlarge instance OS

          "TargetPlatform": {"Os": "LINUX", "Arch": "X86_64", "Accelerator": "NVIDIA"},

          "CompilerOptions": {'mcpu': 'skylake-avx512'}

        • RK3399

          "TargetPlatform": {"Os": "LINUX", "Arch": "ARM64", "Accelerator": "MALI"}

        • ARMv7 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM_EABI"},

          "CompilerOptions": {'ANDROID_PLATFORM': 25, 'mattr': ['+neon']}

        • ARMv8 phone (CPU)

          "TargetPlatform": {"Os": "ANDROID", "Arch": "ARM64"},

          "CompilerOptions": {'ANDROID_PLATFORM': 29}

        Returns:
        Returns a reference to this object so that method calls can be chained together.

      • setCompilerOptions

        public void setCompilerOptions(String compilerOptions)

        Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32".

          • int64: Use either "int64" or "long".

          For example, {"dtype" : "float32"}.

        • CPU: Compilation for CPU supports the following compiler options.

          • mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM: Details of ARM CPU compilations.

          • NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.

            For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code: Specifies the targeted architecture.

          • trt-ver: Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver: Specifies the CUDA version in x.y format.

          For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID: Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.

          • mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".

          For information about supported compiler options, see Neuron Compiler CLI 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"]}

        Parameters:
        compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32".

          • int64: Use either "int64" or "long".

          For example, {"dtype" : "float32"}.

        • CPU: Compilation for CPU supports the following compiler options.

          • mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM: Details of ARM CPU compilations.

          • NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.

            For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code: Specifies the targeted architecture.

          • trt-ver: Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver: Specifies the CUDA version in x.y format.

          For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID: Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.

          • mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".

          For information about supported compiler options, see Neuron Compiler CLI 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"]}

      • getCompilerOptions

        public String getCompilerOptions()

        Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32".

          • int64: Use either "int64" or "long".

          For example, {"dtype" : "float32"}.

        • CPU: Compilation for CPU supports the following compiler options.

          • mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM: Details of ARM CPU compilations.

          • NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.

            For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code: Specifies the targeted architecture.

          • trt-ver: Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver: Specifies the CUDA version in x.y format.

          For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID: Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.

          • mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".

          For information about supported compiler options, see Neuron Compiler CLI 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"]}

      • withCompilerOptions

        public OutputConfig withCompilerOptions(String compilerOptions)

        Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32".

          • int64: Use either "int64" or "long".

          For example, {"dtype" : "float32"}.

        • CPU: Compilation for CPU supports the following compiler options.

          • mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM: Details of ARM CPU compilations.

          • NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.

            For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code: Specifies the targeted architecture.

          • trt-ver: Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver: Specifies the CUDA version in x.y format.

          For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID: Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.

          • mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".

          For information about supported compiler options, see Neuron Compiler CLI 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"]}

        Parameters:
        compilerOptions - Specifies additional parameters for compiler options in JSON format. The compiler options are TargetPlatform specific. It is required for NVIDIA accelerators and highly recommended for CPU compilations. For any other cases, it is optional to specify CompilerOptions.

        • DTYPE: Specifies the data type for the input. When compiling for ml_* (except for ml_inf) instances using PyTorch framework, provide the data type (dtype) of the model's input. "float32" is used if "DTYPE" is not specified. Options for data type are:

          • float32: Use either "float" or "float32".

          • int64: Use either "int64" or "long".

          For example, {"dtype" : "float32"}.

        • CPU: Compilation for CPU supports the following compiler options.

          • mcpu: CPU micro-architecture. For example, {'mcpu': 'skylake-avx512'}

          • mattr: CPU flags. For example, {'mattr': ['+neon', '+vfpv4']}

        • ARM: Details of ARM CPU compilations.

          • NEON: NEON is an implementation of the Advanced SIMD extension used in ARMv7 processors.

            For example, add {'mattr': ['+neon']} to the compiler options if compiling for ARM 32-bit platform with the NEON support.

        • NVIDIA: Compilation for NVIDIA GPU supports the following compiler options.

          • gpu_code: Specifies the targeted architecture.

          • trt-ver: Specifies the TensorRT versions in x.y.z. format.

          • cuda-ver: Specifies the CUDA version in x.y format.

          For example, {'gpu-code': 'sm_72', 'trt-ver': '6.0.1', 'cuda-ver': '10.1'}

        • ANDROID: Compilation for the Android OS supports the following compiler options:

          • ANDROID_PLATFORM: Specifies the Android API levels. Available levels range from 21 to 29. For example, {'ANDROID_PLATFORM': 28}.

          • mattr: Add {'mattr': ['+neon']} to compiler options if compiling for ARM 32-bit platform with NEON support.

        • INFERENTIA: Compilation for target ml_inf1 uses compiler options passed in as a JSON string. For example, "CompilerOptions": "\"--verbose 1 --num-neuroncores 2 -O2\"".

          For information about supported compiler options, see Neuron Compiler CLI 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:
        Returns a reference to this object so that method calls can be chained together.

      • setKmsKeyId

        public void setKmsKeyId(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

        Parameters:
        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

      • getKmsKeyId

        public String getKmsKeyId()

        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

      • withKmsKeyId

        public OutputConfig withKmsKeyId(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

        Parameters:
        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:
        Returns a reference to this object so that method calls can be chained together.

      • toString

        public String toString()
        Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.
        Overrides:
        toString in class Object
        Returns:
        A string representation of this object.
        See Also:
        Object.toString()
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