Struct aws_sdk_sagemaker::types::builders::OutputConfigBuilder

#[non_exhaustive]
pub struct OutputConfigBuilder { /* private fields */ }

A builder for OutputConfig.

Implementations

impl OutputConfigBuilder

pub fn s3_output_location(self, input: impl Into<String>) -> Self

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

pub fn set_s3_output_location(self, input: Option<String>) -> Self

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

pub fn target_device(self, input: TargetDevice) -> Self

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.

pub fn set_target_device(self, input: Option<TargetDevice>) -> Self

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.

pub fn target_platform(self, input: TargetPlatform) -> Self

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}

pub fn set_target_platform(self, input: Option<TargetPlatform>) -> Self

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}

pub fn compiler_options(self, input: impl Into<String>) -> Self

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

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

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

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

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

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

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

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

• ARM: Details of ARM CPU compilations.

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

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

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

  • gpu_code: Specifies the targeted architecture.

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

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

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

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

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

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

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

  For information about supported compiler options, see Neuron Compiler CLI.

• CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:

  • class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.

• EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:

  • precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".

  • signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.

  • output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.

  For example: {"precision_mode": "FP32", "output_names": ["output:0"]}

pub fn set_compiler_options(self, input: Option<String>) -> Self

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

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

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

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

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

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

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

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

• ARM: Details of ARM CPU compilations.

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

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

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

  • gpu_code: Specifies the targeted architecture.

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

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

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

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

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

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

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

  For information about supported compiler options, see Neuron Compiler CLI.

• CoreML: Compilation for the CoreML OutputConfig$TargetDevice supports the following compiler options:

  • class_labels: Specifies the classification labels file name inside input tar.gz file. For example, {"class_labels": "imagenet_labels_1000.txt"}. Labels inside the txt file should be separated by newlines.

• EIA: Compilation for the Elastic Inference Accelerator supports the following compiler options:

  • precision_mode: Specifies the precision of compiled artifacts. Supported values are "FP16" and "FP32". Default is "FP32".

  • signature_def_key: Specifies the signature to use for models in SavedModel format. Defaults is TensorFlow's default signature def key.

  • output_names: Specifies a list of output tensor names for models in FrozenGraph format. Set at most one API field, either: signature_def_key or output_names.

  For example: {"precision_mode": "FP32", "output_names": ["output:0"]}

pub fn kms_key_id(self, input: impl Into<String>) -> Self

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

pub fn set_kms_key_id(self, input: Option<String>) -> Self

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

pub fn build(self) -> OutputConfig

Consumes the builder and constructs a OutputConfig.

Trait Implementations

impl Clone for OutputConfigBuilder

fn clone(&self) -> OutputConfigBuilder

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for OutputConfigBuilder

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for OutputConfigBuilder

fn default() -> OutputConfigBuilder

Returns the “default value” for a type.

impl PartialEq<OutputConfigBuilder> for OutputConfigBuilder

fn eq(&self, other: &OutputConfigBuilder) -> bool

This method tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

This method tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl StructuralPartialEq for OutputConfigBuilder