Struct aws_sdk_sagemaker::types::builders::RecommendationJobContainerConfigBuilder

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

A builder for RecommendationJobContainerConfig.

Implementations

impl RecommendationJobContainerConfigBuilder

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

The machine learning domain of the model and its components.

Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

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

The machine learning domain of the model and its components.

Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

pub fn get_domain(&self) -> &Option<String>

The machine learning domain of the model and its components.

Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

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

The machine learning task that the model accomplishes.

Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER

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

The machine learning task that the model accomplishes.

Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER

pub fn get_task(&self) -> &Option<String>

The machine learning task that the model accomplishes.

Valid Values: IMAGE_CLASSIFICATION | OBJECT_DETECTION | TEXT_GENERATION | IMAGE_SEGMENTATION | FILL_MASK | CLASSIFICATION | REGRESSION | OTHER

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

The machine learning framework of the container image.

Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN

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

The machine learning framework of the container image.

Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN

pub fn get_framework(&self) -> &Option<String>

The machine learning framework of the container image.

Valid Values: TENSORFLOW | PYTORCH | XGBOOST | SAGEMAKER-SCIKIT-LEARN

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

The framework version of the container image.

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

The framework version of the container image.

pub fn get_framework_version(&self) -> &Option<String>

The framework version of the container image.

pub fn payload_config(self, input: RecommendationJobPayloadConfig) -> Self

Specifies the SamplePayloadUrl and all other sample payload-related fields.

pub fn set_payload_config( self, input: Option<RecommendationJobPayloadConfig> ) -> Self

Specifies the SamplePayloadUrl and all other sample payload-related fields.

pub fn get_payload_config(&self) -> &Option<RecommendationJobPayloadConfig>

Specifies the SamplePayloadUrl and all other sample payload-related fields.

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

The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet

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

The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet

pub fn get_nearest_model_name(&self) -> &Option<String>

The name of a pre-trained machine learning model benchmarked by Amazon SageMaker Inference Recommender that matches your model.

Valid Values: efficientnetb7 | unet | xgboost | faster-rcnn-resnet101 | nasnetlarge | vgg16 | inception-v3 | mask-rcnn | sagemaker-scikit-learn | densenet201-gluon | resnet18v2-gluon | xception | densenet201 | yolov4 | resnet152 | bert-base-cased | xceptionV1-keras | resnet50 | retinanet

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

Appends an item to supported_instance_types.

To override the contents of this collection use set_supported_instance_types.

A list of the instance types that are used to generate inferences in real-time.

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

A list of the instance types that are used to generate inferences in real-time.

pub fn get_supported_instance_types(&self) -> &Option<Vec<String>>

A list of the instance types that are used to generate inferences in real-time.

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

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.

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

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.

pub fn get_data_input_config(&self) -> &Option<String>

Specifies the name and shape of the expected data inputs for your trained model with a JSON dictionary form. This field is used for optimizing your model using SageMaker Neo. For more information, see DataInputConfig.

pub fn supported_endpoint_type( self, input: RecommendationJobSupportedEndpointType ) -> Self

The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.

pub fn set_supported_endpoint_type( self, input: Option<RecommendationJobSupportedEndpointType> ) -> Self

The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.

pub fn get_supported_endpoint_type( &self ) -> &Option<RecommendationJobSupportedEndpointType>

The endpoint type to receive recommendations for. By default this is null, and the results of the inference recommendation job return a combined list of both real-time and serverless benchmarks. By specifying a value for this field, you can receive a longer list of benchmarks for the desired endpoint type.

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

Appends an item to supported_response_mime_types.

To override the contents of this collection use set_supported_response_mime_types.

The supported MIME types for the output data.

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

The supported MIME types for the output data.

pub fn get_supported_response_mime_types(&self) -> &Option<Vec<String>>

The supported MIME types for the output data.

pub fn build(self) -> RecommendationJobContainerConfig

Consumes the builder and constructs a RecommendationJobContainerConfig.

Trait Implementations

impl Clone for RecommendationJobContainerConfigBuilder

fn clone(&self) -> RecommendationJobContainerConfigBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for RecommendationJobContainerConfigBuilder

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

Formats the value using the given formatter.

impl Default for RecommendationJobContainerConfigBuilder

fn default() -> RecommendationJobContainerConfigBuilder

Returns the “default value” for a type.

impl PartialEq for RecommendationJobContainerConfigBuilder

fn eq(&self, other: &RecommendationJobContainerConfigBuilder) -> 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 RecommendationJobContainerConfigBuilder