Struct RecommendationJobContainerConfig

#[non_exhaustive]
pub struct RecommendationJobContainerConfig {
    pub domain: Option<String>,
    pub task: Option<String>,
    pub framework: Option<String>,
    pub framework_version: Option<String>,
    pub payload_config: Option<RecommendationJobPayloadConfig>,
    pub nearest_model_name: Option<String>,
    pub supported_instance_types: Option<Vec<String>>,
    pub supported_endpoint_type: Option<RecommendationJobSupportedEndpointType>,
    pub data_input_config: Option<String>,
    pub supported_response_mime_types: Option<Vec<String>>,
}

Specifies mandatory fields for running an Inference Recommender job directly in the CreateInferenceRecommendationsJob API. The fields specified in ContainerConfig override the corresponding fields in the model package. Use ContainerConfig if you want to specify these fields for the recommendation job but don't want to edit them in your model package.

Fields (Non-exhaustive)

Non-exhaustive structs could have additional fields added in future. Therefore, non-exhaustive structs cannot be constructed in external crates using the traditional Struct { .. } syntax; cannot be matched against without a wildcard ..; and struct update syntax will not work.

domain: Option<String>

The machine learning domain of the model and its components.

Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

task: 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

framework: Option<String>

The machine learning framework of the container image.

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

framework_version: Option<String>

The framework version of the container image.

payload_config: Option<RecommendationJobPayloadConfig>

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

nearest_model_name: 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

supported_instance_types: Option<Vec<String>>

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

supported_endpoint_type: 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.

data_input_config: 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.

supported_response_mime_types: Option<Vec<String>>

The supported MIME types for the output data.

Implementations

impl RecommendationJobContainerConfig

pub fn domain(&self) -> Option<&str>

The machine learning domain of the model and its components.

Valid Values: COMPUTER_VISION | NATURAL_LANGUAGE_PROCESSING | MACHINE_LEARNING

pub fn task(&self) -> Option<&str>

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) -> Option<&str>

The machine learning framework of the container image.

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

pub fn framework_version(&self) -> Option<&str>

The framework version of the container image.

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

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

pub fn nearest_model_name(&self) -> Option<&str>

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) -> &[String]

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

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supported_instance_types.is_none().

pub fn 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 data_input_config(&self) -> Option<&str>

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_response_mime_types(&self) -> &[String]

The supported MIME types for the output data.

If no value was sent for this field, a default will be set. If you want to determine if no value was sent, use .supported_response_mime_types.is_none().

impl RecommendationJobContainerConfig

pub fn builder() -> RecommendationJobContainerConfigBuilder

Creates a new builder-style object to manufacture RecommendationJobContainerConfig.

Trait Implementations

impl Clone for RecommendationJobContainerConfig

fn clone(&self) -> RecommendationJobContainerConfig

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for RecommendationJobContainerConfig

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

Formats the value using the given formatter.

impl PartialEq for RecommendationJobContainerConfig

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

Tests for self and other values to be equal, and is used by ==.

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

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

impl StructuralPartialEq for RecommendationJobContainerConfig