Struct aws_sdk_neptunedata::operation::create_ml_endpoint::builders::CreateMLEndpointFluentBuilder

pub struct CreateMLEndpointFluentBuilder { /* private fields */ }

Fluent builder constructing a request to CreateMLEndpoint.

Creates a new Neptune ML inference endpoint that lets you query one specific model that the model-training process constructed. See Managing inference endpoints using the endpoints command.

When invoking this operation in a Neptune cluster that has IAM authentication enabled, the IAM user or role making the request must have a policy attached that allows the neptune-db:CreateMLEndpoint IAM action in that cluster.

Implementations

impl CreateMLEndpointFluentBuilder

pub fn as_input(&self) -> &CreateMlEndpointInputBuilder

Access the CreateMLEndpoint as a reference.

pub async fn send( self ) -> Result<CreateMlEndpointOutput, SdkError<CreateMLEndpointError, HttpResponse>>

Sends the request and returns the response.

If an error occurs, an SdkError will be returned with additional details that can be matched against.

By default, any retryable failures will be retried twice. Retry behavior is configurable with the RetryConfig, which can be set when configuring the client.

pub fn customize( self ) -> CustomizableOperation<CreateMlEndpointOutput, CreateMLEndpointError, Self>

Consumes this builder, creating a customizable operation that can be modified before being sent.

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

A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

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

A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

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

A unique identifier for the new inference endpoint. The default is an autogenerated timestamped name.

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

The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The job Id of the completed model-training job that has created the model that the inference endpoint will point to. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The job Id of the completed model-transform job. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

pub fn update(self, input: bool) -> Self

If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

pub fn set_update(self, input: Option<bool>) -> Self

If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

pub fn get_update(&self) -> &Option<bool>

If set to true, update indicates that this is an update request. The default is false. You must supply either the mlModelTrainingJobId or the mlModelTransformJobId.

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

The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

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

The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

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

The ARN of an IAM role providing Neptune access to SageMaker and Amazon S3 resources. This must be listed in your DB cluster parameter group or an error will be thrown.

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

Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn. Valid values for knowledge graphs are: kge, transe, distmult, and rotate.

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

Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn. Valid values for knowledge graphs are: kge, transe, distmult, and rotate.

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

Model type for training. By default the Neptune ML model is automatically based on the modelType used in data processing, but you can specify a different model type here. The default is rgcn for heterogeneous graphs and kge for knowledge graphs. The only valid value for heterogeneous graphs is rgcn. Valid values for knowledge graphs are: kge, transe, distmult, and rotate.

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

The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

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

The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

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

The type of Neptune ML instance to use for online servicing. The default is ml.m5.xlarge. Choosing the ML instance for an inference endpoint depends on the task type, the graph size, and your budget.

pub fn instance_count(self, input: i32) -> Self

The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

pub fn set_instance_count(self, input: Option<i32>) -> Self

The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

pub fn get_instance_count(&self) -> &Option<i32>

The minimum number of Amazon EC2 instances to deploy to an endpoint for prediction. The default is 1

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

The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

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

The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

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

The Amazon Key Management Service (Amazon KMS) key that SageMaker uses to encrypt data on the storage volume attached to the ML compute instances that run the training job. The default is None.

Trait Implementations

impl Clone for CreateMLEndpointFluentBuilder

fn clone(&self) -> CreateMLEndpointFluentBuilder

Returns a copy of the value.

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

Performs copy-assignment from source.

impl Debug for CreateMLEndpointFluentBuilder

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

Formats the value using the given formatter.