Struct aws_sdk_sagemaker::client::fluent_builders::CreateModel

pub struct CreateModel<C = DynConnector, M = AwsMiddleware, R = Standard> { /* fields omitted */ }

Fluent builder constructing a request to CreateModel.

Creates a model in Amazon SageMaker. In the request, you name the model and describe a primary container. For the primary container, you specify the Docker image that contains inference code, artifacts (from prior training), and a custom environment map that the inference code uses when you deploy the model for predictions.

Use this API to create a model if you want to use Amazon SageMaker hosting services or run a batch transform job.

To host your model, you create an endpoint configuration with the CreateEndpointConfig API, and then create an endpoint with the CreateEndpoint API. Amazon SageMaker then deploys all of the containers that you defined for the model in the hosting environment.

For an example that calls this method when deploying a model to Amazon SageMaker hosting services, see Deploy the Model to Amazon SageMaker Hosting Services (Amazon Web Services SDK for Python (Boto 3)).

To run a batch transform using your model, you start a job with the CreateTransformJob API. Amazon SageMaker uses your model and your dataset to get inferences which are then saved to a specified S3 location.

In the CreateModel request, you must define a container with the PrimaryContainer parameter.

In the request, you also provide an IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute hosting instances or for batch transform jobs. In addition, you also use the IAM role to manage permissions the inference code needs. For example, if the inference code access any other Amazon Web Services resources, you grant necessary permissions via this role.

Implementations

impl<C, M, R> CreateModel<C, M, R> where
    C: SmithyConnector,
    M: SmithyMiddleware<C>,
    R: NewRequestPolicy, 

pub async fn send(self) -> Result<CreateModelOutput, SdkError<CreateModelError>> where
    R::Policy: SmithyRetryPolicy<CreateModelInputOperationOutputAlias, CreateModelOutput, CreateModelError, CreateModelInputOperationRetryAlias>, 

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 model_name(self, inp: impl Into<String>) -> Self

The name of the new model.

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

The name of the new model.

pub fn primary_container(self, inp: ContainerDefinition) -> Self

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

pub fn set_primary_container(self, input: Option<ContainerDefinition>) -> Self

The location of the primary docker image containing inference code, associated artifacts, and custom environment map that the inference code uses when the model is deployed for predictions.

pub fn containers(self, inp: impl Into<ContainerDefinition>) -> Self

Appends an item to Containers.

To override the contents of this collection use set_containers.

Specifies the containers in the inference pipeline.

pub fn set_containers(self, input: Option<Vec<ContainerDefinition>>) -> Self

Specifies the containers in the inference pipeline.

pub fn inference_execution_config(self, inp: InferenceExecutionConfig) -> Self

Specifies details of how containers in a multi-container endpoint are called.

pub fn set_inference_execution_config(
    self,
    input: Option<InferenceExecutionConfig>
) -> Self

Specifies details of how containers in a multi-container endpoint are called.

pub fn execution_role_arn(self, inp: impl Into<String>) -> Self

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

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

The Amazon Resource Name (ARN) of the IAM role that Amazon SageMaker can assume to access model artifacts and docker image for deployment on ML compute instances or for batch transform jobs. Deploying on ML compute instances is part of model hosting. For more information, see Amazon SageMaker Roles.

To be able to pass this role to Amazon SageMaker, the caller of this API must have the iam:PassRole permission.

pub fn tags(self, inp: impl Into<Tag>) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

pub fn set_tags(self, input: Option<Vec<Tag>>) -> Self

An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web Services Resources.

pub fn vpc_config(self, inp: VpcConfig) -> Self

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

pub fn set_vpc_config(self, input: Option<VpcConfig>) -> Self

A VpcConfig object that specifies the VPC that you want your model to connect to. Control access to and from your model container by configuring the VPC. VpcConfig is used in hosting services and in batch transform. For more information, see Protect Endpoints by Using an Amazon Virtual Private Cloud and Protect Data in Batch Transform Jobs by Using an Amazon Virtual Private Cloud.

pub fn enable_network_isolation(self, inp: bool) -> Self

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

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

Isolates the model container. No inbound or outbound network calls can be made to or from the model container.

Trait Implementations

impl<C: Debug, M: Debug, R: Debug> Debug for CreateModel<C, M, R>

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

Formats the value using the given formatter.