Struct CreateCompilationJobInput

#[non_exhaustive]
pub struct CreateCompilationJobInput {
    pub compilation_job_name: Option<String>,
    pub role_arn: Option<String>,
    pub model_package_version_arn: Option<String>,
    pub input_config: Option<InputConfig>,
    pub output_config: Option<OutputConfig>,
    pub vpc_config: Option<NeoVpcConfig>,
    pub stopping_condition: Option<StoppingCondition>,
    pub tags: Option<Vec<Tag>>,
}

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.

compilation_job_name: Option<String>

A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

role_arn: Option<String>

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

During model compilation, Amazon SageMaker AI needs your permission to:

• Read input data from an S3 bucket

• Write model artifacts to an S3 bucket

• Write logs to Amazon CloudWatch Logs

• Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

model_package_version_arn: Option<String>

The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

input_config: Option<InputConfig>

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

output_config: Option<OutputConfig>

Provides information about the output location for the compiled model and the target device the model runs on.

vpc_config: Option<NeoVpcConfig>

A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

stopping_condition: Option<StoppingCondition>

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.

tags: Option<Vec<Tag>>

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.

Implementations

impl CreateCompilationJobInput

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

A name for the model compilation job. The name must be unique within the Amazon Web Services Region and within your Amazon Web Services account.

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

The Amazon Resource Name (ARN) of an IAM role that enables Amazon SageMaker AI to perform tasks on your behalf.

During model compilation, Amazon SageMaker AI needs your permission to:

• Read input data from an S3 bucket

• Write model artifacts to an S3 bucket

• Write logs to Amazon CloudWatch Logs

• Publish metrics to Amazon CloudWatch

You grant permissions for all of these tasks to an IAM role. To pass this role to Amazon SageMaker AI, the caller of this API must have the iam:PassRole permission. For more information, see Amazon SageMaker AI Roles.

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

The Amazon Resource Name (ARN) of a versioned model package. Provide either a ModelPackageVersionArn or an InputConfig object in the request syntax. The presence of both objects in the CreateCompilationJob request will return an exception.

pub fn input_config(&self) -> Option<&InputConfig>

Provides information about the location of input model artifacts, the name and shape of the expected data inputs, and the framework in which the model was trained.

pub fn output_config(&self) -> Option<&OutputConfig>

Provides information about the output location for the compiled model and the target device the model runs on.

pub fn vpc_config(&self) -> Option<&NeoVpcConfig>

A VpcConfig object that specifies the VPC that you want your compilation job to connect to. Control access to your models by configuring the VPC. For more information, see Protect Compilation Jobs by Using an Amazon Virtual Private Cloud.

pub fn stopping_condition(&self) -> Option<&StoppingCondition>

Specifies a limit to how long a model compilation job can run. When the job reaches the time limit, Amazon SageMaker AI ends the compilation job. Use this API to cap model training costs.

pub fn tags(&self) -> &[Tag]

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.

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

impl CreateCompilationJobInput

pub fn builder() -> CreateCompilationJobInputBuilder

Creates a new builder-style object to manufacture CreateCompilationJobInput.

Trait Implementations

impl Clone for CreateCompilationJobInput

fn clone(&self) -> CreateCompilationJobInput

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for CreateCompilationJobInput

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

Formats the value using the given formatter.

impl PartialEq for CreateCompilationJobInput

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

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

const 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 CreateCompilationJobInput