aws_sdk_sagemaker::operation::create_model_package::builders

Struct CreateModelPackageFluentBuilder

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pub struct CreateModelPackageFluentBuilder { /* private fields */ }
Expand description

Fluent builder constructing a request to CreateModelPackage.

Creates a model package that you can use to create SageMaker models or list on Amazon Web Services Marketplace, or a versioned model that is part of a model group. Buyers can subscribe to model packages listed on Amazon Web Services Marketplace to create models in SageMaker.

To create a model package by specifying a Docker container that contains your inference code and the Amazon S3 location of your model artifacts, provide values for InferenceSpecification. To create a model from an algorithm resource that you created or subscribed to in Amazon Web Services Marketplace, provide a value for SourceAlgorithmSpecification.

There are two types of model packages:

  • Versioned - a model that is part of a model group in the model registry.

  • Unversioned - a model package that is not part of a model group.

Implementations§

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impl CreateModelPackageFluentBuilder

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pub fn as_input(&self) -> &CreateModelPackageInputBuilder

Access the CreateModelPackage as a reference.

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pub async fn send( self, ) -> Result<CreateModelPackageOutput, SdkError<CreateModelPackageError, 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.

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pub fn customize( self, ) -> CustomizableOperation<CreateModelPackageOutput, CreateModelPackageError, Self>

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

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

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

The name of the model package. The name must have 1 to 63 characters. Valid characters are a-z, A-Z, 0-9, and - (hyphen).

This parameter is required for unversioned models. It is not applicable to versioned models.

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

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

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

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

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

The name or Amazon Resource Name (ARN) of the model package group that this model version belongs to.

This parameter is required for versioned models, and does not apply to unversioned models.

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

A description of the model package.

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

A description of the model package.

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

A description of the model package.

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pub fn inference_specification(self, input: InferenceSpecification) -> Self

Specifies details about inference jobs that you can run with models based on this model package, including the following information:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

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pub fn set_inference_specification( self, input: Option<InferenceSpecification>, ) -> Self

Specifies details about inference jobs that you can run with models based on this model package, including the following information:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

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pub fn get_inference_specification(&self) -> &Option<InferenceSpecification>

Specifies details about inference jobs that you can run with models based on this model package, including the following information:

  • The Amazon ECR paths of containers that contain the inference code and model artifacts.

  • The instance types that the model package supports for transform jobs and real-time endpoints used for inference.

  • The input and output content formats that the model package supports for inference.

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pub fn validation_specification( self, input: ModelPackageValidationSpecification, ) -> Self

Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

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pub fn set_validation_specification( self, input: Option<ModelPackageValidationSpecification>, ) -> Self

Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

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pub fn get_validation_specification( &self, ) -> &Option<ModelPackageValidationSpecification>

Specifies configurations for one or more transform jobs that SageMaker runs to test the model package.

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pub fn source_algorithm_specification( self, input: SourceAlgorithmSpecification, ) -> Self

Details about the algorithm that was used to create the model package.

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pub fn set_source_algorithm_specification( self, input: Option<SourceAlgorithmSpecification>, ) -> Self

Details about the algorithm that was used to create the model package.

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pub fn get_source_algorithm_specification( &self, ) -> &Option<SourceAlgorithmSpecification>

Details about the algorithm that was used to create the model package.

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

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

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

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

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

Whether to certify the model package for listing on Amazon Web Services Marketplace.

This parameter is optional for unversioned models, and does not apply to versioned models.

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pub fn tags(self, input: Tag) -> Self

Appends an item to Tags.

To override the contents of this collection use set_tags.

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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pub fn get_tags(&self) -> &Option<Vec<Tag>>

A list of key value pairs associated with the model. For more information, see Tagging Amazon Web Services resources in the Amazon Web Services General Reference Guide.

If you supply ModelPackageGroupName, your model package belongs to the model group you specify and uses the tags associated with the model group. In this case, you cannot supply a tag argument.

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pub fn model_approval_status(self, input: ModelApprovalStatus) -> Self

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

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pub fn set_model_approval_status( self, input: Option<ModelApprovalStatus>, ) -> Self

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

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pub fn get_model_approval_status(&self) -> &Option<ModelApprovalStatus>

Whether the model is approved for deployment.

This parameter is optional for versioned models, and does not apply to unversioned models.

For versioned models, the value of this parameter must be set to Approved to deploy the model.

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pub fn metadata_properties(self, input: MetadataProperties) -> Self

Metadata properties of the tracking entity, trial, or trial component.

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pub fn set_metadata_properties(self, input: Option<MetadataProperties>) -> Self

Metadata properties of the tracking entity, trial, or trial component.

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pub fn get_metadata_properties(&self) -> &Option<MetadataProperties>

Metadata properties of the tracking entity, trial, or trial component.

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pub fn model_metrics(self, input: ModelMetrics) -> Self

A structure that contains model metrics reports.

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pub fn set_model_metrics(self, input: Option<ModelMetrics>) -> Self

A structure that contains model metrics reports.

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pub fn get_model_metrics(&self) -> &Option<ModelMetrics>

A structure that contains model metrics reports.

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

A unique token that guarantees that the call to this API is idempotent.

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

A unique token that guarantees that the call to this API is idempotent.

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

A unique token that guarantees that the call to this API is idempotent.

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

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

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

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

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

The machine learning domain of your model package and its components. Common machine learning domains include computer vision and natural language processing.

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

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

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

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

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

The machine learning task your model package accomplishes. Common machine learning tasks include object detection and image classification. The following tasks are supported by Inference Recommender: "IMAGE_CLASSIFICATION" | "OBJECT_DETECTION" | "TEXT_GENERATION" |"IMAGE_SEGMENTATION" | "FILL_MASK" | "CLASSIFICATION" | "REGRESSION" | "OTHER".

Specify "OTHER" if none of the tasks listed fit your use case.

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

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

The Amazon Simple Storage Service (Amazon S3) path where the sample payload is stored. This path must point to a single gzip compressed tar archive (.tar.gz suffix). This archive can hold multiple files that are all equally used in the load test. Each file in the archive must satisfy the size constraints of the InvokeEndpoint call.

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pub fn customer_metadata_properties( self, k: impl Into<String>, v: impl Into<String>, ) -> Self

Adds a key-value pair to CustomerMetadataProperties.

To override the contents of this collection use set_customer_metadata_properties.

The metadata properties associated with the model package versions.

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

The metadata properties associated with the model package versions.

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pub fn get_customer_metadata_properties( &self, ) -> &Option<HashMap<String, String>>

The metadata properties associated with the model package versions.

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pub fn drift_check_baselines(self, input: DriftCheckBaselines) -> Self

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

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pub fn set_drift_check_baselines( self, input: Option<DriftCheckBaselines>, ) -> Self

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

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pub fn get_drift_check_baselines(&self) -> &Option<DriftCheckBaselines>

Represents the drift check baselines that can be used when the model monitor is set using the model package. For more information, see the topic on Drift Detection against Previous Baselines in SageMaker Pipelines in the Amazon SageMaker Developer Guide.

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pub fn additional_inference_specifications( self, input: AdditionalInferenceSpecificationDefinition, ) -> Self

Appends an item to AdditionalInferenceSpecifications.

To override the contents of this collection use set_additional_inference_specifications.

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

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pub fn set_additional_inference_specifications( self, input: Option<Vec<AdditionalInferenceSpecificationDefinition>>, ) -> Self

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

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pub fn get_additional_inference_specifications( &self, ) -> &Option<Vec<AdditionalInferenceSpecificationDefinition>>

An array of additional Inference Specification objects. Each additional Inference Specification specifies artifacts based on this model package that can be used on inference endpoints. Generally used with SageMaker Neo to store the compiled artifacts.

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pub fn skip_model_validation(self, input: SkipModelValidation) -> Self

Indicates if you want to skip model validation.

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pub fn set_skip_model_validation( self, input: Option<SkipModelValidation>, ) -> Self

Indicates if you want to skip model validation.

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pub fn get_skip_model_validation(&self) -> &Option<SkipModelValidation>

Indicates if you want to skip model validation.

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

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

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

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

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

The URI of the source for the model package. If you want to clone a model package, set it to the model package Amazon Resource Name (ARN). If you want to register a model, set it to the model ARN.

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pub fn security_config(self, input: ModelPackageSecurityConfig) -> Self

The KMS Key ID (KMSKeyId) used for encryption of model package information.

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pub fn set_security_config( self, input: Option<ModelPackageSecurityConfig>, ) -> Self

The KMS Key ID (KMSKeyId) used for encryption of model package information.

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pub fn get_security_config(&self) -> &Option<ModelPackageSecurityConfig>

The KMS Key ID (KMSKeyId) used for encryption of model package information.

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pub fn model_card(self, input: ModelPackageModelCard) -> Self

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

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pub fn set_model_card(self, input: Option<ModelPackageModelCard>) -> Self

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

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pub fn get_model_card(&self) -> &Option<ModelPackageModelCard>

The model card associated with the model package. Since ModelPackageModelCard is tied to a model package, it is a specific usage of a model card and its schema is simplified compared to the schema of ModelCard. The ModelPackageModelCard schema does not include model_package_details, and model_overview is composed of the model_creator and model_artifact properties. For more information about the model package model card schema, see Model package model card schema. For more information about the model card associated with the model package, see View the Details of a Model Version.

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pub fn model_life_cycle(self, input: ModelLifeCycle) -> Self

A structure describing the current state of the model in its life cycle.

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pub fn set_model_life_cycle(self, input: Option<ModelLifeCycle>) -> Self

A structure describing the current state of the model in its life cycle.

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pub fn get_model_life_cycle(&self) -> &Option<ModelLifeCycle>

A structure describing the current state of the model in its life cycle.

Trait Implementations§

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impl Clone for CreateModelPackageFluentBuilder

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fn clone(&self) -> CreateModelPackageFluentBuilder

Returns a duplicate of the value. Read more
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fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source. Read more
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impl Debug for CreateModelPackageFluentBuilder

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

Formats the value using the given formatter. Read more

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Returns self with the bg() set to [Color :: BrightYellow].

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fn on_bright_blue(&self) -> Painted<&T>

Returns self with the bg() set to [Color :: BrightBlue].

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Returns self with the bg() set to [Color :: BrightMagenta].

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Returns self with the bg() set to [Color :: BrightCyan].

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Returns self with the bg() set to [Color :: BrightWhite].

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fn attr(&self, value: Attribute) -> Painted<&T>

Enables the styling Attribute value.

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Make text bold using using bold().

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fn bold(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Bold].

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Returns self with the attr() set to [Attribute :: Dim].

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Returns self with the attr() set to [Attribute :: Italic].

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Returns self with the attr() set to [Attribute :: Underline].

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Returns self with the attr() set to [Attribute :: Blink].

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fn invert(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Invert].

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fn conceal(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Conceal].

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fn strike(&self) -> Painted<&T>

Returns self with the attr() set to [Attribute :: Strike].

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fn quirk(&self, value: Quirk) -> Painted<&T>

Enables the yansi Quirk value.

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Enable wrapping using wrap().

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Returns self with the quirk() set to [Quirk :: Mask].

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Returns self with the quirk() set to [Quirk :: Wrap].

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Returns self with the quirk() set to [Quirk :: Linger].

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fn clear(&self) -> Painted<&T>

👎Deprecated since 1.0.1: renamed to resetting() due to conflicts with Vec::clear(). The clear() method will be removed in a future release.

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fn resetting(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: Resetting].

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Returns self with the quirk() set to [Quirk :: Bright].

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fn on_bright(&self) -> Painted<&T>

Returns self with the quirk() set to [Quirk :: OnBright].

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fn whenever(&self, value: Condition) -> Painted<&T>

Conditionally enable styling based on whether the Condition value applies. Replaces any previous condition.

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Enable styling painted only when both stdout and stderr are TTYs:

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fn new(self) -> Painted<Self>
where Self: Sized,

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where S: Into<Style>,

Apply a style wholesale to self. Any previous style is replaced. Read more
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type Output = T

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type Owned = T

The resulting type after obtaining ownership.
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fn to_owned(&self) -> T

Creates owned data from borrowed data, usually by cloning. Read more
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Uses borrowed data to replace owned data, usually by cloning. Read more
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impl<T, U> TryFrom<U> for T
where U: Into<T>,

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type Error = Infallible

The type returned in the event of a conversion error.
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fn try_from(value: U) -> Result<T, <T as TryFrom<U>>::Error>

Performs the conversion.
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impl<T, U> TryInto<U> for T
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type Error = <U as TryFrom<T>>::Error

The type returned in the event of a conversion error.
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where S: Into<Dispatch>,

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impl<T> ErasedDestructor for T
where T: 'static,