Struct aws_sdk_lookoutvision::client::Client

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pub struct Client { /* private fields */ }

Expand description

Client for Amazon Lookout for Vision

Client for invoking operations on Amazon Lookout for Vision. Each operation on Amazon Lookout for Vision is a method on this this struct. .send() MUST be invoked on the generated operations to dispatch the request to the service.

Examples

Constructing a client and invoking an operation

    // create a shared configuration. This can be used & shared between multiple service clients.
    let shared_config = aws_config::load_from_env().await;
    let client = aws_sdk_lookoutvision::Client::new(&shared_config);
    // invoke an operation
    /* let rsp = client
        .<operation_name>().
        .<param>("some value")
        .send().await; */

Constructing a client with custom configuration

use aws_config::RetryConfig;
let shared_config = aws_config::load_from_env().await;
let config = aws_sdk_lookoutvision::config::Builder::from(&shared_config)
  .retry_config(RetryConfig::disabled())
  .build();
let client = aws_sdk_lookoutvision::Client::from_conf(config);

Implementations

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impl Client

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pub fn with_config(
client: Client<DynConnector, DynMiddleware<DynConnector>>,
conf: Config
) -> Self

Creates a client with the given service configuration.

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pub fn conf(&self) -> &Config

Returns the client’s configuration.

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impl Client

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pub fn create_dataset(&self) -> CreateDataset

Constructs a fluent builder for the CreateDataset operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project in which you want to create a dataset.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of the dataset. Specify train for a training dataset. Specify test for a test dataset.
- dataset_source(DatasetSource) / set_dataset_source(Option<DatasetSource>):
 The location of the manifest file that Amazon Lookout for Vision uses to create the dataset.
 
 If you don’t specify DatasetSource, an empty dataset is created and the operation synchronously returns. Later, you can add JSON Lines by calling UpdateDatasetEntries.
 
 If you specify a value for DataSource, the manifest at the S3 location is validated and used to create the dataset. The call to CreateDataset is asynchronous and might take a while to complete. To find out the current status, Check the value of Status returned in a call to DescribeDataset.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to CreateDataset completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateDataset. In this case, safely retry your call to CreateDataset by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple dataset creation requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateDataset. An idempotency token is active for 8 hours.
On success, responds with CreateDatasetOutput with field(s):
- dataset_metadata(Option<DatasetMetadata>):
 Information about the dataset.
On failure, responds with SdkError<CreateDatasetError>

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pub fn create_model(&self) -> CreateModel

Constructs a fluent builder for the CreateModel operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project in which you want to create a model version.
- description(impl Into<String>) / set_description(Option<String>):
 A description for the version of the model.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to CreateModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateModel. In this case, safely retry your call to CreateModel by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from starting multiple training jobs. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateModel. An idempotency token is active for 8 hours.
- output_config(OutputConfig) / set_output_config(Option<OutputConfig>):
 The location where Amazon Lookout for Vision saves the training results.
- kms_key_id(impl Into<String>) / set_kms_key_id(Option<String>):
 The identifier for your AWS KMS key. The key is used to encrypt training and test images copied into the service for model training. Your source images are unaffected. If this parameter is not specified, the copied images are encrypted by a key that AWS owns and manages.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 A set of tags (key-value pairs) that you want to attach to the model.
On success, responds with CreateModelOutput with field(s):
- model_metadata(Option<ModelMetadata>):
 The response from a call to CreateModel.
On failure, responds with SdkError<CreateModelError>

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pub fn create_project(&self) -> CreateProject

Constructs a fluent builder for the CreateProject operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name for the project.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to CreateProject completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from CreateProject. In this case, safely retry your call to CreateProject by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple project creation requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to CreateProject. An idempotency token is active for 8 hours.
On success, responds with CreateProjectOutput with field(s):
- project_metadata(Option<ProjectMetadata>):
 Information about the project.
On failure, responds with SdkError<CreateProjectError>

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pub fn delete_dataset(&self) -> DeleteDataset

Constructs a fluent builder for the DeleteDataset operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the dataset that you want to delete.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of the dataset to delete. Specify train to delete the training dataset. Specify test to delete the test dataset. To delete the dataset in a single dataset project, specify train.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to DeleteDataset completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from DeleteDataset. In this case, safely retry your call to DeleteDataset by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple deletetion requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteDataset. An idempotency token is active for 8 hours.
On success, responds with DeleteDatasetOutput
On failure, responds with SdkError<DeleteDatasetError>

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pub fn delete_model(&self) -> DeleteModel

Constructs a fluent builder for the DeleteModel operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model that you want to delete.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model that you want to delete.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to DeleteModel completes only once. You choose the value to pass. For example, an issue might prevent you from getting a response from DeleteModel. In this case, safely retry your call to DeleteModel by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple model deletion requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteModel. An idempotency token is active for 8 hours.
On success, responds with DeleteModelOutput with field(s):
- model_arn(Option<String>):
 The Amazon Resource Name (ARN) of the model that was deleted.
On failure, responds with SdkError<DeleteModelError>

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pub fn delete_project(&self) -> DeleteProject

Constructs a fluent builder for the DeleteProject operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project to delete.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to DeleteProject completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from DeleteProject. In this case, safely retry your call to DeleteProject by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple project deletion requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to DeleteProject. An idempotency token is active for 8 hours.
On success, responds with DeleteProjectOutput with field(s):
- project_arn(Option<String>):
 The Amazon Resource Name (ARN) of the project that was deleted.
On failure, responds with SdkError<DeleteProjectError>

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pub fn describe_dataset(&self) -> DescribeDataset

Constructs a fluent builder for the DescribeDataset operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the dataset that you want to describe.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of the dataset to describe. Specify train to describe the training dataset. Specify test to describe the test dataset. If you have a single dataset project, specify train
On success, responds with DescribeDatasetOutput with field(s):
- dataset_description(Option<DatasetDescription>):
 The description of the requested dataset.
On failure, responds with SdkError<DescribeDatasetError>

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pub fn describe_model(&self) -> DescribeModel

Constructs a fluent builder for the DescribeModel operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The project that contains the version of a model that you want to describe.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model that you want to describe.
On success, responds with DescribeModelOutput with field(s):
- model_description(Option<ModelDescription>):
 Contains the description of the model.
On failure, responds with SdkError<DescribeModelError>

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pub fn describe_model_packaging_job(&self) -> DescribeModelPackagingJob

Constructs a fluent builder for the DescribeModelPackagingJob operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model packaging job that you want to describe.
- job_name(impl Into<String>) / set_job_name(Option<String>):
 The job name for the model packaging job.
On success, responds with DescribeModelPackagingJobOutput with field(s):
- model_packaging_description(Option<ModelPackagingDescription>):
 The description of the model packaging job.
On failure, responds with SdkError<DescribeModelPackagingJobError>

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pub fn describe_project(&self) -> DescribeProject

Constructs a fluent builder for the DescribeProject operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that you want to describe.
On success, responds with DescribeProjectOutput with field(s):
- project_description(Option<ProjectDescription>):
 The description of the project.
On failure, responds with SdkError<DescribeProjectError>

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pub fn detect_anomalies(&self) -> DetectAnomalies

Constructs a fluent builder for the DetectAnomalies operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model version that you want to use.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model that you want to use.
- body(byte_stream::ByteStream) / set_body(byte_stream::ByteStream):
 The unencrypted image bytes that you want to analyze.
- content_type(impl Into<String>) / set_content_type(Option<String>):
 The type of the image passed in Body. Valid values are image/png (PNG format images) and image/jpeg (JPG format images).
On success, responds with DetectAnomaliesOutput with field(s):
- detect_anomaly_result(Option<DetectAnomalyResult>):
 The results of the DetectAnomalies operation.
On failure, responds with SdkError<DetectAnomaliesError>

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pub fn list_dataset_entries(&self) -> ListDatasetEntries

Constructs a fluent builder for the ListDatasetEntries operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the dataset that you want to list.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of the dataset that you want to list. Specify train to list the training dataset. Specify test to list the test dataset. If you have a single dataset project, specify train.
- labeled(bool) / set_labeled(Option<bool>):
 Specify true to include labeled entries, otherwise specify false. If you don’t specify a value, Lookout for Vision returns all entries.
- anomaly_class(impl Into<String>) / set_anomaly_class(Option<String>):
 Specify normal to include only normal images. Specify anomaly to only include anomalous entries. If you don’t specify a value, Amazon Lookout for Vision returns normal and anomalous images.
- before_creation_date(DateTime) / set_before_creation_date(Option<DateTime>):
 Only includes entries before the specified date in the response. For example, 2020-06-23T00:00:00.
- after_creation_date(DateTime) / set_after_creation_date(Option<DateTime>):
 Only includes entries after the specified date in the response. For example, 2020-06-23T00:00:00.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of dataset entries.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
- source_ref_contains(impl Into<String>) / set_source_ref_contains(Option<String>):
 Perform a “contains” search on the values of the source-ref key within the dataset. For example a value of “IMG_17” returns all JSON Lines where the source-ref key value matches IMG_17.
On success, responds with ListDatasetEntriesOutput with field(s):
- dataset_entries(Option<Vec<String>>):
 A list of the entries (JSON Lines) within the dataset.
- next_token(Option<String>):
 If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set ofdataset entries.
On failure, responds with SdkError<ListDatasetEntriesError>

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pub fn list_model_packaging_jobs(&self) -> ListModelPackagingJobs

Constructs a fluent builder for the ListModelPackagingJobs operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project for which you want to list the model packaging jobs.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 If the previous response was incomplete (because there is more results to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
On success, responds with ListModelPackagingJobsOutput with field(s):
- model_packaging_jobs(Option<Vec<ModelPackagingJobMetadata>>):
 A list of the model packaging jobs created for the specified Amazon Lookout for Vision project.
- next_token(Option<String>):
 If the previous response was incomplete (because there is more results to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of results.
On failure, responds with SdkError<ListModelPackagingJobsError>

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pub fn list_models(&self) -> ListModels

Constructs a fluent builder for the ListModels operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model versions that you want to list.
- next_token(impl Into<String>) / set_next_token(Option<String>):
 If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of models.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
On success, responds with ListModelsOutput with field(s):
- models(Option<Vec<ModelMetadata>>):
 A list of model versions in the specified project.
- next_token(Option<String>):
 If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set of models.
On failure, responds with SdkError<ListModelsError>

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pub fn list_projects(&self) -> ListProjects

Constructs a fluent builder for the ListProjects operation. This operation supports pagination; See into_paginator().

The fluent builder is configurable:
- next_token(impl Into<String>) / set_next_token(Option<String>):
 If the previous response was incomplete (because there is more data to retrieve), Amazon Lookout for Vision returns a pagination token in the response. You can use this pagination token to retrieve the next set of projects.
- max_results(i32) / set_max_results(Option<i32>):
 The maximum number of results to return per paginated call. The largest value you can specify is 100. If you specify a value greater than 100, a ValidationException error occurs. The default value is 100.
On success, responds with ListProjectsOutput with field(s):
- projects(Option<Vec<ProjectMetadata>>):
 A list of projects in your AWS account.
- next_token(Option<String>):
 If the response is truncated, Amazon Lookout for Vision returns this token that you can use in the subsequent request to retrieve the next set of projects.
On failure, responds with SdkError<ListProjectsError>

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pub fn list_tags_for_resource(&self) -> ListTagsForResource

Constructs a fluent builder for the ListTagsForResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The Amazon Resource Name (ARN) of the model for which you want to list tags.
On success, responds with ListTagsForResourceOutput with field(s):
- tags(Option<Vec<Tag>>):
 A map of tag keys and values attached to the specified model.
On failure, responds with SdkError<ListTagsForResourceError>

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pub fn start_model(&self) -> StartModel

Constructs a fluent builder for the StartModel operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model that you want to start.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model that you want to start.
- min_inference_units(i32) / set_min_inference_units(Option<i32>):
 The minimum number of inference units to use. A single inference unit represents 1 hour of processing. Use a higher number to increase the TPS throughput of your model. You are charged for the number of inference units that you use.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to StartModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModel. In this case, safely retry your call to StartModel by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple start requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModel. An idempotency token is active for 8 hours.
On success, responds with StartModelOutput with field(s):
- status(Option<ModelHostingStatus>):
 The current running status of the model.
On failure, responds with SdkError<StartModelError>

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pub fn start_model_packaging_job(&self) -> StartModelPackagingJob

Constructs a fluent builder for the StartModelPackagingJob operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project which contains the version of the model that you want to package.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model within the project that you want to package.
- job_name(impl Into<String>) / set_job_name(Option<String>):
 A name for the model packaging job. If you don’t supply a value, the service creates a job name for you.
- configuration(ModelPackagingConfiguration) / set_configuration(Option<ModelPackagingConfiguration>):
 The configuration for the model packaging job.
- description(impl Into<String>) / set_description(Option<String>):
 A description for the model packaging job.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to StartModelPackagingJob completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StartModelPackagingJob. In this case, safely retry your call to StartModelPackagingJob by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple dataset creation requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StartModelPackagingJob. An idempotency token is active for 8 hours.
On success, responds with StartModelPackagingJobOutput with field(s):
- job_name(Option<String>):
 The job name for the model packaging job. If you don’t supply a job name in the JobName input parameter, the service creates a job name for you.
On failure, responds with SdkError<StartModelPackagingJobError>

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pub fn stop_model(&self) -> StopModel

Constructs a fluent builder for the StopModel operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the model that you want to stop.
- model_version(impl Into<String>) / set_model_version(Option<String>):
 The version of the model that you want to stop.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to StopModel completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from StopModel. In this case, safely retry your call to StopModel by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple stop requests. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to StopModel. An idempotency token is active for 8 hours.
On success, responds with StopModelOutput with field(s):
- status(Option<ModelHostingStatus>):
 The status of the model.
On failure, responds with SdkError<StopModelError>

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pub fn tag_resource(&self) -> TagResource

Constructs a fluent builder for the TagResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The Amazon Resource Name (ARN) of the model to assign the tags.
- tags(Vec<Tag>) / set_tags(Option<Vec<Tag>>):
 The key-value tags to assign to the model.
On success, responds with TagResourceOutput
On failure, responds with SdkError<TagResourceError>

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pub fn untag_resource(&self) -> UntagResource

Constructs a fluent builder for the UntagResource operation.

The fluent builder is configurable:
- resource_arn(impl Into<String>) / set_resource_arn(Option<String>):
 The Amazon Resource Name (ARN) of the model from which you want to remove tags.
- tag_keys(Vec<String>) / set_tag_keys(Option<Vec<String>>):
 A list of the keys of the tags that you want to remove.
On success, responds with UntagResourceOutput
On failure, responds with SdkError<UntagResourceError>

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pub fn update_dataset_entries(&self) -> UpdateDatasetEntries

Constructs a fluent builder for the UpdateDatasetEntries operation.

The fluent builder is configurable:
- project_name(impl Into<String>) / set_project_name(Option<String>):
 The name of the project that contains the dataset that you want to update.
- dataset_type(impl Into<String>) / set_dataset_type(Option<String>):
 The type of the dataset that you want to update. Specify train to update the training dataset. Specify test to update the test dataset. If you have a single dataset project, specify train.
- changes(Blob) / set_changes(Option<Blob>):
 The entries to add to the dataset.
- client_token(impl Into<String>) / set_client_token(Option<String>):
 ClientToken is an idempotency token that ensures a call to UpdateDatasetEntries completes only once. You choose the value to pass. For example, An issue might prevent you from getting a response from UpdateDatasetEntries. In this case, safely retry your call to UpdateDatasetEntries by using the same ClientToken parameter value.
 
 If you don’t supply a value for ClientToken, the AWS SDK you are using inserts a value for you. This prevents retries after a network error from making multiple updates with the same dataset entries. You’ll need to provide your own value for other use cases.
 
 An error occurs if the other input parameters are not the same as in the first request. Using a different value for ClientToken is considered a new call to UpdateDatasetEntries. An idempotency token is active for 8 hours.
On success, responds with UpdateDatasetEntriesOutput with field(s):
- status(Option<DatasetStatus>):
 The status of the dataset update.
On failure, responds with SdkError<UpdateDatasetEntriesError>