Client — AWS SDK for Ruby V3 (original) (raw)

Constructor Details

#initialize(options) ⇒ Client

Returns a new instance of Client.

Instance Method Details

#add_tags(params = {}) ⇒ Types::AddTagsOutput

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.

#create_batch_prediction(params = {}) ⇒ Types::CreateBatchPredictionOutput

Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource. This operation creates a new BatchPrediction, and uses an MLModeland the data files referenced by the DataSource as information sources.

CreateBatchPrediction is an asynchronous operation. In response toCreateBatchPrediction, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction status toPENDING. After the BatchPrediction completes, Amazon ML sets the status to COMPLETED.

You can poll for status updates by using the GetBatchPrediction operation and checking the Status parameter of the result. After theCOMPLETED status appears, the results are available in the location specified by the OutputUri parameter.

#create_data_source_from_rds(params = {}) ⇒ Types::CreateDataSourceFromRDSOutput

Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS). A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, orCreateBatchPrediction operations.

CreateDataSourceFromRDS is an asynchronous operation. In response toCreateDataSourceFromRDS, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETEDor PENDING state can be used only to perform >CreateMLModel>,CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML cannot accept the input source, it sets the Statusparameter to FAILED and includes an error message in the Messageattribute of the GetDataSource operation response.

#create_data_source_from_redshift(params = {}) ⇒ Types::CreateDataSourceFromRedshiftOutput

Creates a DataSource from a database hosted on an Amazon Redshift cluster. A DataSource references data that can be used to perform either CreateMLModel, CreateEvaluation, or CreateBatchPredictionoperations.

CreateDataSourceFromRedshift is an asynchronous operation. In response to CreateDataSourceFromRedshift, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status toPENDING. After the DataSource is created and ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource inCOMPLETED or PENDING states can be used to perform onlyCreateMLModel, CreateEvaluation, or CreateBatchPredictionoperations.

If Amazon ML can't accept the input source, it sets the Statusparameter to FAILED and includes an error message in the Messageattribute of the GetDataSource operation response.

The observations should be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQueryquery. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query toS3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSourceto train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training anMLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

#create_data_source_from_s3(params = {}) ⇒ Types::CreateDataSourceFromS3Output

Creates a DataSource object. A DataSource references data that can be used to perform CreateMLModel, CreateEvaluation, orCreateBatchPrediction operations.

CreateDataSourceFromS3 is an asynchronous operation. In response toCreateDataSourceFromS3, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource status to PENDING. After the DataSource has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in theCOMPLETED or PENDING state can be used to perform onlyCreateMLModel, CreateEvaluation or CreateBatchPredictionoperations.

If Amazon ML can't accept the input source, it sets the Statusparameter to FAILED and includes an error message in the Messageattribute of the GetDataSource operation response.

The observation data used in a DataSource should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more .csv files in an Amazon Simple Storage Service (Amazon S3) location, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource.

After the DataSource has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSourceto train an MLModel, the DataSource also needs a recipe. A recipe describes how each input variable will be used in training anMLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

#create_evaluation(params = {}) ⇒ Types::CreateEvaluationOutput

Creates a new Evaluation of an MLModel. An MLModel is evaluated on a set of observations associated to a DataSource. Like aDataSource for an MLModel, the DataSource for an Evaluationcontains values for the Target Variable. The Evaluation compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModelfunctions on the test data. Evaluation generates a relevant performance metric, such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType:BINARY, REGRESSION or MULTICLASS.

CreateEvaluation is an asynchronous operation. In response toCreateEvaluation, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING. After theEvaluation is created and ready for use, Amazon ML sets the status to COMPLETED.

You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.

#create_ml_model(params = {}) ⇒ Types::CreateMLModelOutput

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only theMLModelName and the ScoreThreshold in an MLModel without creating a new MLModel.

CreateMLModel is an asynchronous operation. In response toCreateMLModel, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING. After theMLModel has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the progress of theMLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true inCreateDataSourceFromRDS, CreateDataSourceFromS3, orCreateDataSourceFromRedshift operations.

#create_realtime_endpoint(params = {}) ⇒ Types::CreateRealtimeEndpointOutput

Creates a real-time endpoint for the MLModel. The endpoint contains the URI of the MLModel; that is, the location to send real-time prediction requests for the specified MLModel.

#delete_batch_prediction(params = {}) ⇒ Types::DeleteBatchPredictionOutput

Assigns the DELETED status to a BatchPrediction, rendering it unusable.

After using the DeleteBatchPrediction operation, you can use the GetBatchPrediction operation to verify that the status of theBatchPrediction changed to DELETED.

Caution: The result of the DeleteBatchPrediction operation is irreversible.

#delete_data_source(params = {}) ⇒ Types::DeleteDataSourceOutput

Assigns the DELETED status to a DataSource, rendering it unusable.

After using the DeleteDataSource operation, you can use the GetDataSource operation to verify that the status of the DataSourcechanged to DELETED.

Caution: The results of the DeleteDataSource operation are irreversible.

#delete_evaluation(params = {}) ⇒ Types::DeleteEvaluationOutput

Assigns the DELETED status to an Evaluation, rendering it unusable.

After invoking the DeleteEvaluation operation, you can use theGetEvaluation operation to verify that the status of theEvaluation changed to DELETED.

Caution: The results of the DeleteEvaluation operation are irreversible.

#delete_ml_model(params = {}) ⇒ Types::DeleteMLModelOutput

Assigns the DELETED status to an MLModel, rendering it unusable.

After using the DeleteMLModel operation, you can use theGetMLModel operation to verify that the status of the MLModelchanged to DELETED.

Caution: The result of the DeleteMLModel operation is irreversible.

#delete_realtime_endpoint(params = {}) ⇒ Types::DeleteRealtimeEndpointOutput

Deletes a real time endpoint of an MLModel.

#delete_tags(params = {}) ⇒ Types::DeleteTagsOutput

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.

If you specify a tag that doesn't exist, Amazon ML ignores it.

#describe_batch_predictions(params = {}) ⇒ Types::DescribeBatchPredictionsOutput

Returns a list of BatchPrediction operations that match the search criteria in the request.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

The following waiters are defined for this operation (see #wait_until for detailed usage):

• batch_prediction_available

#describe_data_sources(params = {}) ⇒ Types::DescribeDataSourcesOutput

Returns a list of DataSource that match the search criteria in the request.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

The following waiters are defined for this operation (see #wait_until for detailed usage):

• data_source_available

#describe_evaluations(params = {}) ⇒ Types::DescribeEvaluationsOutput

Returns a list of DescribeEvaluations that match the search criteria in the request.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

The following waiters are defined for this operation (see #wait_until for detailed usage):

• evaluation_available

#describe_ml_models(params = {}) ⇒ Types::DescribeMLModelsOutput

Returns a list of MLModel that match the search criteria in the request.

The returned response is a pageable response and is Enumerable. For details on usage see PageableResponse.

The following waiters are defined for this operation (see #wait_until for detailed usage):

• ml_model_available

#describe_tags(params = {}) ⇒ Types::DescribeTagsOutput

Describes one or more of the tags for your Amazon ML object.

#get_batch_prediction(params = {}) ⇒ Types::GetBatchPredictionOutput

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

#get_data_source(params = {}) ⇒ Types::GetDataSourceOutput

Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource.

GetDataSource provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.

#get_evaluation(params = {}) ⇒ Types::GetEvaluationOutput

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

#get_ml_model(params = {}) ⇒ Types::GetMLModelOutput

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

#predict(params = {}) ⇒ Types::PredictOutput

Generates a prediction for the observation using the specified ML Model.

Note: Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

#update_batch_prediction(params = {}) ⇒ Types::UpdateBatchPredictionOutput

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

#update_data_source(params = {}) ⇒ Types::UpdateDataSourceOutput

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

#update_evaluation(params = {}) ⇒ Types::UpdateEvaluationOutput

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

#update_ml_model(params = {}) ⇒ Types::UpdateMLModelOutput

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean

Polls an API operation until a resource enters a desired state.

Basic Usage

A waiter will call an API operation until:

• It is successful
• It enters a terminal state
• It makes the maximum number of attempts

In between attempts, the waiter will sleep.

# polls in a loop, sleeping between attempts
client.wait_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You can pass configuration as the final arguments hash.

# poll for ~25 seconds
client.wait_until(waiter_name, params, {
  max_attempts: 5,
  delay: 5,
})

Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(waiter_name, params, {

  # disable max attempts
  max_attempts: nil,

  # poll for 1 hour, instead of a number of attempts
  before_wait: -> (attempts, response) do
    throw :failure if Time.now - started_at > 3600
  end
})

Handling Errors

When a waiter is unsuccessful, it will raise an error. All of the failure errors extend fromWaiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end

Valid Waiters

The following table lists the valid waiter names, the operations they call, and the default :delay and :max_attempts values.