MachineLearning
Service object for interacting with AWS MachineLearning service.
public struct MachineLearning: AWSService
Definition of the public APIs exposed by Amazon Machine Learning
Inheritance
AWSService
Initializers
init(client:region:partition:endpoint:timeout:byteBufferAllocator:options:)
Initialize the MachineLearning client
public init(
client: AWSClient,
region: SotoCore.Region? = nil,
partition: AWSPartition = .aws,
endpoint: String? = nil,
timeout: TimeAmount? = nil,
byteBufferAllocator: ByteBufferAllocator = ByteBufferAllocator(),
options: AWSServiceConfig.Options = []
)
Parameters
- client: AWSClient used to process requests
- region: Region of server you want to communicate with. This will override the partition parameter.
- partition: AWS partition where service resides, standard (.aws), china (.awscn), government (.awsusgov).
- endpoint: Custom endpoint URL to use instead of standard AWS servers
- timeout: Timeout value for HTTP requests
init(from:patch:)
Initializer required by AWSService.with(middlewares:timeout:byteBufferAllocator:options)
. You are not able to use this initializer directly as there are no public initializers for AWSServiceConfig.Patch
. Please use AWSService.with(middlewares:timeout:byteBufferAllocator:options)
instead.
public init(from: MachineLearning, patch: AWSServiceConfig.Patch)
Properties
client
Client used for communication with AWS
public let client: AWSClient
config
Service configuration
public let config: AWSServiceConfig
Methods
addTags(_:logger:on:)
public func addTags(_ input: AddTagsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.
createBatchPrediction(_:logger:on:)
public func createBatchPrediction(_ input: CreateBatchPredictionInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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 MLModel
and the data
files referenced by the DataSource
as information sources.
<p>
<code>CreateBatchPrediction</code> is an asynchronous operation. In response to <code>CreateBatchPrediction</code>,
Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>BatchPrediction</code> status to <code>PENDING</code>.
After the <code>BatchPrediction</code> completes, Amazon ML sets the status to <code>COMPLETED</code>.
</p>
<p>You can poll for status updates by using the <a>GetBatchPrediction</a> operation and checking the <code>Status</code> parameter of the result. After the <code>COMPLETED</code> status appears,
the results are available in the location specified by the <code>OutputUri</code> parameter.</p>
createDataSourceFromRDS(_:logger:on:)
public func createDataSourceFromRDS(_ input: CreateDataSourceFromRDSInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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
, or CreateBatchPrediction
operations.
<p>
<code>CreateDataSourceFromRDS</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRDS</code>,
Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>.
After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>.
<code>DataSource</code> in the <code>COMPLETED</code> or <code>PENDING</code> state can
be used only to perform <code>>CreateMLModel</code>>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
</p>
<p>
If Amazon ML cannot accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code> attribute of the <code>GetDataSource</code> operation response.
</p>
createDataSourceFromRedshift(_:logger:on:)
public func createDataSourceFromRedshift(_ input: CreateDataSourceFromRedshiftInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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 CreateBatchPrediction
operations.
<p>
<code>CreateDataSourceFromRedshift</code> is an asynchronous operation. In response to <code>CreateDataSourceFromRedshift</code>, Amazon Machine Learning (Amazon ML) immediately returns and sets the <code>DataSource</code> status to <code>PENDING</code>.
After the <code>DataSource</code> is created and ready for use, Amazon ML sets the <code>Status</code> parameter to <code>COMPLETED</code>.
<code>DataSource</code> in <code>COMPLETED</code> or <code>PENDING</code> states can be
used to perform only <code>CreateMLModel</code>, <code>CreateEvaluation</code>, or <code>CreateBatchPrediction</code> operations.
</p>
<p>
If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to <code>FAILED</code> and includes an error message in the <code>Message</code>
attribute of the <code>GetDataSource</code> operation response.
</p>
<p>The observations should be contained in the database hosted on an Amazon Redshift cluster
and should be specified by a <code>SelectSqlQuery</code> query. Amazon ML executes an
<code>Unload</code> command in Amazon Redshift to transfer the result set of
the <code>SelectSqlQuery</code> query to <code>S3StagingLocation</code>.</p>
<p>After the <code>DataSource</code> has been created, it's ready for use in evaluations and
batch predictions. If you plan to use the <code>DataSource</code> to train an
<code>MLModel</code>, the <code>DataSource</code> also requires a recipe. A recipe
describes how each input variable will be used in training an <code>MLModel</code>. 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.</p>
<p>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
<code>GetDataSource</code> for an existing datasource and copy the values to a
<code>CreateDataSource</code> call. Change the settings that you want to change and
make sure that all required fields have the appropriate values.</p>
createDataSourceFromS3(_:logger:on:)
public func createDataSourceFromS3(_ input: CreateDataSourceFromS3Input, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateDataSourceFromS3Output>
Creates a DataSource
object. A DataSource
references data that
can be used to perform CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
<p>
<code>CreateDataSourceFromS3</code> is an asynchronous operation. In response to
<code>CreateDataSourceFromS3</code>, Amazon Machine Learning (Amazon ML) immediately
returns and sets the <code>DataSource</code> status to <code>PENDING</code>. After the
<code>DataSource</code> has been created and is ready for use, Amazon ML sets the
<code>Status</code> parameter to <code>COMPLETED</code>. <code>DataSource</code> in
the <code>COMPLETED</code> or <code>PENDING</code> state can be used to perform only
<code>CreateMLModel</code>, <code>CreateEvaluation</code> or
<code>CreateBatchPrediction</code> operations. </p>
<p> If Amazon ML can't accept the input source, it sets the <code>Status</code> parameter to
<code>FAILED</code> and includes an error message in the <code>Message</code>
attribute of the <code>GetDataSource</code> operation response. </p>
<p>The observation data used in a <code>DataSource</code> 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 <code>DataSource</code>. </p>
<p>After the <code>DataSource</code> has been created, it's ready to use in evaluations and
batch predictions. If you plan to use the <code>DataSource</code> to train an
<code>MLModel</code>, the <code>DataSource</code> also needs a recipe. A recipe
describes how each input variable will be used in training an <code>MLModel</code>. 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.</p>
createEvaluation(_:logger:on:)
public func createEvaluation(_ input: CreateEvaluationInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateEvaluationOutput>
Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a set of observations associated to a DataSource
. Like a DataSource
for an MLModel
, the DataSource
for an Evaluation
contains 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 MLModel
functions 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 to CreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately
returns and sets the evaluation status to PENDING
. After the Evaluation
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.
createMLModel(_:logger:on:)
public func createMLModel(_ input: CreateMLModelInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<CreateMLModelOutput>
Creates a new MLModel
using the DataSource
and the recipe as
information sources.
An MLModel
is nearly immutable. Users can update only the
MLModelName
and the ScoreThreshold
in an
MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to
CreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns
and sets the MLModel
status to PENDING
. After the
MLModel
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 the
MLModel
during the creation operation.
CreateMLModel
requires a DataSource
with computed statistics,
which can be created by setting ComputeStatistics
to true
in
CreateDataSourceFromRDS
, CreateDataSourceFromS3
, or
CreateDataSourceFromRedshift
operations.
createRealtimeEndpoint(_:logger:on:)
public func createRealtimeEndpoint(_ input: CreateRealtimeEndpointInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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
.
deleteBatchPrediction(_:logger:on:)
public func deleteBatchPrediction(_ input: DeleteBatchPredictionInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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 the BatchPrediction
changed to DELETED.
<p>
<b>Caution:</b> The result of the <code>DeleteBatchPrediction</code> operation is irreversible.</p>
deleteDataSource(_:logger:on:)
public func deleteDataSource(_ input: DeleteDataSourceInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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 DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
deleteEvaluation(_:logger:on:)
public func deleteEvaluation(_ input: DeleteEvaluationInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteEvaluationOutput>
Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the
GetEvaluation
operation to verify that the status of the Evaluation
changed to DELETED
.
Caution: The results of the DeleteEvaluation
operation are irreversible.
deleteMLModel(_:logger:on:)
public func deleteMLModel(_ input: DeleteMLModelInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteMLModelOutput>
Assigns the DELETED
status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the
GetMLModel
operation to verify that the status of the MLModel
changed to DELETED.
<p>
<b>Caution:</b> The result of the <code>DeleteMLModel</code> operation is irreversible.</p>
deleteRealtimeEndpoint(_:logger:on:)
public func deleteRealtimeEndpoint(_ input: DeleteRealtimeEndpointInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DeleteRealtimeEndpointOutput>
Deletes a real time endpoint of an MLModel
.
deleteTags(_:logger:on:)
public func deleteTags(_ input: DeleteTagsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.
describeBatchPredictions(_:logger:on:)
public func describeBatchPredictions(_ input: DescribeBatchPredictionsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeBatchPredictionsOutput>
Returns a list of BatchPrediction
operations that match the search criteria in the request.
describeDataSources(_:logger:on:)
public func describeDataSources(_ input: DescribeDataSourcesInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeDataSourcesOutput>
Returns a list of DataSource
that match the search criteria in the request.
describeEvaluations(_:logger:on:)
public func describeEvaluations(_ input: DescribeEvaluationsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeEvaluationsOutput>
Returns a list of DescribeEvaluations
that match the search criteria in the request.
describeMLModels(_:logger:on:)
public func describeMLModels(_ input: DescribeMLModelsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeMLModelsOutput>
Returns a list of MLModel
that match the search criteria in the request.
describeTags(_:logger:on:)
public func describeTags(_ input: DescribeTagsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<DescribeTagsOutput>
Describes one or more of the tags for your Amazon ML object.
getBatchPrediction(_:logger:on:)
public func getBatchPrediction(_ input: GetBatchPredictionInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetBatchPredictionOutput>
Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a
Batch Prediction
request.
getDataSource(_:logger:on:)
public func getDataSource(_ input: GetDataSourceInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.
getEvaluation(_:logger:on:)
public func getEvaluation(_ input: GetEvaluationInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<GetEvaluationOutput>
Returns an Evaluation
that includes metadata as well as the current status of the Evaluation
.
getMLModel(_:logger:on:)
public func getMLModel(_ input: GetMLModelInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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(_:logger:on:)
public func predict(_ input: PredictInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.
updateBatchPrediction(_:logger:on:)
public func updateBatchPrediction(_ input: UpdateBatchPredictionInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UpdateBatchPredictionOutput>
Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction
operation to view the contents of the updated data element.
updateDataSource(_:logger:on:)
public func updateDataSource(_ input: UpdateDataSourceInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UpdateDataSourceOutput>
Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource
operation to view the contents of the updated data element.
updateEvaluation(_:logger:on:)
public func updateEvaluation(_ input: UpdateEvaluationInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<UpdateEvaluationOutput>
Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation
operation to view the contents of the updated data element.
updateMLModel(_:logger:on:)
public func updateMLModel(_ input: UpdateMLModelInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil) -> EventLoopFuture<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.
describeBatchPredictionsPaginator(_:logger:on:)
compiler(>=5.5.2) && canImport(_Concurrency)
public func describeBatchPredictionsPaginator( _ input: DescribeBatchPredictionsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil ) -> AWSClient.PaginatorSequence<DescribeBatchPredictionsInput, DescribeBatchPredictionsOutput>
Returns a list of
Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process onBatchPrediction
operations that match the search criteria in the request.
describeDataSourcesPaginator(_:logger:on:)
compiler(>=5.5.2) && canImport(_Concurrency)
public func describeDataSourcesPaginator( _ input: DescribeDataSourcesInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil ) -> AWSClient.PaginatorSequence<DescribeDataSourcesInput, DescribeDataSourcesOutput>
Returns a list of
Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process onDataSource
that match the search criteria in the request.
describeEvaluationsPaginator(_:logger:on:)
compiler(>=5.5.2) && canImport(_Concurrency)
public func describeEvaluationsPaginator( _ input: DescribeEvaluationsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil ) -> AWSClient.PaginatorSequence<DescribeEvaluationsInput, DescribeEvaluationsOutput>
Returns a list of
Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process onDescribeEvaluations
that match the search criteria in the request.
describeMLModelsPaginator(_:logger:on:)
compiler(>=5.5.2) && canImport(_Concurrency)
public func describeMLModelsPaginator( _ input: DescribeMLModelsInput, logger: Logger = AWSClient.loggingDisabled, on eventLoop: EventLoop? = nil ) -> AWSClient.PaginatorSequence<DescribeMLModelsInput, DescribeMLModelsOutput>
Returns a list of
Return PaginatorSequence for operation. - Parameters: - input: Input for request - logger: Logger used flot logging - eventLoop: EventLoop to run this process onMLModel
that match the search criteria in the request.
describeBatchPredictionsPaginator(_:_:logger:on:onPage:)
Provide paginated results to closure onPage
for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result
.
public func describeBatchPredictionsPaginator<Result>(
_ input: DescribeBatchPredictionsInput,
_ initialValue: Result,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (Result, DescribeBatchPredictionsOutput, EventLoop) -> EventLoopFuture<(Bool, Result)>
) -> EventLoopFuture<Result>
Returns a list of BatchPrediction
operations that match the search criteria in the request.
Parameters:
- input: Input for request
- initialValue: The value to use as the initial accumulating value.
initialValue
is passed toonPage
the first time it is called.
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.
describeBatchPredictionsPaginator(_:logger:on:onPage:)
Provide paginated results to closure onPage
.
public func describeBatchPredictionsPaginator(
_ input: DescribeBatchPredictionsInput,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (DescribeBatchPredictionsOutput, EventLoop) -> EventLoopFuture<Bool>
) -> EventLoopFuture<Void>
Parameters
- input: Input for request
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.
describeDataSourcesPaginator(_:_:logger:on:onPage:)
Provide paginated results to closure onPage
for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result
.
public func describeDataSourcesPaginator<Result>(
_ input: DescribeDataSourcesInput,
_ initialValue: Result,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (Result, DescribeDataSourcesOutput, EventLoop) -> EventLoopFuture<(Bool, Result)>
) -> EventLoopFuture<Result>
Returns a list of DataSource
that match the search criteria in the request.
Parameters:
- input: Input for request
- initialValue: The value to use as the initial accumulating value.
initialValue
is passed toonPage
the first time it is called.
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.
describeDataSourcesPaginator(_:logger:on:onPage:)
Provide paginated results to closure onPage
.
public func describeDataSourcesPaginator(
_ input: DescribeDataSourcesInput,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (DescribeDataSourcesOutput, EventLoop) -> EventLoopFuture<Bool>
) -> EventLoopFuture<Void>
Parameters
- input: Input for request
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.
describeEvaluationsPaginator(_:_:logger:on:onPage:)
Provide paginated results to closure onPage
for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result
.
public func describeEvaluationsPaginator<Result>(
_ input: DescribeEvaluationsInput,
_ initialValue: Result,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (Result, DescribeEvaluationsOutput, EventLoop) -> EventLoopFuture<(Bool, Result)>
) -> EventLoopFuture<Result>
Returns a list of DescribeEvaluations
that match the search criteria in the request.
Parameters:
- input: Input for request
- initialValue: The value to use as the initial accumulating value.
initialValue
is passed toonPage
the first time it is called.
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.
describeEvaluationsPaginator(_:logger:on:onPage:)
Provide paginated results to closure onPage
.
public func describeEvaluationsPaginator(
_ input: DescribeEvaluationsInput,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (DescribeEvaluationsOutput, EventLoop) -> EventLoopFuture<Bool>
) -> EventLoopFuture<Void>
Parameters
- input: Input for request
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.
describeMLModelsPaginator(_:_:logger:on:onPage:)
Provide paginated results to closure onPage
for it to combine them into one result. This works in a similar manner to Array.reduce<Result>(_:_:) -> Result
.
public func describeMLModelsPaginator<Result>(
_ input: DescribeMLModelsInput,
_ initialValue: Result,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (Result, DescribeMLModelsOutput, EventLoop) -> EventLoopFuture<(Bool, Result)>
) -> EventLoopFuture<Result>
Returns a list of MLModel
that match the search criteria in the request.
Parameters:
- input: Input for request
- initialValue: The value to use as the initial accumulating value.
initialValue
is passed toonPage
the first time it is called.
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each paginated response. It combines an accumulating result with the contents of response. This combined result is then returned along with a boolean indicating if the paginate operation should continue.
describeMLModelsPaginator(_:logger:on:onPage:)
Provide paginated results to closure onPage
.
public func describeMLModelsPaginator(
_ input: DescribeMLModelsInput,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil,
onPage: @escaping (DescribeMLModelsOutput, EventLoop) -> EventLoopFuture<Bool>
) -> EventLoopFuture<Void>
Parameters
- input: Input for request
- logger: Logger used flot logging
- eventLoop: EventLoop to run this process on
- onPage: closure called with each block of entries. Returns boolean indicating whether we should continue.
waitUntilBatchPredictionAvailable(_:maxWaitTime:logger:on:)
public func waitUntilBatchPredictionAvailable(
_ input: DescribeBatchPredictionsInput,
maxWaitTime: TimeAmount? = nil,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil
) -> EventLoopFuture<Void>
waitUntilDataSourceAvailable(_:maxWaitTime:logger:on:)
public func waitUntilDataSourceAvailable(
_ input: DescribeDataSourcesInput,
maxWaitTime: TimeAmount? = nil,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil
) -> EventLoopFuture<Void>
waitUntilEvaluationAvailable(_:maxWaitTime:logger:on:)
public func waitUntilEvaluationAvailable(
_ input: DescribeEvaluationsInput,
maxWaitTime: TimeAmount? = nil,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil
) -> EventLoopFuture<Void>
waitUntilMLModelAvailable(_:maxWaitTime:logger:on:)
public func waitUntilMLModelAvailable(
_ input: DescribeMLModelsInput,
maxWaitTime: TimeAmount? = nil,
logger: Logger = AWSClient.loggingDisabled,
on eventLoop: EventLoop? = nil
) -> EventLoopFuture<Void>