Class: Aws::MachineLearning::Client
- Inherits:
-
Seahorse::Client::Base
- Object
- Seahorse::Client::Base
- Aws::MachineLearning::Client
- Includes:
- ClientStubs
- Defined in:
- gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb
Overview
An API client for MachineLearning. To construct a client, you need to configure a :region
and :credentials
.
client = Aws::MachineLearning::Client.new(
region: region_name,
credentials: credentials,
# ...
)
For details on configuring region and credentials see the developer guide.
See #initialize for a full list of supported configuration options.
Instance Attribute Summary
Attributes inherited from Seahorse::Client::Base
API Operations collapse
-
#add_tags(params = {}) ⇒ Types::AddTagsOutput
Adds one or more tags to an object, up to a limit of 10.
-
#create_batch_prediction(params = {}) ⇒ Types::CreateBatchPredictionOutput
Generates predictions for a group of observations.
-
#create_data_source_from_rds(params = {}) ⇒ Types::CreateDataSourceFromRDSOutput
Creates a
DataSource
object from an [ Amazon Relational Database Service][1] (Amazon RDS). -
#create_data_source_from_redshift(params = {}) ⇒ Types::CreateDataSourceFromRedshiftOutput
Creates a
DataSource
from a database hosted on an Amazon Redshift cluster. -
#create_data_source_from_s3(params = {}) ⇒ Types::CreateDataSourceFromS3Output
Creates a
DataSource
object. -
#create_evaluation(params = {}) ⇒ Types::CreateEvaluationOutput
Creates a new
Evaluation
of anMLModel
. -
#create_ml_model(params = {}) ⇒ Types::CreateMLModelOutput
Creates a new
MLModel
using theDataSource
and the recipe as information sources. -
#create_realtime_endpoint(params = {}) ⇒ Types::CreateRealtimeEndpointOutput
Creates a real-time endpoint for the
MLModel
. -
#delete_batch_prediction(params = {}) ⇒ Types::DeleteBatchPredictionOutput
Assigns the DELETED status to a
BatchPrediction
, rendering it unusable. -
#delete_data_source(params = {}) ⇒ Types::DeleteDataSourceOutput
Assigns the DELETED status to a
DataSource
, rendering it unusable. -
#delete_evaluation(params = {}) ⇒ Types::DeleteEvaluationOutput
Assigns the
DELETED
status to anEvaluation
, rendering it unusable. -
#delete_ml_model(params = {}) ⇒ Types::DeleteMLModelOutput
Assigns the
DELETED
status to anMLModel
, rendering it unusable. -
#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.
-
#describe_batch_predictions(params = {}) ⇒ Types::DescribeBatchPredictionsOutput
Returns a list of
BatchPrediction
operations that match the search criteria in the request. -
#describe_data_sources(params = {}) ⇒ Types::DescribeDataSourcesOutput
Returns a list of
DataSource
that match the search criteria in the request. -
#describe_evaluations(params = {}) ⇒ Types::DescribeEvaluationsOutput
Returns a list of
DescribeEvaluations
that match the search criteria in the request. -
#describe_ml_models(params = {}) ⇒ Types::DescribeMLModelsOutput
Returns a list of
MLModel
that match the search criteria in the request. -
#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 aBatch 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 theDataSource
. -
#get_evaluation(params = {}) ⇒ Types::GetEvaluationOutput
Returns an
Evaluation
that includes metadata as well as the current status of theEvaluation
. -
#get_ml_model(params = {}) ⇒ Types::GetMLModelOutput
Returns an
MLModel
that includes detailed metadata, data source information, and the current status of theMLModel
. -
#predict(params = {}) ⇒ Types::PredictOutput
Generates a prediction for the observation using the specified
ML Model
. -
#update_batch_prediction(params = {}) ⇒ Types::UpdateBatchPredictionOutput
Updates the
BatchPredictionName
of aBatchPrediction
. -
#update_data_source(params = {}) ⇒ Types::UpdateDataSourceOutput
Updates the
DataSourceName
of aDataSource
. -
#update_evaluation(params = {}) ⇒ Types::UpdateEvaluationOutput
Updates the
EvaluationName
of anEvaluation
. -
#update_ml_model(params = {}) ⇒ Types::UpdateMLModelOutput
Updates the
MLModelName
and theScoreThreshold
of anMLModel
.
Instance Method Summary collapse
-
#initialize(options) ⇒ Client
constructor
A new instance of Client.
-
#wait_until(waiter_name, params = {}, options = {}) {|w.waiter| ... } ⇒ Boolean
Polls an API operation until a resource enters a desired state.
Methods included from ClientStubs
#api_requests, #stub_data, #stub_responses
Methods inherited from Seahorse::Client::Base
add_plugin, api, clear_plugins, define, new, #operation_names, plugins, remove_plugin, set_api, set_plugins
Methods included from Seahorse::Client::HandlerBuilder
#handle, #handle_request, #handle_response
Constructor Details
#initialize(options) ⇒ Client
Returns a new instance of Client.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 397 def initialize(*args) super end |
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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 444 def (params = {}, = {}) req = build_request(:add_tags, params) req.send_request() end |
#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 MLModel
and the data files referenced by the DataSource
as information
sources.
CreateBatchPrediction
is an asynchronous operation. In response to
CreateBatchPrediction
, Amazon Machine Learning (Amazon ML)
immediately returns and sets the BatchPrediction
status to
PENDING
. 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 the
COMPLETED
status appears, the results are available in the location
specified by the OutputUri
parameter.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 515 def create_batch_prediction(params = {}, = {}) req = build_request(:create_batch_prediction, params) req.send_request() end |
#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
, or
CreateBatchPrediction
operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to
CreateDataSourceFromRDS
, 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 COMPLETED
or PENDING
state can be used only to perform >CreateMLModel
>,
CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource
operation response.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 651 def create_data_source_from_rds(params = {}, = {}) req = build_request(:create_data_source_from_rds, params) req.send_request() end |
#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 CreateBatchPrediction
operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In
response to CreateDataSourceFromRedshift
, 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
COMPLETED
or PENDING
states can be used to perform only
CreateMLModel
, CreateEvaluation
, or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute 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 SelectSqlQuery
query. Amazon ML executes an Unload
command in Amazon Redshift to
transfer the result set of the SelectSqlQuery
query to
S3StagingLocation
.
After the DataSource
has been created, it's ready for use in
evaluations and batch predictions. If you plan to use the DataSource
to train an MLModel
, the DataSource
also requires a recipe. A
recipe describes how each input variable will be used in training an
MLModel
. 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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 786 def create_data_source_from_redshift(params = {}, = {}) req = build_request(:create_data_source_from_redshift, params) req.send_request() end |
#create_data_source_from_s3(params = {}) ⇒ Types::CreateDataSourceFromS3Output
Creates a DataSource
object. A DataSource
references data that can
be used to perform CreateMLModel
, CreateEvaluation
, or
CreateBatchPrediction
operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to
CreateDataSourceFromS3
, 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 the
COMPLETED
or PENDING
state can be used to perform only
CreateMLModel
, CreateEvaluation
or CreateBatchPrediction
operations.
If Amazon ML can't accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute 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 DataSource
to train an MLModel
, the DataSource
also needs a recipe. A recipe
describes how each input variable will be used in training an
MLModel
. 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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 879 def create_data_source_from_s3(params = {}, = {}) req = build_request(:create_data_source_from_s3, params) req.send_request() end |
#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 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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 939 def create_evaluation(params = {}, = {}) req = build_request(:create_evaluation, params) req.send_request() end |
#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 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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1071 def create_ml_model(params = {}, = {}) req = build_request(:create_ml_model, params) req.send_request() end |
#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
.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1104 def create_realtime_endpoint(params = {}, = {}) req = build_request(:create_realtime_endpoint, params) req.send_request() end |
#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 the
BatchPrediction
changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is
irreversible.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1138 def delete_batch_prediction(params = {}, = {}) req = build_request(:delete_batch_prediction, params) req.send_request() end |
#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 DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are
irreversible.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1171 def delete_data_source(params = {}, = {}) req = build_request(:delete_data_source, params) req.send_request() end |
#delete_evaluation(params = {}) ⇒ Types::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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1206 def delete_evaluation(params = {}, = {}) req = build_request(:delete_evaluation, params) req.send_request() end |
#delete_ml_model(params = {}) ⇒ Types::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.
Caution: The result of the DeleteMLModel
operation is
irreversible.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1239 def delete_ml_model(params = {}, = {}) req = build_request(:delete_ml_model, params) req.send_request() end |
#delete_realtime_endpoint(params = {}) ⇒ Types::DeleteRealtimeEndpointOutput
Deletes a real time endpoint of an MLModel
.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1270 def delete_realtime_endpoint(params = {}, = {}) req = build_request(:delete_realtime_endpoint, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1309 def (params = {}, = {}) req = build_request(:delete_tags, params) req.send_request() end |
#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
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1454 def describe_batch_predictions(params = {}, = {}) req = build_request(:describe_batch_predictions, params) req.send_request() end |
#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
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1602 def describe_data_sources(params = {}, = {}) req = build_request(:describe_data_sources, params) req.send_request() end |
#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
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1745 def describe_evaluations(params = {}, = {}) req = build_request(:describe_evaluations, params) req.send_request() end |
#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
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1901 def describe_ml_models(params = {}, = {}) req = build_request(:describe_ml_models, params) req.send_request() end |
#describe_tags(params = {}) ⇒ Types::DescribeTagsOutput
Describes one or more of the tags for your Amazon ML object.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1937 def (params = {}, = {}) req = build_request(:describe_tags, params) req.send_request() end |
#get_batch_prediction(params = {}) ⇒ Types::GetBatchPredictionOutput
Returns a BatchPrediction
that includes detailed metadata, status,
and data file information for a Batch Prediction
request.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 1996 def get_batch_prediction(params = {}, = {}) req = build_request(:get_batch_prediction, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2083 def get_data_source(params = {}, = {}) req = build_request(:get_data_source, params) req.send_request() end |
#get_evaluation(params = {}) ⇒ Types::GetEvaluationOutput
Returns an Evaluation
that includes metadata as well as the current
status of the Evaluation
.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2141 def get_evaluation(params = {}, = {}) req = build_request(:get_evaluation, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2222 def get_ml_model(params = {}, = {}) req = build_request(:get_ml_model, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2267 def predict(params = {}, = {}) req = build_request(:predict, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2300 def update_batch_prediction(params = {}, = {}) req = build_request(:update_batch_prediction, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2334 def update_data_source(params = {}, = {}) req = build_request(:update_data_source, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2368 def update_evaluation(params = {}, = {}) req = build_request(:update_evaluation, params) req.send_request() end |
#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.
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# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2412 def update_ml_model(params = {}, = {}) req = build_request(:update_ml_model, params) req.send_request() end |
#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 from Waiters::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.
waiter_name | params | :delay | :max_attempts |
---|---|---|---|
batch_prediction_available | #describe_batch_predictions | 30 | 60 |
data_source_available | #describe_data_sources | 30 | 60 |
evaluation_available | #describe_evaluations | 30 | 60 |
ml_model_available | #describe_ml_models | 30 | 60 |
2525 2526 2527 2528 2529 |
# File 'gems/aws-sdk-machinelearning/lib/aws-sdk-machinelearning/client.rb', line 2525 def wait_until(waiter_name, params = {}, = {}) w = waiter(waiter_name, ) yield(w.waiter) if block_given? # deprecated w.wait(params) end |