2024/08/29 - Amazon Personalize - 1 new1 updated api methods
Changes This releases ability to update automatic training scheduler for customer solutions
Updates an Amazon Personalize solution to use a different automatic training configuration. When you update a solution, you can change whether the solution uses automatic training, and you can change the training frequency. For more information about updating a solution, see Updating a solution.
A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
To get the status of a solution update, call the DescribeSolution API operation and find the status in the latestSolutionUpdate.
See also: AWS API Documentation
Request Syntax
client.update_solution(
solutionArn='string',
performAutoTraining=True|False,
solutionUpdateConfig={
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
}
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution to update.
boolean
Whether the solution uses automatic training to create new solution versions (trained models). You can change the training frequency by specifying a schedulingExpression in the AutoTrainingConfig as part of solution configuration.
If you turn on automatic training, the first automatic training starts within one hour after the solution update completes. If you manually create a solution version within the hour, the solution skips the first automatic training. For more information about automatic training, see Configuring automatic training.
After training starts, you can get the solution version's Amazon Resource Name (ARN) with the ListSolutionVersions API operation. To get its status, use the DescribeSolutionVersion.
dict
The new configuration details of the solution.
autoTrainingConfig (dict) --
The automatic training configuration to use when performAutoTraining is true.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
dict
Response Syntax
{
'solutionArn': 'string'
}
Response Structure
(dict) --
solutionArn (string) --
The same solution Amazon Resource Name (ARN) as given in the request.
{'solution': {'latestSolutionUpdate': {'creationDateTime': 'timestamp',
'failureReason': 'string',
'lastUpdatedDateTime': 'timestamp',
'performAutoTraining': 'boolean',
'solutionUpdateConfig': {'autoTrainingConfig': {'schedulingExpression': 'string'}},
'status': 'string'}}}
Describes a solution. For more information on solutions, see CreateSolution.
See also: AWS API Documentation
Request Syntax
client.describe_solution(
solutionArn='string'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the solution to describe.
dict
Response Syntax
{
'solution': {
'name': 'string',
'solutionArn': 'string',
'performHPO': True|False,
'performAutoML': True|False,
'performAutoTraining': True|False,
'recipeArn': 'string',
'datasetGroupArn': 'string',
'eventType': 'string',
'solutionConfig': {
'eventValueThreshold': 'string',
'hpoConfig': {
'hpoObjective': {
'type': 'string',
'metricName': 'string',
'metricRegex': 'string'
},
'hpoResourceConfig': {
'maxNumberOfTrainingJobs': 'string',
'maxParallelTrainingJobs': 'string'
},
'algorithmHyperParameterRanges': {
'integerHyperParameterRanges': [
{
'name': 'string',
'minValue': 123,
'maxValue': 123
},
],
'continuousHyperParameterRanges': [
{
'name': 'string',
'minValue': 123.0,
'maxValue': 123.0
},
],
'categoricalHyperParameterRanges': [
{
'name': 'string',
'values': [
'string',
]
},
]
}
},
'algorithmHyperParameters': {
'string': 'string'
},
'featureTransformationParameters': {
'string': 'string'
},
'autoMLConfig': {
'metricName': 'string',
'recipeList': [
'string',
]
},
'optimizationObjective': {
'itemAttribute': 'string',
'objectiveSensitivity': 'LOW'|'MEDIUM'|'HIGH'|'OFF'
},
'trainingDataConfig': {
'excludedDatasetColumns': {
'string': [
'string',
]
}
},
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
},
'autoMLResult': {
'bestRecipeArn': 'string'
},
'status': 'string',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'latestSolutionVersion': {
'solutionVersionArn': 'string',
'status': 'string',
'trainingMode': 'FULL'|'UPDATE'|'AUTOTRAIN',
'trainingType': 'AUTOMATIC'|'MANUAL',
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
},
'latestSolutionUpdate': {
'solutionUpdateConfig': {
'autoTrainingConfig': {
'schedulingExpression': 'string'
}
},
'status': 'string',
'performAutoTraining': True|False,
'creationDateTime': datetime(2015, 1, 1),
'lastUpdatedDateTime': datetime(2015, 1, 1),
'failureReason': 'string'
}
}
}
Response Structure
(dict) --
solution (dict) --
An object that describes the solution.
name (string) --
The name of the solution.
solutionArn (string) --
The ARN of the solution.
performHPO (boolean) --
Whether to perform hyperparameter optimization (HPO) on the chosen recipe. The default is false.
performAutoML (boolean) --
When true, Amazon Personalize performs a search for the best USER_PERSONALIZATION recipe from the list specified in the solution configuration ( recipeArn must not be specified). When false (the default), Amazon Personalize uses recipeArn for training.
performAutoTraining (boolean) --
Specifies whether the solution automatically creates solution versions. The default is True and the solution automatically creates new solution versions every 7 days.
For more information about auto training, see Creating and configuring a solution.
recipeArn (string) --
The ARN of the recipe used to create the solution. This is required when performAutoML is false.
datasetGroupArn (string) --
The Amazon Resource Name (ARN) of the dataset group that provides the training data.
eventType (string) --
The event type (for example, 'click' or 'like') that is used for training the model. If no eventType is provided, Amazon Personalize uses all interactions for training with equal weight regardless of type.
solutionConfig (dict) --
Describes the configuration properties for the solution.
eventValueThreshold (string) --
Only events with a value greater than or equal to this threshold are used for training a model.
hpoConfig (dict) --
Describes the properties for hyperparameter optimization (HPO).
hpoObjective (dict) --
The metric to optimize during HPO.
type (string) --
The type of the metric. Valid values are Maximize and Minimize.
metricName (string) --
The name of the metric.
metricRegex (string) --
A regular expression for finding the metric in the training job logs.
hpoResourceConfig (dict) --
Describes the resource configuration for HPO.
maxNumberOfTrainingJobs (string) --
The maximum number of training jobs when you create a solution version. The maximum value for maxNumberOfTrainingJobs is 40.
maxParallelTrainingJobs (string) --
The maximum number of parallel training jobs when you create a solution version. The maximum value for maxParallelTrainingJobs is 10.
algorithmHyperParameterRanges (dict) --
The hyperparameters and their allowable ranges.
integerHyperParameterRanges (list) --
The integer-valued hyperparameters and their ranges.
(dict) --
Provides the name and range of an integer-valued hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (integer) --
The minimum allowable value for the hyperparameter.
maxValue (integer) --
The maximum allowable value for the hyperparameter.
continuousHyperParameterRanges (list) --
The continuous hyperparameters and their ranges.
(dict) --
Provides the name and range of a continuous hyperparameter.
name (string) --
The name of the hyperparameter.
minValue (float) --
The minimum allowable value for the hyperparameter.
maxValue (float) --
The maximum allowable value for the hyperparameter.
categoricalHyperParameterRanges (list) --
The categorical hyperparameters and their ranges.
(dict) --
Provides the name and range of a categorical hyperparameter.
name (string) --
The name of the hyperparameter.
values (list) --
A list of the categories for the hyperparameter.
(string) --
algorithmHyperParameters (dict) --
Lists the algorithm hyperparameters and their values.
(string) --
(string) --
featureTransformationParameters (dict) --
Lists the feature transformation parameters.
(string) --
(string) --
autoMLConfig (dict) --
The AutoMLConfig object containing a list of recipes to search when AutoML is performed.
metricName (string) --
The metric to optimize.
recipeList (list) --
The list of candidate recipes.
(string) --
optimizationObjective (dict) --
Describes the additional objective for the solution, such as maximizing streaming minutes or increasing revenue. For more information see Optimizing a solution.
itemAttribute (string) --
The numerical metadata column in an Items dataset related to the optimization objective. For example, VIDEO_LENGTH (to maximize streaming minutes), or PRICE (to maximize revenue).
objectiveSensitivity (string) --
Specifies how Amazon Personalize balances the importance of your optimization objective versus relevance.
trainingDataConfig (dict) --
Specifies the training data configuration to use when creating a custom solution version (trained model).
excludedDatasetColumns (dict) --
Specifies the columns to exclude from training. Each key is a dataset type, and each value is a list of columns. Exclude columns to control what data Amazon Personalize uses to generate recommendations.
For example, you might have a column that you want to use only to filter recommendations. You can exclude this column from training and Amazon Personalize considers it only when filtering.
(string) --
(list) --
(string) --
autoTrainingConfig (dict) --
Specifies the automatic training configuration to use.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
autoMLResult (dict) --
When performAutoML is true, specifies the best recipe found.
bestRecipeArn (string) --
The Amazon Resource Name (ARN) of the best recipe.
status (string) --
The status of the solution.
A solution can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
DELETE PENDING > DELETE IN_PROGRESS
creationDateTime (datetime) --
The creation date and time (in Unix time) of the solution.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution was last updated.
latestSolutionVersion (dict) --
Describes the latest version of the solution, including the status and the ARN.
solutionVersionArn (string) --
The Amazon Resource Name (ARN) of the solution version.
status (string) --
The status of the solution version.
A solution version can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
trainingMode (string) --
The scope of training to be performed when creating the solution version. A FULL training considers all of the data in your dataset group. An UPDATE processes only the data that has changed since the latest training. Only solution versions created with the User-Personalization recipe can use UPDATE.
trainingType (string) --
Whether the solution version was created automatically or manually.
creationDateTime (datetime) --
The date and time (in Unix time) that this version of a solution was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution version was last updated.
failureReason (string) --
If a solution version fails, the reason behind the failure.
latestSolutionUpdate (dict) --
Provides a summary of the latest updates to the solution.
solutionUpdateConfig (dict) --
The configuration details of the solution.
autoTrainingConfig (dict) --
The automatic training configuration to use when performAutoTraining is true.
schedulingExpression (string) --
Specifies how often to automatically train new solution versions. Specify a rate expression in rate(value unit) format. For value, specify a number between 1 and 30. For unit, specify day or days. For example, to automatically create a new solution version every 5 days, specify rate(5 days). The default is every 7 days.
For more information about auto training, see Creating and configuring a solution.
status (string) --
The status of the solution update. A solution update can be in one of the following states:
CREATE PENDING > CREATE IN_PROGRESS > ACTIVE -or- CREATE FAILED
performAutoTraining (boolean) --
Whether the solution automatically creates solution versions.
creationDateTime (datetime) --
The date and time (in Unix format) that the solution update was created.
lastUpdatedDateTime (datetime) --
The date and time (in Unix time) that the solution update was last updated.
failureReason (string) --
If a solution update fails, the reason behind the failure.