Trains or retrains an active solution in a Custom dataset group. A solution is created using the CreateSolution operation and must be in the ACTIVE state before calling CreateSolutionVersion. A new version of the solution is created every time you call this operation.
Status
A solution version can be in one of the following states:
-
CREATE PENDING
-
CREATE IN_PROGRESS
-
ACTIVE
-
CREATE FAILED
-
CREATE STOPPING
-
CREATE STOPPED
To get the status of the version, call DescribeSolutionVersion. Wait until the status shows as ACTIVE before calling CreateCampaign.
If the status shows as CREATE FAILED, the response includes a failureReason key, which describes why the job failed.
Related APIs
Parameters:
-
name
(Optional[Name], default:
None
)
–
The name of the solution version.
-
solution_arn
(Arn)
–
The Amazon Resource Name (ARN) of the solution containing the training configuration information.
-
training_mode
(Optional[TrainingMode], default:
None
)
–
The scope of training to be performed when creating the solution version. The default is FULL. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group.
If you use User-Personalization, you can specify a training mode of UPDATE. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify UPDATE, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to FULL and deploy it in a campaign. For more information about automatic updates, see Automatic updates.
The UPDATE option can only be used when you already have an active solution version created from the input solution using the FULL option and the input solution was trained with the User-Personalization recipe or the legacy HRNN-Coldstart recipe.
-
tags
(Optional[Tags], default:
None
)
–
A list of tags to apply to the solution version.
Raises:
Source code in docs_src/capo_personalize/_services/personalize.py
| def create_solution_version(
self,
solution_arn: "capo_personalize.types.arn.Arn",
*,
config_overrides: Optional[PersonalizeClientConfig] = None,
name: Optional["capo_personalize.types.name.Name"] = None,
training_mode: Optional[
"capo_personalize.types.training_mode.TrainingMode"
] = None,
tags: Optional["capo_personalize.types.tags.Tags"] = None,
) -> "capo_personalize.types.create_solution_version_response.CreateSolutionVersionResponse":
"""<p>Trains or retrains an active solution in a Custom dataset group. A solution is created using the <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html">CreateSolution</a> operation and must be in the ACTIVE state before calling <code>CreateSolutionVersion</code>. A new version of the solution is created every time you call this operation.</p> <p> <b>Status</b> </p> <p>A solution version can be in one of the following states:</p> <ul> <li> <p>CREATE PENDING</p> </li> <li> <p>CREATE IN_PROGRESS</p> </li> <li> <p>ACTIVE</p> </li> <li> <p>CREATE FAILED</p> </li> <li> <p>CREATE STOPPING</p> </li> <li> <p>CREATE STOPPED</p> </li> </ul> <p>To get the status of the version, call <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a>. Wait until the status shows as ACTIVE before calling <code>CreateCampaign</code>.</p> <p>If the status shows as CREATE FAILED, the response includes a <code>failureReason</code> key, which describes why the job failed.</p> <p class="title"> <b>Related APIs</b> </p> <ul> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutionVersions.html">ListSolutionVersions</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolutionVersion.html">DescribeSolutionVersion</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_ListSolutions.html">ListSolutions</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_CreateSolution.html">CreateSolution</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DescribeSolution.html">DescribeSolution</a> </p> </li> <li> <p> <a href="https://docs.aws.amazon.com/personalize/latest/dg/API_DeleteSolution.html">DeleteSolution</a> </p> </li> </ul>
Args:
name: <p>The name of the solution version.</p>
solution_arn: <p>The Amazon Resource Name (ARN) of the solution containing the training configuration information.</p>
training_mode: <p>The scope of training to be performed when creating the solution version. The default is <code>FULL</code>. This creates a completely new model based on the entirety of the training data from the datasets in your dataset group. </p> <p>If you use <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a>, you can specify a training mode of <code>UPDATE</code>. This updates the model to consider new items for recommendations. It is not a full retraining. You should still complete a full retraining weekly. If you specify <code>UPDATE</code>, Amazon Personalize will stop automatic updates for the solution version. To resume updates, create a new solution with training mode set to <code>FULL</code> and deploy it in a campaign. For more information about automatic updates, see <a href="https://docs.aws.amazon.com/personalize/latest/dg/use-case-recipe-features.html#maintaining-with-automatic-updates">Automatic updates</a>. </p> <p>The <code>UPDATE</code> option can only be used when you already have an active solution version created from the input solution using the <code>FULL</code> option and the input solution was trained with the <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-new-item-USER_PERSONALIZATION.html">User-Personalization</a> recipe or the legacy <a href="https://docs.aws.amazon.com/personalize/latest/dg/native-recipe-hrnn-coldstart.html">HRNN-Coldstart</a> recipe.</p>
tags: <p>A list of <a href="https://docs.aws.amazon.com/personalize/latest/dg/tagging-resources.html">tags</a> to apply to the solution version.</p>
Raises:
capo_personalize.errors.invalid_input_exception.InvalidInputException: <p>Provide a valid value for the field or parameter.</p>
capo_personalize.errors.limit_exceeded_exception.LimitExceededException: <p>The limit on the number of requests per second has been exceeded.</p>
capo_personalize.errors.resource_already_exists_exception.ResourceAlreadyExistsException: <p>The specified resource already exists.</p>
capo_personalize.errors.resource_in_use_exception.ResourceInUseException: <p>The specified resource is in use.</p>
capo_personalize.errors.resource_not_found_exception.ResourceNotFoundException: <p>Could not find the specified resource.</p>
capo_personalize.errors.too_many_tags_exception.TooManyTagsException: <p>You have exceeded the maximum number of tags you can apply to this resource. </p>
capo_personalize.errors.UnknownServiceError: The service returned an error code this client does not model.
"""
def _handler(
req: "OperationRequest[capo_personalize.types.create_solution_version_request.CreateSolutionVersionRequest]",
) -> OperationResponse[
"capo_personalize.types.create_solution_version_response.CreateSolutionVersionResponse"
]:
import capo_personalize._operations.amazon_personalize.create_solution_version
output, http_response = (
capo_personalize._operations.amazon_personalize.create_solution_version.create_solution_version(
req.options, req.input
)
)
return OperationResponse(output=output, response=http_response)
def _redact_input(
input_: "capo_personalize.types.create_solution_version_request.CreateSolutionVersionRequest",
) -> dict[str, Any]:
import capo_personalize.types.create_solution_version_request
return (
capo_personalize.types.create_solution_version_request.redact_sensitive(
input_
)
)
interceptors_, options_ = self.operation_options(
config_overrides,
OperationLogging("CreateSolutionVersion", redact_input=_redact_input),
)
input_: capo_personalize.types.create_solution_version_request.CreateSolutionVersionRequest = {
"solution_arn": solution_arn
}
if name is not None:
input_["name"] = name
if training_mode is not None:
input_["training_mode"] = training_mode
if tags is not None:
input_["tags"] = tags
response = execute_pipeline(
OperationRequest(input=input_, options=options_),
handler=_handler,
interceptors=list(interceptors_),
)
response.response.close()
return response.output
|