2023/09/14 - Amazon Lookout for Equipment - 8 new5 updated api methods
Changes This release adds APIs for the new scheduled retraining feature.
Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.
See also: AWS API Documentation
Request Syntax
client.describe_retraining_scheduler(
ModelName='string'
)
string
[REQUIRED]
The name of the model that the retraining scheduler is attached to.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'RetrainingStartDate': datetime(2015, 1, 1),
'RetrainingFrequency': 'string',
'LookbackWindow': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED',
'PromoteMode': 'MANAGED'|'MANUAL',
'CreatedAt': datetime(2015, 1, 1),
'UpdatedAt': datetime(2015, 1, 1)
}
Response Structure
(dict) --
ModelName (string) --
The name of the model that the retraining scheduler is attached to.
ModelArn (string) --
The ARN of the model that the retraining scheduler is attached to.
RetrainingStartDate (datetime) --
The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
RetrainingFrequency (string) --
The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.
LookbackWindow (string) --
The number of past days of data used for retraining.
Status (string) --
The status of the retraining scheduler.
PromoteMode (string) --
Indicates how the service uses new models. In MANAGED mode, new models are used for inference if they have better performance than the current model. In MANUAL mode, the new models are not used until they are manually activated.
CreatedAt (datetime) --
Indicates the time and date at which the retraining scheduler was created.
UpdatedAt (datetime) --
Indicates the time and date at which the retraining scheduler was updated.
Stops a retraining scheduler.
See also: AWS API Documentation
Request Syntax
client.stop_retraining_scheduler(
ModelName='string'
)
string
[REQUIRED]
The name of the model whose retraining scheduler you want to stop.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
}
Response Structure
(dict) --
ModelName (string) --
The name of the model whose retraining scheduler is being stopped.
ModelArn (string) --
The ARN of the model whose retraining scheduler is being stopped.
Status (string) --
The status of the retraining scheduler.
Updates a retraining scheduler.
See also: AWS API Documentation
Request Syntax
client.update_retraining_scheduler(
ModelName='string',
RetrainingStartDate=datetime(2015, 1, 1),
RetrainingFrequency='string',
LookbackWindow='string',
PromoteMode='MANAGED'|'MANUAL'
)
string
[REQUIRED]
The name of the model whose retraining scheduler you want to update.
datetime
The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
string
This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:
P3M15D – Every 3 months and 15 days
P2M – Every 2 months
P150D – Every 150 days
string
The number of past days of data that will be used for retraining.
string
Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.
None
Deletes a retraining scheduler from a model. The retraining scheduler must be in the STOPPED status.
See also: AWS API Documentation
Request Syntax
client.delete_retraining_scheduler(
ModelName='string'
)
string
[REQUIRED]
The name of the model whose retraining scheduler you want to delete.
None
Creates a retraining scheduler on the specified model.
See also: AWS API Documentation
Request Syntax
client.create_retraining_scheduler(
ModelName='string',
RetrainingStartDate=datetime(2015, 1, 1),
RetrainingFrequency='string',
LookbackWindow='string',
PromoteMode='MANAGED'|'MANUAL',
ClientToken='string'
)
string
[REQUIRED]
The name of the model to add the retraining scheduler to.
datetime
The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
string
[REQUIRED]
This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:
P3M15D – Every 3 months and 15 days
P2M – Every 2 months
P150D – Every 150 days
string
[REQUIRED]
The number of past days of data that will be used for retraining.
string
Indicates how the service will use new models. In MANAGED mode, new models will automatically be used for inference if they have better performance than the current model. In MANUAL mode, the new models will not be used until they are manually activated.
string
[REQUIRED]
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
This field is autopopulated if not provided.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
}
Response Structure
(dict) --
ModelName (string) --
The name of the model that you added the retraining scheduler to.
ModelArn (string) --
The ARN of the model that you added the retraining scheduler to.
Status (string) --
The status of the retraining scheduler.
Starts a retraining scheduler.
See also: AWS API Documentation
Request Syntax
client.start_retraining_scheduler(
ModelName='string'
)
string
[REQUIRED]
The name of the model whose retraining scheduler you want to start.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
}
Response Structure
(dict) --
ModelName (string) --
The name of the model whose retraining scheduler is being started.
ModelArn (string) --
The ARN of the model whose retraining scheduler is being started.
Status (string) --
The status of the retraining scheduler.
Updates a model in the account.
See also: AWS API Documentation
Request Syntax
client.update_model(
ModelName='string',
LabelsInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
RoleArn='string'
)
string
[REQUIRED]
The name of the model to update.
dict
Contains the configuration information for the S3 location being used to hold label data.
S3InputConfiguration (dict) --
Contains location information for the S3 location being used for label data.
Bucket (string) -- [REQUIRED]
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
LabelGroupName (string) --
The name of the label group to be used for label data.
string
The ARN of the model to update.
None
Lists all retraining schedulers in your account, filtering by model name prefix and status.
See also: AWS API Documentation
Request Syntax
client.list_retraining_schedulers(
ModelNameBeginsWith='string',
Status='PENDING'|'RUNNING'|'STOPPING'|'STOPPED',
NextToken='string',
MaxResults=123
)
string
Specify this field to only list retraining schedulers whose machine learning models begin with the value you specify.
string
Specify this field to only list retraining schedulers whose status matches the value you specify.
string
If the number of results exceeds the maximum, a pagination token is returned. Use the token in the request to show the next page of retraining schedulers.
integer
Specifies the maximum number of retraining schedulers to list.
dict
Response Syntax
{
'RetrainingSchedulerSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'Status': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED',
'RetrainingStartDate': datetime(2015, 1, 1),
'RetrainingFrequency': 'string',
'LookbackWindow': 'string'
},
],
'NextToken': 'string'
}
Response Structure
(dict) --
RetrainingSchedulerSummaries (list) --
Provides information on the specified retraining scheduler, including the model name, model ARN, status, and start date.
(dict) --
Provides information about the specified retraining scheduler, including model name, status, start date, frequency, and lookback window.
ModelName (string) --
The name of the model that the retraining scheduler is attached to.
ModelArn (string) --
The ARN of the model that the retraining scheduler is attached to.
Status (string) --
The status of the retraining scheduler.
RetrainingStartDate (datetime) --
The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
RetrainingFrequency (string) --
The frequency at which the model retraining is set. This follows the ISO 8601 guidelines.
LookbackWindow (string) --
The number of past days of data used for retraining.
NextToken (string) --
If the number of results exceeds the maximum, this pagination token is returned. Use this token in the request to show the next page of retraining schedulers.
{'AccumulatedInferenceDataEndTime': 'timestamp',
'AccumulatedInferenceDataStartTime': 'timestamp',
'LatestScheduledRetrainingAvailableDataInDays': 'integer',
'LatestScheduledRetrainingFailedReason': 'string',
'LatestScheduledRetrainingModelVersion': 'long',
'LatestScheduledRetrainingStartTime': 'timestamp',
'LatestScheduledRetrainingStatus': 'IN_PROGRESS | SUCCESS | FAILED | '
'IMPORT_IN_PROGRESS | CANCELED',
'NextScheduledRetrainingStartDate': 'timestamp',
'PriorModelMetrics': 'string',
'RetrainingSchedulerStatus': 'PENDING | RUNNING | STOPPING | STOPPED'}
Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on.
See also: AWS API Documentation
Request Syntax
client.describe_model(
ModelName='string'
)
string
[REQUIRED]
The name of the machine learning model to be described.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'DatasetName': 'string',
'DatasetArn': 'string',
'Schema': 'string',
'LabelsInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
'TrainingDataStartTime': datetime(2015, 1, 1),
'TrainingDataEndTime': datetime(2015, 1, 1),
'EvaluationDataStartTime': datetime(2015, 1, 1),
'EvaluationDataEndTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'DataPreProcessingConfiguration': {
'TargetSamplingRate': 'PT1S'|'PT5S'|'PT10S'|'PT15S'|'PT30S'|'PT1M'|'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H'
},
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
'TrainingExecutionStartTime': datetime(2015, 1, 1),
'TrainingExecutionEndTime': datetime(2015, 1, 1),
'FailedReason': 'string',
'ModelMetrics': 'string',
'LastUpdatedTime': datetime(2015, 1, 1),
'CreatedAt': datetime(2015, 1, 1),
'ServerSideKmsKeyId': 'string',
'OffCondition': 'string',
'SourceModelVersionArn': 'string',
'ImportJobStartTime': datetime(2015, 1, 1),
'ImportJobEndTime': datetime(2015, 1, 1),
'ActiveModelVersion': 123,
'ActiveModelVersionArn': 'string',
'ModelVersionActivatedAt': datetime(2015, 1, 1),
'PreviousActiveModelVersion': 123,
'PreviousActiveModelVersionArn': 'string',
'PreviousModelVersionActivatedAt': datetime(2015, 1, 1),
'PriorModelMetrics': 'string',
'LatestScheduledRetrainingFailedReason': 'string',
'LatestScheduledRetrainingStatus': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
'LatestScheduledRetrainingModelVersion': 123,
'LatestScheduledRetrainingStartTime': datetime(2015, 1, 1),
'LatestScheduledRetrainingAvailableDataInDays': 123,
'NextScheduledRetrainingStartDate': datetime(2015, 1, 1),
'AccumulatedInferenceDataStartTime': datetime(2015, 1, 1),
'AccumulatedInferenceDataEndTime': datetime(2015, 1, 1),
'RetrainingSchedulerStatus': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
}
Response Structure
(dict) --
ModelName (string) --
The name of the machine learning model being described.
ModelArn (string) --
The Amazon Resource Name (ARN) of the machine learning model being described.
DatasetName (string) --
The name of the dataset being used by the machine learning being described.
DatasetArn (string) --
The Amazon Resouce Name (ARN) of the dataset used to create the machine learning model being described.
Schema (string) --
A JSON description of the data that is in each time series dataset, including names, column names, and data types.
LabelsInputConfiguration (dict) --
Specifies configuration information about the labels input, including its S3 location.
S3InputConfiguration (dict) --
Contains location information for the S3 location being used for label data.
Bucket (string) --
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
LabelGroupName (string) --
The name of the label group to be used for label data.
TrainingDataStartTime (datetime) --
Indicates the time reference in the dataset that was used to begin the subset of training data for the machine learning model.
TrainingDataEndTime (datetime) --
Indicates the time reference in the dataset that was used to end the subset of training data for the machine learning model.
EvaluationDataStartTime (datetime) --
Indicates the time reference in the dataset that was used to begin the subset of evaluation data for the machine learning model.
EvaluationDataEndTime (datetime) --
Indicates the time reference in the dataset that was used to end the subset of evaluation data for the machine learning model.
RoleArn (string) --
The Amazon Resource Name (ARN) of a role with permission to access the data source for the machine learning model being described.
DataPreProcessingConfiguration (dict) --
The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.
When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H
TargetSamplingRate (string) --
The sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.
When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H
Status (string) --
Specifies the current status of the model being described. Status describes the status of the most recent action of the model.
TrainingExecutionStartTime (datetime) --
Indicates the time at which the training of the machine learning model began.
TrainingExecutionEndTime (datetime) --
Indicates the time at which the training of the machine learning model was completed.
FailedReason (string) --
If the training of the machine learning model failed, this indicates the reason for that failure.
ModelMetrics (string) --
The Model Metrics show an aggregated summary of the model's performance within the evaluation time range. This is the JSON content of the metrics created when evaluating the model.
LastUpdatedTime (datetime) --
Indicates the last time the machine learning model was updated. The type of update is not specified.
CreatedAt (datetime) --
Indicates the time and date at which the machine learning model was created.
ServerSideKmsKeyId (string) --
Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
OffCondition (string) --
Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.
SourceModelVersionArn (string) --
The Amazon Resource Name (ARN) of the source model version. This field appears if the active model version was imported.
ImportJobStartTime (datetime) --
The date and time when the import job was started. This field appears if the active model version was imported.
ImportJobEndTime (datetime) --
The date and time when the import job was completed. This field appears if the active model version was imported.
ActiveModelVersion (integer) --
The name of the model version used by the inference schedular when running a scheduled inference execution.
ActiveModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version used by the inference scheduler when running a scheduled inference execution.
ModelVersionActivatedAt (datetime) --
The date the active model version was activated.
PreviousActiveModelVersion (integer) --
The model version that was set as the active model version prior to the current active model version.
PreviousActiveModelVersionArn (string) --
The ARN of the model version that was set as the active model version prior to the current active model version.
PreviousModelVersionActivatedAt (datetime) --
The date and time when the previous active model version was activated.
PriorModelMetrics (string) --
If the model version was retrained, this field shows a summary of the performance of the prior model on the new training range. You can use the information in this JSON-formatted object to compare the new model version and the prior model version.
LatestScheduledRetrainingFailedReason (string) --
If the model version was generated by retraining and the training failed, this indicates the reason for that failure.
LatestScheduledRetrainingStatus (string) --
Indicates the status of the most recent scheduled retraining run.
LatestScheduledRetrainingModelVersion (integer) --
Indicates the most recent model version that was generated by retraining.
LatestScheduledRetrainingStartTime (datetime) --
Indicates the start time of the most recent scheduled retraining run.
LatestScheduledRetrainingAvailableDataInDays (integer) --
Indicates the number of days of data used in the most recent scheduled retraining run.
NextScheduledRetrainingStartDate (datetime) --
Indicates the date and time that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.
AccumulatedInferenceDataStartTime (datetime) --
Indicates the start time of the inference data that has been accumulated.
AccumulatedInferenceDataEndTime (datetime) --
Indicates the end time of the inference data that has been accumulated.
RetrainingSchedulerStatus (string) --
Indicates the status of the retraining scheduler.
{'AutoPromotionResult': 'MODEL_PROMOTED | MODEL_NOT_PROMOTED | '
'RETRAINING_INTERNAL_ERROR | RETRAINING_CUSTOMER_ERROR '
'| RETRAINING_CANCELLED',
'AutoPromotionResultReason': 'string',
'PriorModelMetrics': 'string',
'RetrainingAvailableDataInDays': 'integer'}
Retrieves information about a specific machine learning model version.
See also: AWS API Documentation
Request Syntax
client.describe_model_version(
ModelName='string',
ModelVersion=123
)
string
[REQUIRED]
The name of the machine learning model that this version belongs to.
integer
[REQUIRED]
The version of the machine learning model.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'ModelVersion': 123,
'ModelVersionArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
'SourceType': 'TRAINING'|'RETRAINING'|'IMPORT',
'DatasetName': 'string',
'DatasetArn': 'string',
'Schema': 'string',
'LabelsInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
'TrainingDataStartTime': datetime(2015, 1, 1),
'TrainingDataEndTime': datetime(2015, 1, 1),
'EvaluationDataStartTime': datetime(2015, 1, 1),
'EvaluationDataEndTime': datetime(2015, 1, 1),
'RoleArn': 'string',
'DataPreProcessingConfiguration': {
'TargetSamplingRate': 'PT1S'|'PT5S'|'PT10S'|'PT15S'|'PT30S'|'PT1M'|'PT5M'|'PT10M'|'PT15M'|'PT30M'|'PT1H'
},
'TrainingExecutionStartTime': datetime(2015, 1, 1),
'TrainingExecutionEndTime': datetime(2015, 1, 1),
'FailedReason': 'string',
'ModelMetrics': 'string',
'LastUpdatedTime': datetime(2015, 1, 1),
'CreatedAt': datetime(2015, 1, 1),
'ServerSideKmsKeyId': 'string',
'OffCondition': 'string',
'SourceModelVersionArn': 'string',
'ImportJobStartTime': datetime(2015, 1, 1),
'ImportJobEndTime': datetime(2015, 1, 1),
'ImportedDataSizeInBytes': 123,
'PriorModelMetrics': 'string',
'RetrainingAvailableDataInDays': 123,
'AutoPromotionResult': 'MODEL_PROMOTED'|'MODEL_NOT_PROMOTED'|'RETRAINING_INTERNAL_ERROR'|'RETRAINING_CUSTOMER_ERROR'|'RETRAINING_CANCELLED',
'AutoPromotionResultReason': 'string'
}
Response Structure
(dict) --
ModelName (string) --
The name of the machine learning model that this version belongs to.
ModelArn (string) --
The Amazon Resource Name (ARN) of the parent machine learning model that this version belong to.
ModelVersion (integer) --
The version of the machine learning model.
ModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version.
Status (string) --
The current status of the model version.
SourceType (string) --
Indicates whether this model version was created by training or by importing.
DatasetName (string) --
The name of the dataset used to train the model version.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset used to train the model version.
Schema (string) --
The schema of the data used to train the model version.
LabelsInputConfiguration (dict) --
Contains the configuration information for the S3 location being used to hold label data.
S3InputConfiguration (dict) --
Contains location information for the S3 location being used for label data.
Bucket (string) --
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
LabelGroupName (string) --
The name of the label group to be used for label data.
TrainingDataStartTime (datetime) --
The date on which the training data began being gathered. If you imported the version, this is the date that the training data in the source version began being gathered.
TrainingDataEndTime (datetime) --
The date on which the training data finished being gathered. If you imported the version, this is the date that the training data in the source version finished being gathered.
EvaluationDataStartTime (datetime) --
The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version began being gathered.
EvaluationDataEndTime (datetime) --
The date on which the data in the evaluation set began being gathered. If you imported the version, this is the date that the evaluation set data in the source version finished being gathered.
RoleArn (string) --
The Amazon Resource Name (ARN) of the role that was used to train the model version.
DataPreProcessingConfiguration (dict) --
The configuration is the TargetSamplingRate, which is the sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.
When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H
TargetSamplingRate (string) --
The sampling rate of the data after post processing by Amazon Lookout for Equipment. For example, if you provide data that has been collected at a 1 second level and you want the system to resample the data at a 1 minute rate before training, the TargetSamplingRate is 1 minute.
When providing a value for the TargetSamplingRate, you must attach the prefix "PT" to the rate you want. The value for a 1 second rate is therefore PT1S, the value for a 15 minute rate is PT15M, and the value for a 1 hour rate is PT1H
TrainingExecutionStartTime (datetime) --
The time when the training of the version began.
TrainingExecutionEndTime (datetime) --
The time when the training of the version completed.
FailedReason (string) --
The failure message if the training of the model version failed.
ModelMetrics (string) --
Shows an aggregated summary, in JSON format, of the model's performance within the evaluation time range. These metrics are created when evaluating the model.
LastUpdatedTime (datetime) --
Indicates the last time the machine learning model version was updated.
CreatedAt (datetime) --
Indicates the time and date at which the machine learning model version was created.
ServerSideKmsKeyId (string) --
The identifier of the KMS key key used to encrypt model version data by Amazon Lookout for Equipment.
OffCondition (string) --
Indicates that the asset associated with this sensor has been shut off. As long as this condition is met, Lookout for Equipment will not use data from this asset for training, evaluation, or inference.
SourceModelVersionArn (string) --
If model version was imported, then this field is the arn of the source model version.
ImportJobStartTime (datetime) --
The date and time when the import job began. This field appears if the model version was imported.
ImportJobEndTime (datetime) --
The date and time when the import job completed. This field appears if the model version was imported.
ImportedDataSizeInBytes (integer) --
The size in bytes of the imported data. This field appears if the model version was imported.
PriorModelMetrics (string) --
If the model version was retrained, this field shows a summary of the performance of the prior model on the new training range. You can use the information in this JSON-formatted object to compare the new model version and the prior model version.
RetrainingAvailableDataInDays (integer) --
Indicates the number of days of data used in the most recent scheduled retraining run.
AutoPromotionResult (string) --
Indicates whether the model version was promoted to be the active version after retraining or if there was an error with or cancellation of the retraining.
AutoPromotionResultReason (string) --
Indicates the reason for the AutoPromotionResult. For example, a model might not be promoted if its performance was worse than the active version, if there was an error during training, or if the retraining scheduler was using MANUAL promote mode. The model will be promoted in MANAGED promote mode if the performance is better than the previous model.
{'InferenceDataImportStrategy': 'NO_IMPORT | ADD_WHEN_EMPTY | OVERWRITE'}
Imports a model that has been trained successfully.
See also: AWS API Documentation
Request Syntax
client.import_model_version(
SourceModelVersionArn='string',
ModelName='string',
DatasetName='string',
LabelsInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
ClientToken='string',
RoleArn='string',
ServerSideKmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
InferenceDataImportStrategy='NO_IMPORT'|'ADD_WHEN_EMPTY'|'OVERWRITE'
)
string
[REQUIRED]
The Amazon Resource Name (ARN) of the model version to import.
string
The name for the machine learning model to be created. If the model already exists, Amazon Lookout for Equipment creates a new version. If you do not specify this field, it is filled with the name of the source model.
string
[REQUIRED]
The name of the dataset for the machine learning model being imported.
dict
Contains the configuration information for the S3 location being used to hold label data.
S3InputConfiguration (dict) --
Contains location information for the S3 location being used for label data.
Bucket (string) -- [REQUIRED]
The name of the S3 bucket holding the label data.
Prefix (string) --
The prefix for the S3 bucket used for the label data.
LabelGroupName (string) --
The name of the label group to be used for label data.
string
[REQUIRED]
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
This field is autopopulated if not provided.
string
The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.
string
Provides the identifier of the KMS key key used to encrypt model data by Amazon Lookout for Equipment.
list
The tags associated with the machine learning model to be created.
(dict) --
A tag is a key-value pair that can be added to a resource as metadata.
Key (string) -- [REQUIRED]
The key for the specified tag.
Value (string) -- [REQUIRED]
The value for the specified tag.
string
Indicates how to import the accumulated inference data when a model version is imported. The possible values are as follows:
NO_IMPORT – Don't import the data.
ADD_WHEN_EMPTY – Only import the data from the source model if there is no existing data in the target model.
OVERWRITE – Import the data from the source model and overwrite the existing data in the target model.
dict
Response Syntax
{
'ModelName': 'string',
'ModelArn': 'string',
'ModelVersionArn': 'string',
'ModelVersion': 123,
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED'
}
Response Structure
(dict) --
ModelName (string) --
The name for the machine learning model.
ModelArn (string) --
The Amazon Resource Name (ARN) of the model being created.
ModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version being created.
ModelVersion (integer) --
The version of the model being created.
Status (string) --
The status of the ImportModelVersion operation.
{'InferenceExecutionSummaries': {'ModelVersion': 'long',
'ModelVersionArn': 'string'}}
Lists all inference executions that have been performed by the specified inference scheduler.
See also: AWS API Documentation
Request Syntax
client.list_inference_executions(
NextToken='string',
MaxResults=123,
InferenceSchedulerName='string',
DataStartTimeAfter=datetime(2015, 1, 1),
DataEndTimeBefore=datetime(2015, 1, 1),
Status='IN_PROGRESS'|'SUCCESS'|'FAILED'
)
string
An opaque pagination token indicating where to continue the listing of inference executions.
integer
Specifies the maximum number of inference executions to list.
string
[REQUIRED]
The name of the inference scheduler for the inference execution listed.
datetime
The time reference in the inferenced dataset after which Amazon Lookout for Equipment started the inference execution.
datetime
The time reference in the inferenced dataset before which Amazon Lookout for Equipment stopped the inference execution.
string
The status of the inference execution.
dict
Response Syntax
{
'NextToken': 'string',
'InferenceExecutionSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'InferenceSchedulerName': 'string',
'InferenceSchedulerArn': 'string',
'ScheduledStartTime': datetime(2015, 1, 1),
'DataStartTime': datetime(2015, 1, 1),
'DataEndTime': datetime(2015, 1, 1),
'DataInputConfiguration': {
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'InputTimeZoneOffset': 'string',
'InferenceInputNameConfiguration': {
'TimestampFormat': 'string',
'ComponentTimestampDelimiter': 'string'
}
},
'DataOutputConfiguration': {
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
},
'CustomerResultObject': {
'Bucket': 'string',
'Key': 'string'
},
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED',
'FailedReason': 'string',
'ModelVersion': 123,
'ModelVersionArn': 'string'
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of inference executions.
InferenceExecutionSummaries (list) --
Provides an array of information about the individual inference executions returned from the ListInferenceExecutions operation, including model used, inference scheduler, data configuration, and so on.
(dict) --
Contains information about the specific inference execution, including input and output data configuration, inference scheduling information, status, and so on.
ModelName (string) --
The name of the machine learning model being used for the inference execution.
ModelArn (string) --
The Amazon Resource Name (ARN) of the machine learning model used for the inference execution.
InferenceSchedulerName (string) --
The name of the inference scheduler being used for the inference execution.
InferenceSchedulerArn (string) --
The Amazon Resource Name (ARN) of the inference scheduler being used for the inference execution.
ScheduledStartTime (datetime) --
Indicates the start time at which the inference scheduler began the specific inference execution.
DataStartTime (datetime) --
Indicates the time reference in the dataset at which the inference execution began.
DataEndTime (datetime) --
Indicates the time reference in the dataset at which the inference execution stopped.
DataInputConfiguration (dict) --
Specifies configuration information for the input data for the inference scheduler, including delimiter, format, and dataset location.
S3InputConfiguration (dict) --
Specifies configuration information for the input data for the inference, including Amazon S3 location of input data.
Bucket (string) --
The bucket containing the input dataset for the inference.
Prefix (string) --
The prefix for the S3 bucket used for the input data for the inference.
InputTimeZoneOffset (string) --
Indicates the difference between your time zone and Coordinated Universal Time (UTC).
InferenceInputNameConfiguration (dict) --
Specifies configuration information for the input data for the inference, including timestamp format and delimiter.
TimestampFormat (string) --
The format of the timestamp, whether Epoch time, or standard, with or without hyphens (-).
ComponentTimestampDelimiter (string) --
Indicates the delimiter character used between items in the data.
DataOutputConfiguration (dict) --
Specifies configuration information for the output results from for the inference execution, including the output Amazon S3 location.
S3OutputConfiguration (dict) --
Specifies configuration information for the output results from for the inference, output S3 location.
Bucket (string) --
The bucket containing the output results from the inference
Prefix (string) --
The prefix for the S3 bucket used for the output results from the inference.
KmsKeyId (string) --
The ID number for the KMS key key used to encrypt the inference output.
CustomerResultObject (dict) --
The S3 object that the inference execution results were uploaded to.
Bucket (string) --
The name of the specific S3 bucket.
Key (string) --
The Amazon Web Services Key Management Service (KMS key) key being used to encrypt the S3 object. Without this key, data in the bucket is not accessible.
Status (string) --
Indicates the status of the inference execution.
FailedReason (string) --
Specifies the reason for failure when an inference execution has failed.
ModelVersion (integer) --
The model version used for the inference execution.
ModelVersionArn (string) --
The Amazon Resource Number (ARN) of the model version used for the inference execution.
{'ModelSummaries': {'LatestScheduledRetrainingModelVersion': 'long',
'LatestScheduledRetrainingStartTime': 'timestamp',
'LatestScheduledRetrainingStatus': 'IN_PROGRESS | SUCCESS '
'| FAILED | '
'IMPORT_IN_PROGRESS | '
'CANCELED',
'NextScheduledRetrainingStartDate': 'timestamp',
'RetrainingSchedulerStatus': 'PENDING | RUNNING | STOPPING '
'| STOPPED'}}
Generates a list of all models in the account, including model name and ARN, dataset, and status.
See also: AWS API Documentation
Request Syntax
client.list_models(
NextToken='string',
MaxResults=123,
Status='IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
ModelNameBeginsWith='string',
DatasetNameBeginsWith='string'
)
string
An opaque pagination token indicating where to continue the listing of machine learning models.
integer
Specifies the maximum number of machine learning models to list.
string
The status of the machine learning model.
string
The beginning of the name of the machine learning models being listed.
string
The beginning of the name of the dataset of the machine learning models to be listed.
dict
Response Syntax
{
'NextToken': 'string',
'ModelSummaries': [
{
'ModelName': 'string',
'ModelArn': 'string',
'DatasetName': 'string',
'DatasetArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS',
'CreatedAt': datetime(2015, 1, 1),
'ActiveModelVersion': 123,
'ActiveModelVersionArn': 'string',
'LatestScheduledRetrainingStatus': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'|'CANCELED',
'LatestScheduledRetrainingModelVersion': 123,
'LatestScheduledRetrainingStartTime': datetime(2015, 1, 1),
'NextScheduledRetrainingStartDate': datetime(2015, 1, 1),
'RetrainingSchedulerStatus': 'PENDING'|'RUNNING'|'STOPPING'|'STOPPED'
},
]
}
Response Structure
(dict) --
NextToken (string) --
An opaque pagination token indicating where to continue the listing of machine learning models.
ModelSummaries (list) --
Provides information on the specified model, including created time, model and dataset ARNs, and status.
(dict) --
Provides information about the specified machine learning model, including dataset and model names and ARNs, as well as status.
ModelName (string) --
The name of the machine learning model.
ModelArn (string) --
The Amazon Resource Name (ARN) of the machine learning model.
DatasetName (string) --
The name of the dataset being used for the machine learning model.
DatasetArn (string) --
The Amazon Resource Name (ARN) of the dataset used to create the model.
Status (string) --
Indicates the status of the machine learning model.
CreatedAt (datetime) --
The time at which the specific model was created.
ActiveModelVersion (integer) --
The model version that the inference scheduler uses to run an inference execution.
ActiveModelVersionArn (string) --
The Amazon Resource Name (ARN) of the model version that is set as active. The active model version is the model version that the inference scheduler uses to run an inference execution.
LatestScheduledRetrainingStatus (string) --
Indicates the status of the most recent scheduled retraining run.
LatestScheduledRetrainingModelVersion (integer) --
Indicates the most recent model version that was generated by retraining.
LatestScheduledRetrainingStartTime (datetime) --
Indicates the start time of the most recent scheduled retraining run.
NextScheduledRetrainingStartDate (datetime) --
Indicates the date that the next scheduled retraining run will start on. Lookout for Equipment truncates the time you provide to the nearest UTC day.
RetrainingSchedulerStatus (string) --
Indicates the status of the retraining scheduler.