2024/02/14 - Amazon Lookout for Equipment - 5 updated api methods
Changes This feature allows customers to see pointwise model diagnostics results for their models.
{'ModelDiagnosticsOutputConfiguration': {'KmsKeyId': 'string',
'S3OutputConfiguration': {'Bucket': 'string',
'Prefix': 'string'}}}
Creates a machine learning model for data inference.
A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred.
Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy.
See also: AWS API Documentation
Request Syntax
client.create_model(
ModelName='string',
DatasetName='string',
DatasetSchema={
'InlineDataSchema': 'string'
},
LabelsInputConfiguration={
'S3InputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'LabelGroupName': 'string'
},
ClientToken='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'
},
ServerSideKmsKeyId='string',
Tags=[
{
'Key': 'string',
'Value': 'string'
},
],
OffCondition='string',
ModelDiagnosticsOutputConfiguration={
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
}
)
string
[REQUIRED]
The name for the machine learning model to be created.
string
[REQUIRED]
The name of the dataset for the machine learning model being created.
dict
The data schema for the machine learning model being created.
InlineDataSchema (string) --
The data schema used within the given dataset.
dict
The input configuration for the labels being used for the machine learning model that's being created.
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.
datetime
Indicates the time reference in the dataset that should be used to begin the subset of training data for the machine learning model.
datetime
Indicates the time reference in the dataset that should be used to end the subset of training data for the machine learning model.
datetime
Indicates the time reference in the dataset that should be used to begin the subset of evaluation data for the machine learning model.
datetime
Indicates the time reference in the dataset that should be used to end the subset of evaluation data for the machine learning model.
string
The Amazon Resource Name (ARN) of a role with permission to access the data source being used to create the machine learning model.
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
string
Provides the identifier of the KMS key used to encrypt model data by Amazon Lookout for Equipment.
list
Any tags associated with the machine learning model being 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 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.
dict
The Amazon S3 location where you want Amazon Lookout for Equipment to save the pointwise model diagnostics. You must also specify the RoleArn request parameter.
S3OutputConfiguration (dict) -- [REQUIRED]
The Amazon S3 location for the pointwise model diagnostics.
Bucket (string) -- [REQUIRED]
The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
Prefix (string) --
The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. ( bucket ).
When you call CreateModel or UpdateModel , specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz .
When you call DescribeModel or DescribeModelVersion , prefix contains the file path and filename of the model evaluation file.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
dict
Response Syntax
{
'ModelArn': 'string',
'Status': 'IN_PROGRESS'|'SUCCESS'|'FAILED'|'IMPORT_IN_PROGRESS'
}
Response Structure
(dict) --
ModelArn (string) --
The Amazon Resource Name (ARN) of the model being created.
Status (string) --
Indicates the status of the CreateModel operation.
{'ModelDiagnosticsOutputConfiguration': {'KmsKeyId': 'string',
'S3OutputConfiguration': {'Bucket': 'string',
'Prefix': 'string'}}}
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',
'ModelDiagnosticsOutputConfiguration': {
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
}
}
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.
ModelDiagnosticsOutputConfiguration (dict) --
Configuration information for the model's pointwise model diagnostics.
S3OutputConfiguration (dict) --
The Amazon S3 location for the pointwise model diagnostics.
Bucket (string) --
The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
Prefix (string) --
The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. ( bucket ).
When you call CreateModel or UpdateModel , specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz .
When you call DescribeModel or DescribeModelVersion , prefix contains the file path and filename of the model evaluation file.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
{'ModelDiagnosticsOutputConfiguration': {'KmsKeyId': 'string',
'S3OutputConfiguration': {'Bucket': 'string',
'Prefix': 'string'}},
'ModelDiagnosticsResultsObject': {'Bucket': 'string', 'Key': 'string'}}
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',
'ModelDiagnosticsOutputConfiguration': {
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
},
'ModelDiagnosticsResultsObject': {
'Bucket': 'string',
'Key': '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.
ModelDiagnosticsOutputConfiguration (dict) --
The Amazon S3 location where Amazon Lookout for Equipment saves the pointwise model diagnostics for the model version.
S3OutputConfiguration (dict) --
The Amazon S3 location for the pointwise model diagnostics.
Bucket (string) --
The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
Prefix (string) --
The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. ( bucket ).
When you call CreateModel or UpdateModel , specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz .
When you call DescribeModel or DescribeModelVersion , prefix contains the file path and filename of the model evaluation file.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
ModelDiagnosticsResultsObject (dict) --
The Amazon S3 output prefix for where Lookout for Equipment saves the pointwise model diagnostics for the model version.
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.
{'ModelSummaries': {'ModelDiagnosticsOutputConfiguration': {'KmsKeyId': 'string',
'S3OutputConfiguration': {'Bucket': 'string',
'Prefix': 'string'}}}}
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',
'ModelDiagnosticsOutputConfiguration': {
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': 'string'
}
},
]
}
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.
ModelDiagnosticsOutputConfiguration (dict) --
Output configuration information for the pointwise model diagnostics for an Amazon Lookout for Equipment model.
S3OutputConfiguration (dict) --
The Amazon S3 location for the pointwise model diagnostics.
Bucket (string) --
The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
Prefix (string) --
The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. ( bucket ).
When you call CreateModel or UpdateModel , specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz .
When you call DescribeModel or DescribeModelVersion , prefix contains the file path and filename of the model evaluation file.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
{'ModelDiagnosticsOutputConfiguration': {'KmsKeyId': 'string',
'S3OutputConfiguration': {'Bucket': 'string',
'Prefix': 'string'}}}
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',
ModelDiagnosticsOutputConfiguration={
'S3OutputConfiguration': {
'Bucket': 'string',
'Prefix': 'string'
},
'KmsKeyId': '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.
dict
The Amazon S3 location where you want Amazon Lookout for Equipment to save the pointwise model diagnostics for the model. You must also specify the RoleArn request parameter.
S3OutputConfiguration (dict) -- [REQUIRED]
The Amazon S3 location for the pointwise model diagnostics.
Bucket (string) -- [REQUIRED]
The name of the Amazon S3 bucket where the pointwise model diagnostics are located. You must be the owner of the Amazon S3 bucket.
Prefix (string) --
The Amazon S3 prefix for the location of the pointwise model diagnostics. The prefix specifies the folder and evaluation result file name. ( bucket ).
When you call CreateModel or UpdateModel , specify the path within the bucket that you want Lookout for Equipment to save the model to. During training, Lookout for Equipment creates the model evaluation model as a compressed JSON file with the name model_diagnostics_results.json.gz .
When you call DescribeModel or DescribeModelVersion , prefix contains the file path and filename of the model evaluation file.
KmsKeyId (string) --
The Amazon Web Services Key Management Service (KMS) key identifier to encrypt the pointwise model diagnostics files.
None