abacusai.deployment
Module Contents
Classes
A model deployment |
- class abacusai.deployment.Deployment(client, deploymentId=None, name=None, status=None, description=None, deployedAt=None, createdAt=None, projectId=None, modelId=None, modelVersion=None, featureGroupId=None, featureGroupVersion=None, callsPerSecond=None, autoDeploy=None, regions=None, error=None, refreshSchedules={})
Bases:
abacusai.return_class.AbstractApiClass
A model deployment
- Parameters
client (ApiClient) – An authenticated API Client instance
deploymentId (str) – The unique identifier for the deployment.
name (str) – The user-friendly name for the deployment.
status (str) – The status of the deployment.
description (str) – A description of this deployment.
deployedAt (str) – When the deployment last became active.
createdAt (str) – When the deployment was created.
projectId (str) – The unique identifier of the project this deployment belongs to.
modelId (str) – The model that is currently deployed.
modelVersion (str) – The model version ID that is currently deployed.
featureGroupId (str) – The feature group that is currently deployed.
featureGroupVersion (str) – The feature group version ID that is currently deployed.
callsPerSecond (int) – The number of calls per second the deployment could handle.
autoDeploy (bool) – Flag marking the deployment eligible for auto deployments whenever any model in the project finishes training.
regions (list of strings) – List of regions that a deployment has been deployed to
error (str) – Relevant error if the status is FAILED
refreshSchedules (RefreshSchedule) – List of refresh schedules that indicate when the deployment will be updated to the latest model version
- __repr__(self)
Return repr(self).
- to_dict(self)
Get a dict representation of the parameters in this class
- Returns
The dict value representation of the class parameters
- Return type
- refresh(self)
Calls describe and refreshes the current object’s fields
- Returns
The current object
- Return type
- describe(self)
Retrieves a full description of the specified deployment.
- Parameters
deployment_id (str) – The unique ID associated with the deployment.
- Returns
The description of the deployment.
- Return type
- update(self, description=None)
Updates a deployment’s description.
- Parameters
description (str) – The new deployment description.
- rename(self, name)
Updates a deployment’s name and/or description.
- Parameters
name (str) – The new deployment name.
- set_auto(self, enable=None)
Enable/Disable auto deployment for the specified deployment.
When a model is scheduled to retrain, deployments with this enabled will be marked to automatically promote the new model version. After the newly trained model completes, a check on its metrics in comparison to the currently deployed model version will be performed. If the metrics are comparable or better, the newly trained model version is automatically promoted. If not, it will be marked as a failed model version promotion with an error indicating poor metrics performance.
- Parameters
enable (bool) – Enable/disable the autoDeploy property of the Deployment.
- set_model_version(self, model_version)
Promotes a Model Version to be served in the Deployment
- Parameters
model_version (str) – The unique ID for the Model Version
- set_feature_group_version(self, feature_group_version)
Promotes a Feature Group Version to be served in the Deployment
- Parameters
feature_group_version (str) – The unique ID for the Feature Group Version
- start(self)
Restarts the specified deployment that was previously suspended.
- Parameters
deployment_id (str) – The unique ID associated with the deployment.
- delete(self)
Deletes the specified deployment. The deployment’s models will not be affected. Note that the deployments are not recoverable after they are deleted.
- Parameters
deployment_id (str) – The ID of the deployment to delete.
- set_feature_group_export_file_connector_output(self, output_format=None, output_location=None)
Sets the export output for the Feature Group Deployment to be a file connector.
- set_feature_group_export_database_connector_output(self, database_connector_id=None, object_name=None, write_mode=None, database_feature_mapping=None, id_column=None)
Sets the export output for the Feature Group Deployment to be a Database connector.
- Parameters
database_connector_id (str) – The database connector ID used
object_name (str) – The database connector’s object to write to
write_mode (str) – UPSERT or INSERT for writing to the database connector
database_feature_mapping (dict) – The column/feature pairs mapping the features to the database columns
id_column (str) – The id column to use as the upsert key
- remove_feature_group_export_output(self)
Removes the export type that is set for the Feature Group Deployment
- Parameters
deployment_id (str) – The deployment for which the export type is set
- create_batch_prediction(self, table_name=None, name=None, global_prediction_args=None, explanations=False, output_format=None, output_location=None, database_connector_id=None, database_output_config=None, refresh_schedule=None, csv_input_prefix=None, csv_prediction_prefix=None, csv_explanations_prefix=None)
Creates a batch prediction job description for the given deployment.
- Parameters
table_name (str) – If specified, the name of the feature group table to write the results of the batch prediction. Can only be specified iff outputLocation and databaseConnectorId are not specified. If table_name is specified, the outputType will be enforced as CSV
name (str) – The name of batch prediction job.
global_prediction_args (dict) – Argument(s) to pass on every prediction call.
explanations (bool) – If true, will provide SHAP Explanations for each prediction, if supported by the use case.
output_format (str) – If specified, sets the format of the batch prediction output (CSV or JSON)
output_location (str) – If specified, the location to write the prediction results. Otherwise, results will be stored in Abacus.AI.
database_connector_id (str) – The unique identifier of an Database Connection to write predictions to. Cannot be specified in conjunction with outputLocation.
database_output_config (dict) – A key-value pair of columns/values to write to the database connector. Only available if databaseConnectorId is specified.
refresh_schedule (str) – A cron-style string that describes a schedule in UTC to automatically run the batch prediction.
csv_input_prefix (str) – A prefix to prepend to the input columns, only applies when output format is CSV
csv_prediction_prefix (str) – A prefix to prepend to the prediction columns, only applies when output format is CSV
csv_explanations_prefix (str) – A prefix to prepend to the explanation columns, only applies when output format is CSV
- Returns
The batch prediction description.
- Return type
- wait_for_deployment(self, wait_states={'PENDING', 'DEPLOYING'}, timeout=480)
A waiting call until deployment is completed.
- Parameters
timeout (int, optional) – The waiting time given to the call to finish, if it doesn’t finish by the allocated time, the call is said to be timed out. Default value given is 480 milliseconds.
- get_status(self)
Gets the status of the deployment.
- Returns
A string describing the status of a deploymet (pending, deploying, active, etc.).
- Return type
- create_refresh_policy(self, cron)
To create a refresh policy for a deployment.
- Parameters
cron (str) – A cron style string to set the refresh time.
- Returns
The refresh policy object.
- Return type
- list_refresh_policies(self)
Gets the refresh policies in a list.
- Returns
A list of refresh policy objects.
- Return type
List[RefreshPolicy]