utils.rc.dtos.prediction_service
Module Contents
Classes
Attributes
- utils.rc.dtos.prediction_service.logger
- class utils.rc.dtos.prediction_service.PredictionService(data={})
- static create_service(name: str, description: str, model_name: str, service_obj_path: str, env_id: str, data_source_ids: List[str], model_version: str = None) PredictionService
Creates a prediction service
- Parameters:
name (str) – name of the service
description (str) – description of the service
model_name (str) – name of the model
model_version (str) – version of the model
service_obj_path (str) – location of the py file for service
env_id (str) – id of the environment
data_source_ids (list[str]) – list of datasource ids
- Returns:
created prediction service object
- Return type:
- static update_service(service_id: str, name: str, description: str, model_name: str, env_id: str, data_source_ids: list[str]) PredictionService
Updates prediction service
- Parameters:
service_id (str) – id of the service
name (str) – name of the service
description (str) – description of the service
model_name (str) – model name
env_id (str) – id of the environment
data_source_ids (list[str]) – list of data source ids
- Returns:
updated prediction service object
- Return type:
- static get_service_by_name(name: str) PredictionService
Get Prediction service by name
- Parameters:
name (str) – name of the prediction service
- Returns:
prediction service object
- Return type:
- static delete_service_by_id(service_id: str)
Deletes the prediction service by id
- Parameters:
service_id (str) – id of the service
- static get_signed_url_for_upload(service_id: str, file_name: str)
- static refresh_service(service_name: str)
Refresh the prediction service. Use this method to reflect the updated changes in your service
- Parameters:
service_name (str) – name of the service
- static predict_by_service(service_name: str, payload)
Call the prediction service to do predictions :param service_name: name of the service :type service_name: str :param payload: payload :type payload: dict
- Returns:
response
- Return type:
dict
- static get_all_models() List[str]
Get all the models associated with the tenant :returns: list of all the model names :rtype: List[str]
- static delete_model(name: str)
Delete the model :param name: name of the model
- Returns:
None
- static get_model_details(name: str)
- classmethod download_file(name: str, file_name: str, folder: str = None, folder_type: str = 'model', model_version: str = 'default') None
Download a file from model to your local file system :param folder_type: models or predictions :param name: name of the model :type name: str :param file_name: name of the file in model file system :type file_name: str :param folder: folder path to download, if None, takes current folder :type folder: str :param model_version: version of the model :type model_version: str
- Returns:
None
- classmethod download_model(name: str, folder: str = None) None
Download all the files in model to specified folder :param name: name of the model :param folder: folder path to download, if None, takes current folder
- Returns:
None
- classmethod download_for_local(model_name: str, tenant_id: str)
Download a model for local use. This class method allows the user to download a model specified by its name for local use. The model will be associated with the specified tenant ID. :param model_name: The name of the model to be downloaded. :type model_name: str :param tenant_id: The ID of the tenant associated with the downloaded model. :type tenant_id: str
- Returns:
None
- static prepare_local_docker(service_name)