Seeq Add-on User Interface

class seeq.addons.azureml._add_on_main.MlOperate(sdl_notebook_url, config_file=None)[source]

Bases: object

Main class for the Seeq - Azure Ml integration Add-on. Creates an instance of the Add-on UI and passes the callbacks for the different events.

workbook_id

The ID of the Seeq workbook where the Add-on was instantiated

Type: str

config_file

The configuration file with the necessary information to connect to the Azure ML service. By default, the file is located in ~/.seeq/aml_config.ini

Type: str

default_time_delta

Default time delta to populate the start time of the investigate range

Type: str

deploy_frequency

The schedule frequency for the “deploy” option as a pandas.Timedelta

Type: pd.Timedelta

inputs_provider

An instance of the ModelInputsProvider object that gets the input signal IDs from Seeq that are required by the Azure ML model.

Type: seeq.addons.azureml.backend.ModelInputsProvider

app

An instance of the Add-on UI

Type: seeq.addons.azureml.ui_components.AppLayout

class seeq.addons.azureml.backend._seeq_inputs_provider.ModelInputsProvider(config_file=None)[source]

Bases: object

Provides the Seeq and AmlOnlineEndpointService inputs to the Azure ML model based upon user selections

endpoint_svc

An instance of the AmlOnlineEndpointService to make the necessary calls to Azure ML services.

Type: seeq.addons.azureml.backend.AmlOnlineEndpointService

endpoints

Dictionary with endpoint names as keys and OnlineEndpoint(s) as values.

Type: dict

deployment

Deployment associated with the selected OnlineEndpoint.

Type: seeq.addons.azureml.backend.OnlineDeployment

asset_paths

Dictionary containing the valid Seeq asset trees on which the model may be applied. The name of the asset trees are the keys and asset tree IDs are the values.

Type: dict

model_name

Name of the Azure ML model for the selected OnlineDeployment.

Type: str

model_version

Version of the Azure ML model for the selected OnlineDeployment.

Type: str

model_signal_inputs

Dictionary with the input signals for the Azure ML model which are pulled from Seeq. The name of the signals are the keys of the dict and the IDs of the signals are the values.

Type: dict

model_sample_rate

The sampling rate required by the Azure ML model for the input signals. For example, ‘2 min’.

Type: str

model_endpoint_uri

The endpoint identifier of the AzureML model used to compute the result signal.

Type: str

asset_path_from_signals

This attribute is determined when the Azure ML model specifies signal IDs as inputs rather than asset path IDs. If the input signals belong to the same asset tree, then the name and ID of the asset tree are stored as a key-value pair, {name: ID}. Otherwise, this attribute will default to None.

Type: dict