Iterations

MLOps Iterations contain information about a single machine learning model run

mlops.tracking.start_iteration

Function creates an instance of Iteration

Arguments:

• iteration_name: string

  name of the created iteration

• project_id: string, optional

  Id of the target project. By default value is the id of the active project

• experiment_id: string, optional

  Id of the target experiment. By default value is the id of the active experiment

• send_email: bool, optional

  If true email will be sent to email address specified in library settings. False by default.

Returns:

• iteration dictionary JSON data of the created iteration

iteration.log_model_name

Function logs the model name in the currently running iteration.

Arguments:

• model_name: string

  Name of the model that’s being tracked

iteration.log_path_to_model

Function logs the path to model file

Arguments:

• path_to_model: string

  Path to the file containing the tracked model

iteration.log_metric

Function logs a single metric along with it’s value

Arguments:

• metric_name: string

  Name of the logged metric

• value:

  Value of the logged metric

iteration.log_metrics

Function logs multiple metrics at once

Arguments:

• metrics: dictionary

  Dictionary containing metric: value pairs that are going to be logged

iteration.log_parameter

Function logs a single parameter along with it’s value

Arguments:

• parameter_name:

  Name of the logged parameter

• value:

  Value of the logged parameter

iteration.log_parameters

Function logs multiple parameters at once

Arguments:

• parameters: dictionary

  Dictionary containing parameter: value pairs that are going to be logged

iteration.log_dataset

Function logs an existing dataset with an iteration.

Arguments:

• dataset_id: string

  Id of an existing dataset in webapp

iteration.end_iteration

Function ends the iteration and sends the logged data to the MLOps App

Returns:

• iteration: dictionary

  JSON data of created iteration