Initialization

automl.interface. init ( engine = 'dask' , engine_opts = {'dask_scheduler': None} , logger = None , loglevel = None , check_deprecation_warnings = True )

Initializes the AutoMLx framework’s execution engine. AutoMLx can work with a variety of parallelization platforms.

Warning

Dask and Spark parallelization frameworks and their options are deprecated and will be removed in version 23.3.0.

Parameters

  • engine ( str ) –

    Name of the parallelization framework. Can be one of:

    • 'local' : Use Python’s inbuilt multiprocessing framework.

    • 'threading' : Use Python’s inbuilt multithreading framework.

    • 'dask' : Use Dask as the distributed compute engine.

    Defaults to 'dask' .

  • engine_opts ( dict ) –

    Options for the parallelization framework. When engine is:

    • 'local' : engine_opts is of the form {'n_jobs' : val1, 'model_n_jobs' : val2} , where val1 is the degree of inter-model parallelism and val2 is the degree of intra-model parallelism.

    • 'threading' : engine_opts is of the form {'n_jobs' : val} , where val is the degree of parallelism.

    • 'dask' : engine_opts is of the form {'dask_scheduler' : val} , where val can be one of

      • URI of the dask scheduler (e.g. 127.0.0.1:8786)

      • an already initialized dask.Client instance

      • None

    Defaults to {'dask_scheduler' : None} .

  • logger (logging.Logger, str, optional ) –

    Logging mode. One of

    • None : Log to console with specified loglevel (by default logging.WARNING ).

    • str : Log to the provided file path and console.

    • logging.Logger : Use existing Logger object.

    Defaults to None .

  • loglevel (int, optional ) – Log level is derived from the python logging module, and adjusts the logging verbosity in the following increasing order: logging.CRITICAL < logging.WARNING < logging.INFO < logging.DEBUG . Defaults to None (use logging module’s configuration). Setting the loglevel here does nothing if the root logger already has handlers configured.

  • check_deprecation_warnings (bool, optional ) – If true, the deprecation warnings for the package will be checked and displayed; however, it affects the running time of the AutoML pipeline.