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Experiment Tracking

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Experiment Tracking

The Base Tracker Class

class accelerate.tracking.GeneralTracker

< >

( _blank = False )

A base Tracker class to be used for all logging integration implementations.

Each function should take in **kwargs that will automatically be passed in from a base dictionary provided to Accelerator.

Should implement name, requires_logging_directory, and tracker properties such that:

name (str): String representation of the tracker class name, such as “TensorBoard” requires_logging_directory (bool): Whether the logger requires a directory to store their logs. tracker (object): Should return internal tracking mechanism used by a tracker class (such as the run for wandb)

Implementations can also include a main_process_only (bool) attribute to toggle if relevent logging, init, and other functions should occur on the main process or across all processes (by default will use True)

finish

< >

( )

Should run any finalizing functions within the tracking API. If the API should not have one, just don’t overwrite that method.

log

< >

( values: dict step: typing.Optional[int] **kwargs )

Parameters

  • values (Dictionary str to str, float, or int) — Values to be logged as key-value pairs. The values need to have type str, float, or int.
  • step (int, optional) — The run step. If included, the log will be affiliated with this step.

Logs values to the current run. Base log implementations of a tracking API should go in here, along with special behavior for the `step parameter.

store_init_configuration

< >

( values: dict )

Parameters

  • values (Dictionary str to bool, str, float or int) — Values to be stored as initial hyperparameters as key-value pairs. The values need to have type bool, str, float, int, or None.

Logs values as hyperparameters for the run. Implementations should use the experiment configuration functionality of a tracking API.

Integrated Trackers

class accelerate.tracking.TensorBoardTracker

< >

( run_name: str logging_dir: typing.Union[str, os.PathLike] **kwargs )

Parameters

  • run_name (str) — The name of the experiment run
  • logging_dir (str, os.PathLike) — Location for TensorBoard logs to be stored. kwargs — Additional key word arguments passed along to the tensorboard.SummaryWriter.__init__ method.

A Tracker class that supports tensorboard. Should be initialized at the start of your script.

__init__

< >

( run_name: str logging_dir: typing.Union[str, os.PathLike] **kwargs )

class accelerate.tracking.WandBTracker

< >

( run_name: str **kwargs )

Parameters

  • run_name (str) — The name of the experiment run. kwargs — Additional key word arguments passed along to the wandb.init method.

A Tracker class that supports wandb. Should be initialized at the start of your script.

__init__

< >

( run_name: str **kwargs )

class accelerate.tracking.CometMLTracker

< >

( run_name: str **kwargs )

Parameters

  • run_name (str) — The name of the experiment run. kwargs — Additional key word arguments passed along to the Experiment.__init__ method.

A Tracker class that supports comet_ml. Should be initialized at the start of your script.

API keys must be stored in a Comet config file.

__init__

< >

( run_name: str **kwargs )

class accelerate.tracking.AimTracker

< >

( run_name: str logging_dir: typing.Union[str, os.PathLike, NoneType] = '.' **kwargs )

Parameters

  • run_name (str) — The name of the experiment run. kwargs — Additional key word arguments passed along to the Run.__init__ method.

A Tracker class that supports aim. Should be initialized at the start of your script.

__init__

< >

( run_name: str logging_dir: typing.Union[str, os.PathLike, NoneType] = '.' **kwargs )

class accelerate.tracking.MLflowTracker

< >

( experiment_name: str = None logging_dir: typing.Union[str, os.PathLike, NoneType] = '.' run_id: typing.Optional[str] = None tags: typing.Union[typing.Dict[str, typing.Any], str, NoneType] = None nested_run: typing.Optional[bool] = False run_name: typing.Optional[str] = None description: typing.Optional[str] = None )

Parameters

  • experiment_name (str, optional) — Name of the experiment. Environment variable MLFLOW_EXPERIMENT_NAME has priority over this argument.
  • logging_dir (str or os.PathLike, defaults to ".") — Location for mlflow logs to be stored.
  • run_id (str, optional) — If specified, get the run with the specified UUID and log parameters and metrics under that run. The run’s end time is unset and its status is set to running, but the run’s other attributes (source_version, source_type, etc.) are not changed. Environment variable MLFLOW_RUN_ID has priority over this argument.
  • tags (Dict[str, str], optional) — An optional dict of str keys and values, or a str dump from a dict, to set as tags on the run. If a run is being resumed, these tags are set on the resumed run. If a new run is being created, these tags are set on the new run. Environment variable MLFLOW_TAGS has priority over this argument.
  • nested_run (bool, optional, defaults to False) — Controls whether run is nested in parent run. True creates a nested run. Environment variable MLFLOW_NESTED_RUN has priority over this argument.
  • run_name (str, optional) — Name of new run (stored as a mlflow.runName tag). Used only when run_id is unspecified.
  • description (str, optional) — An optional string that populates the description box of the run. If a run is being resumed, the description is set on the resumed run. If a new run is being created, the description is set on the new run.

A Tracker class that supports mlflow. Should be initialized at the start of your script.

__init__

< >

( experiment_name: str = None logging_dir: typing.Union[str, os.PathLike, NoneType] = '.' run_id: typing.Optional[str] = None tags: typing.Union[typing.Dict[str, typing.Any], str, NoneType] = None nested_run: typing.Optional[bool] = False run_name: typing.Optional[str] = None description: typing.Optional[str] = None )

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