edsnlp.training.loggers

Directory in which to store the JSON file.

TYPE: str or PathLike

Name of the JSON file. Defaults to "metrics.json".

TYPE: str DEFAULT: 'metrics.json'

Directory in which to store the CSV.

TYPE: str or PathLike

Name of the CSV file. Defaults to "metrics.csv".

TYPE: str DEFAULT: 'metrics.csv'

Field descriptors containing goal ("lower_is_better" or "higher_is_better"), format and display name The key is a regex that will be used to match the fields to log Each entry of the dictionary should match the following scheme:

• key: a regex to match columns
• value: either a Dict or False to hide the column, the dict format is
  • name: the name of the column
  • goal: "lower_is_better" or "higher_is_better"

This defaults to a set of metrics and stats that are commonly logged during EDS-NLP training.

TYPE: Dict[str, Union[Dict, bool]] DEFAULT: None

Key to group the logs

TYPE: Optional[str] DEFAULT: None

Whether to replace the tqdm progress bar with a rich progress bar. Indeed, rich progress bars integrate better with the rich table.

TYPE: bool DEFAULT: True

Name of the project.

TYPE: str

Directory in which to store the Aim logs. The environment variable AIM_LOGGING_DIR takes precedence over this argument.

TYPE: Optional[Union[str, PathLike]] DEFAULT: None

Additional keyword arguments to pass to the Aim init function.

DEFAULT: {}

Name of the project.

TYPE: str

Directory in which to store the TensorBoard logs. Logs of different runs will be stored in logging_dir/project_name. The environment variable TENSORBOARD_LOGGING_DIR takes precedence over this argument.

TYPE: Optional[Union[str, PathLike]] DEFAULT: None

Name of the project. This will become the project parameter in wandb.init.

TYPE: str

Additional keyword arguments to pass to the WandB init function.

DEFAULT: {}

Name of the project. This will become the mlflow experiment name.

TYPE: str

Directory in which to store the MLflow logs.

TYPE: Optional[Union[str, PathLike]] DEFAULT: None

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.

TYPE: Optional[str] DEFAULT: None

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.

TYPE: Optional[Union[Dict[str, Any], str]] DEFAULT: None

Controls whether run is nested in parent run. True creates a nested run. Environment variable MLFLOW_NESTED_RUN has priority over this argument.

TYPE: Optional[bool] DEFAULT: False

Name of new run (stored as a mlflow.runName tag). Used only when run_id is unspecified.

TYPE: Optional[str] DEFAULT: None

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.

TYPE: Optional[str] DEFAULT: None

Name of the project.

TYPE: str

Additional keyword arguments to pass to the CometML Experiment object.

DEFAULT: {}