CSVLogger

class lightning.pytorch.loggers.CSVLogger(save_dir, name='lightning_logs', version=None, prefix='', flush_logs_every_n_steps=100)

Bases: lightning.pytorch.loggers.logger.Logger, lightning.fabric.loggers.csv_logs.CSVLogger

Log to local file system in yaml and CSV format.

Logs are saved to os.path.join(save_dir, name, version).

Example

>>> from lightning.pytorch import Trainer
>>> from lightning.pytorch.loggers import CSVLogger
>>> logger = CSVLogger("logs", name="my_exp_name")
>>> trainer = Trainer(logger=logger)

Parameters

• save_dir (Union[str, Path]) – Save directory

• name (str) – Experiment name. Defaults to 'lightning_logs'.

• version (Union[int, str, None]) – Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version.

• prefix (str) – A string to put at the beginning of metric keys.

• flush_logs_every_n_steps (int) – How often to flush logs to disk (defaults to every 100 steps).

log_hyperparams(params)

Record hyperparameters.

Parameters

• params (Union[Dict[str, Any], Namespace]) – Namespace or Dict containing the hyperparameters

• args – Optional positional arguments, depends on the specific logger being used

• kwargs – Optional keyword arguments, depends on the specific logger being used

Return type

None

property experiment: lightning.fabric.loggers.csv_logs._ExperimentWriter

Actual _ExperimentWriter object. To use _ExperimentWriter features in your LightningModule do the following.

Example:

self.logger.experiment.some_experiment_writer_function()

Return type

_ExperimentWriter

property log_dir: str

The log directory for this run.

By default, it is named 'version_${self.version}' but it can be overridden by passing a string value for the constructor’s version parameter instead of None or an int.

Return type

str

property root_dir: str

Parent directory for all checkpoint subdirectories.

If the experiment name parameter is an empty string, no experiment subdirectory is used and the checkpoint will be saved in “save_dir/version”

Return type

str

property save_dir: str

The current directory where logs are saved.

Return type

str

Returns

The path to current directory where logs are saved.