CSVLogger

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

Bases: pytorch_lightning.loggers.logger.Logger

Log to local file system in yaml and CSV format.

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

Example

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

Parameters:

• save_dir (str) – Save directory

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

• 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).

finalize(status)

Do any processing that is necessary to finalize an experiment.

Parameters:

status (str) – Status that the experiment finished with (e.g. success, failed, aborted)

Return type:

None

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

log_metrics(metrics, step=None)

Records metrics. This method logs metrics as soon as it received them.

Parameters:

• metrics (Dict[str, Union[Tensor, float]]) – Dictionary with metric names as keys and measured quantities as values

• step (Optional[int]) – Step number at which the metrics should be recorded

Return type:

None

save()

Save log data.

Return type:

None

property experiment: pytorch_lightning.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()

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.

property name: str

Gets the name of the experiment.

Returns:

The name of the experiment.

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”

property save_dir: str

The current directory where logs are saved.

Returns:

The path to current directory where logs are saved.

property version: Union[int, str]

Gets the version of the experiment.

Returns:

The version of the experiment if it is specified, else the next version.