csv_logs¶
Classes
| Log to local file system in yaml and CSV format. | |
| Experiment writer for CSVLogger. | 
CSV logger¶
CSV logger for basic experiment logging that does not require opening ports
- class lightning.pytorch.loggers.csv_logs.CSVLogger(save_dir, name='lightning_logs', version=None, prefix='', flush_logs_every_n_steps=100)[source]¶
- 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
- 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).
 
 - property experiment: lightning.fabric.loggers.csv_logs._ExperimentWriter¶
- Actual _ExperimentWriter object. To use _ExperimentWriter features in your - LightningModuledo 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- Noneor an int.- Return type