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 pytorch_lightning.loggers.csv_logs.CSVLogger(save_dir, name='lightning_logs', version=None, prefix='', flush_logs_every_n_steps=100)[source]¶
Bases:
pytorch_lightning.loggers.logger.Logger,lightning_fabric.loggers.csv_logs.CSVLoggerLog 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
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 ofNoneor an int.- Return type
- class pytorch_lightning.loggers.csv_logs.ExperimentWriter(log_dir)[source]¶
Bases:
lightning_fabric.loggers.csv_logs._ExperimentWriterExperiment writer for CSVLogger.
Currently, supports to log hyperparameters and metrics in YAML and CSV format, respectively.
This logger supports logging to remote filesystems via
fsspec. Make sure you have it installed.