csv_logs¶

Classes

`CSVLogger`	Log to local file system in yaml and CSV format.
`ExperimentWriter`	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='default', version=None, prefix='')[source]¶

Bases: pytorch_lightning.loggers.base.LightningLoggerBase

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¶ (Optional[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.

finalize(status)[source]¶

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)[source]¶

Record hyperparameters.

Parameters

params¶ (Union[Dict[str, Any], Namespace]) – Namespace containing the hyperparameters
args¶ – Optional positional arguments, depends on the specific logger being used
kwargs¶ – Optional keywoard arguments, depends on the specific logger being used

Return type

None

log_metrics(metrics, step=None)[source]¶

Records metrics. This method logs metrics as as soon as it received them. If you want to aggregate metrics for one specific step, use the agg_and_log_metrics() method.

Parameters

metrics¶ (Dict[str, 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()[source]¶

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()

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 name: str¶

Return the experiment name.

Return type: str

property root_dir: str¶

Parent directory for all checkpoint subdirectories. If the experiment name parameter is None or the empty string, no experiment subdirectory is used and the checkpoint will be saved in “save_dir/version_dir”

Return type: str

property save_dir: Optional[str]¶

Return the root directory where experiment logs get saved, or None if the logger does not save data locally.

Return type: Optional[str]

property version: int¶

Return the experiment version.

Return type: int

class pytorch_lightning.loggers.csv_logs.ExperimentWriter(log_dir)[source]¶

Bases: object

Experiment writer for CSVLogger.

Currently supports to log hyperparameters and metrics in YAML and CSV format, respectively.

Parameters: log_dir¶ (str) – Directory for the experiment logs

log_hparams(params)[source]¶

Record hparams

Return type: None

log_metrics(metrics_dict, step=None)[source]¶

Record metrics

Return type: None

save()[source]¶

Save recorded hparams and metrics into files

Return type: None