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='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 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, 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()

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

Gets the name of the experiment.

Return type:

str

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”

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.

property version: Union[int, str]

Gets the version of the experiment.

Return type:

Union[int, str]

Returns:

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

class pytorch_lightning.loggers.csv_logs.ExperimentWriter(log_dir)

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)

Record hparams.

Return type:

None

log_metrics(metrics_dict, step=None)

Record metrics.

Return type:

None

save()

Save recorded hparams and metrics into files.

Return type:

None