csv_logs¶

Classes

`CSVLogger`	Log to local file system in CSV and YAML format.
`ExperimentWriter`	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: Logger, CSVLogger

Log to local file system in CSV and YAML format.

Metrics (from self.log() calls) are logged to CSV format while hyperparameters (from self.log_hyperparams() or self.save_hyperparameters()) are logged to YAML format.

Logs are saved to os.path.join(save_dir, name, version). The CSV file is named metrics.csv and the YAML file is named hparams.yaml.

This logger supports logging to remote filesystems via fsspec. Make sure you have it installed.

Example:

from lightning.pytorch import Trainer
from lightning.pytorch.loggers import CSVLogger

# Basic usage
logger = CSVLogger("logs", name="my_exp_name")
trainer = Trainer(logger=logger)

Use the logger anywhere in your LightningModule as follows:

import torch
from lightning.pytorch import LightningModule

class LitModel(LightningModule):
    def __init__(self, learning_rate=0.001, batch_size=32):
        super().__init__()
        # This will log hyperparameters to hparams.yaml
        self.save_hyperparameters()

    def training_step(self, batch, batch_idx):
        loss = self.compute_loss(batch)
        # This will log metrics to metrics.csv
        self.log("train_loss", loss)
        return loss

    def configure_optimizers(self):
        return torch.optim.Adam(self.parameters(), lr=self.hparams.learning_rate)

You can also manually log hyperparameters:

# Log additional hyperparameters manually
logger.log_hyperparams({"dropout": 0.2, "optimizer": "adam"})

File Structure:

The logger creates the following files in the log directory:

metrics.csv: Contains metrics from self.log() calls
hparams.yaml: Contains hyperparameters from self.save_hyperparameters() or self.log_hyperparams()

Parameters:

save_dir¶ (Union[str, Path]) – Save directory
name¶ (Optional[str]) – Experiment name, optional. Defaults to 'lightning_logs'. If name is None, logs (versions) will be stored to the save dir directly.
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).

log_hyperparams(params=None)[source]¶

Log hyperparameters to YAML format.

Hyperparameters are saved to hparams.yaml in the log directory. This method is automatically called when using self.save_hyperparameters() in your LightningModule, but can also be called manually.

Parameters:: params¶ (Union[dict[str, Any], Namespace, None]) – Dictionary or Namespace containing hyperparameters to log.
Return type:: None

Example

>>> logger = CSVLogger("logs")
>>> logger.log_hyperparams({"learning_rate": 0.001, "batch_size": 32})
>>> # This creates logs/lightning_logs/version_0/hparams.yaml

property experiment: _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 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.

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

Bases: _ExperimentWriter

Experiment writer for CSVLogger.

Logs metrics in CSV format and hyperparameters in YAML format.

This logger supports logging to remote filesystems via fsspec. Make sure you have it installed.

Parameters:: log_dir¶ (str) – Directory where experiment logs will be saved.

log_hparams(params)[source]¶

Save hyperparameters to a YAML file in the log directory.

This method updates the internal hyperparameters dictionary and saves all hyperparameters to hparams.yaml in the log directory.

Parameters:: params¶ (dict[str, Any]) – Dictionary of hyperparameters to log.
Return type:: None