Source code for lightning.pytorch.loggers.csv_logs

# Copyright The Lightning AI team.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# distributed under the License is distributed on an "AS IS" BASIS,
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CSV logger

CSV logger for basic experiment logging that does not require opening ports

import logging
import os
from argparse import Namespace
from typing import Any, Dict, Optional, Union

from lightning.fabric.loggers.csv_logs import _ExperimentWriter as _FabricExperimentWriter
from lightning.fabric.loggers.csv_logs import CSVLogger as FabricCSVLogger
from lightning.fabric.loggers.logger import rank_zero_experiment
from lightning.fabric.utilities.logger import _convert_params
from lightning.fabric.utilities.types import _PATH
from lightning.pytorch.core.saving import save_hparams_to_yaml
from lightning.pytorch.loggers.logger import Logger
from lightning.pytorch.utilities.rank_zero import rank_zero_only

log = logging.getLogger(__name__)

[docs]class ExperimentWriter(_FabricExperimentWriter): r"""Experiment writer for CSVLogger. Currently, supports to log hyperparameters and metrics in YAML and CSV format, respectively. Args: log_dir: Directory for the experiment logs """ NAME_HPARAMS_FILE = "hparams.yaml" def __init__(self, log_dir: str) -> None: super().__init__(log_dir=log_dir) self.hparams: Dict[str, Any] = {}
[docs] def log_hparams(self, params: Dict[str, Any]) -> None: """Record hparams.""" self.hparams.update(params)
[docs] def save(self) -> None: """Save recorded hparams and metrics into files.""" hparams_file = os.path.join(self.log_dir, self.NAME_HPARAMS_FILE) save_hparams_to_yaml(hparams_file, self.hparams) return super().save()
[docs]class CSVLogger(Logger, FabricCSVLogger): r"""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) Args: save_dir: Save directory name: Experiment name. Defaults to ``'lightning_logs'``. version: Experiment version. If version is not specified the logger inspects the save directory for existing versions, then automatically assigns the next available version. prefix: A string to put at the beginning of metric keys. flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps). """ LOGGER_JOIN_CHAR = "-" def __init__( self, save_dir: _PATH, name: str = "lightning_logs", version: Optional[Union[int, str]] = None, prefix: str = "", flush_logs_every_n_steps: int = 100, ): super().__init__( root_dir=save_dir, name=name, version=version, prefix=prefix, flush_logs_every_n_steps=flush_logs_every_n_steps, ) self._save_dir = os.fspath(save_dir) @property def root_dir(self) -> 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 os.path.join(self.save_dir, @property def log_dir(self) -> 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. """ # create a pseudo standard path version = self.version if isinstance(self.version, str) else f"version_{self.version}" return os.path.join(self.root_dir, version) @property def save_dir(self) -> str: """The current directory where logs are saved. Returns: The path to current directory where logs are saved. """ return self._save_dir
[docs] @rank_zero_only def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: params = _convert_params(params) self.experiment.log_hparams(params)
@property @rank_zero_experiment def experiment(self) -> _FabricExperimentWriter: r""" Actual _ExperimentWriter object. To use _ExperimentWriter features in your :class:`~lightning.pytorch.core.module.LightningModule` do the following. Example:: self.logger.experiment.some_experiment_writer_function() """ if self._experiment is not None: return self._experiment self._fs.makedirs(self.root_dir, exist_ok=True) self._experiment = ExperimentWriter(log_dir=self.log_dir) return self._experiment

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