# 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.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
CSV logger
----------
CSV logger for basic experiment logging that does not require opening ports
"""
import os
from argparse import Namespace
from typing import Any, Optional, Union
from typing_extensions import override
from lightning_fabric.loggers.csv_logs import CSVLogger as FabricCSVLogger
from lightning_fabric.loggers.csv_logs import _ExperimentWriter as _FabricExperimentWriter
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 pytorch_lightning.core.saving import save_hparams_to_yaml
from pytorch_lightning.loggers.logger import Logger
from pytorch_lightning.utilities.rank_zero import rank_zero_only
class ExperimentWriter(_FabricExperimentWriter):
r"""Experiment 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.
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] = {}
def log_hparams(self, params: dict[str, Any]) -> None:
"""Record hparams and save into files."""
self.hparams.update(params)
hparams_file = os.path.join(self.log_dir, self.NAME_HPARAMS_FILE)
save_hparams_to_yaml(hparams_file, self.hparams)
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 pytorch_lightning import Trainer
>>> from pytorch_lightning.loggers import CSVLogger
>>> logger = CSVLogger("logs", name="my_exp_name")
>>> trainer = Trainer(logger=logger)
Args:
save_dir: Save directory
name: Experiment name, optional. Defaults to ``'lightning_logs'``. If name is ``None``, logs
(versions) will be stored to the save dir directly.
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: Optional[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
@override
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, self.name)
@property
@override
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
@override
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
@override
@rank_zero_only
def log_hyperparams(self, params: Optional[Union[dict[str, Any], Namespace]] = None) -> None:
params = _convert_params(params)
self.experiment.log_hparams(params)
@property
@override
@rank_zero_experiment
def experiment(self) -> _FabricExperimentWriter:
r"""Actual _ExperimentWriter object. To use _ExperimentWriter features in your
:class:`~pytorch_lightning.core.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