Source code for lightning_fabric.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.
# 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.
import csv
import logging
import os
from argparse import Namespace
from typing import Any, Dict, List, Optional, Union
from torch import Tensor
from lightning_fabric.loggers.logger import Logger, rank_zero_experiment
from lightning_fabric.utilities.logger import _add_prefix
from lightning_fabric.utilities.rank_zero import rank_zero_only, rank_zero_warn
from lightning_fabric.utilities.types import _PATH
log = logging.getLogger(__name__)
[docs]class CSVLogger(Logger):
r"""
Log to the local file system in CSV format.
Logs are saved to ``os.path.join(root_dir, name, version)``.
Args:
root_dir: The root directory in which all your experiments with different names and versions will be stored.
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).
Example::
from lightning.fabric.loggers import CSVLogger
logger = CSVLogger("path/to/logs/root", name="my_model")
logger.log_metrics({"loss": 0.235, "acc": 0.75})
logger.finalize("success")
"""
LOGGER_JOIN_CHAR = "-"
def __init__(
self,
root_dir: _PATH,
name: str = "lightning_logs",
version: Optional[Union[int, str]] = None,
prefix: str = "",
flush_logs_every_n_steps: int = 100,
):
super().__init__()
self._root_dir = os.fspath(root_dir)
self._name = name or ""
self._version = version
self._prefix = prefix
self._experiment: Optional[_ExperimentWriter] = None
self._flush_logs_every_n_steps = flush_logs_every_n_steps
@property
def name(self) -> str:
"""Gets the name of the experiment.
Returns:
The name of the experiment.
"""
return self._name
@property
def version(self) -> Union[int, str]:
"""Gets the version of the experiment.
Returns:
The version of the experiment if it is specified, else the next version.
"""
if self._version is None:
self._version = self._get_next_version()
return self._version
@property
def root_dir(self) -> str:
"""Gets the save directory where the versioned CSV experiments are saved."""
return self._root_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}"
log_dir = os.path.join(self.root_dir, self.name, version)
return log_dir
@property
@rank_zero_experiment
def experiment(self) -> "_ExperimentWriter":
"""Actual ExperimentWriter object. To use ExperimentWriter features anywhere in your code, do the
following.
Example::
self.logger.experiment.some_experiment_writer_function()
"""
if self._experiment is not None:
return self._experiment
os.makedirs(self.root_dir, exist_ok=True)
self._experiment = _ExperimentWriter(log_dir=self.log_dir)
return self._experiment
[docs] @rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None:
raise NotImplementedError("The `CSVLogger` does not yet support logging hyperparameters.")
[docs] @rank_zero_only
def log_metrics(self, metrics: Dict[str, Union[Tensor, float]], step: Optional[int] = None) -> None:
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
self.experiment.log_metrics(metrics, step)
if step is not None and (step + 1) % self._flush_logs_every_n_steps == 0:
self.save()
[docs] @rank_zero_only
def finalize(self, status: str) -> None:
if self._experiment is None:
# When using multiprocessing, finalize() should be a no-op on the main process, as no experiment has been
# initialized there
return
self.save()
def _get_next_version(self) -> int:
root_dir = self.root_dir
if not os.path.isdir(root_dir):
log.warning("Missing logger folder: %s", root_dir)
return 0
existing_versions = []
for d in os.listdir(root_dir):
if os.path.isdir(os.path.join(root_dir, d)) and d.startswith("version_"):
existing_versions.append(int(d.split("_")[1]))
if len(existing_versions) == 0:
return 0
return max(existing_versions) + 1
class _ExperimentWriter:
r"""
Experiment writer for CSVLogger.
Args:
log_dir: Directory for the experiment logs
"""
NAME_METRICS_FILE = "metrics.csv"
def __init__(self, log_dir: str) -> None:
self.metrics: List[Dict[str, float]] = []
self.log_dir = log_dir
if os.path.exists(self.log_dir) and os.listdir(self.log_dir):
rank_zero_warn(
f"Experiment logs directory {self.log_dir} exists and is not empty."
" Previous log files in this directory will be deleted when the new ones are saved!"
)
os.makedirs(self.log_dir, exist_ok=True)
self.metrics_file_path = os.path.join(self.log_dir, self.NAME_METRICS_FILE)
def log_metrics(self, metrics_dict: Dict[str, float], step: Optional[int] = None) -> None:
"""Record metrics."""
def _handle_value(value: Union[Tensor, Any]) -> Any:
if isinstance(value, Tensor):
return value.item()
return value
if step is None:
step = len(self.metrics)
metrics = {k: _handle_value(v) for k, v in metrics_dict.items()}
metrics["step"] = step
self.metrics.append(metrics)
def save(self) -> None:
"""Save recorded metrics into files."""
if not self.metrics:
return
last_m = {}
for m in self.metrics:
last_m.update(m)
metrics_keys = list(last_m.keys())
with open(self.metrics_file_path, "w", newline="") as f:
writer = csv.DictWriter(f, fieldnames=metrics_keys)
writer.writeheader()
writer.writerows(self.metrics)
