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, Set, Union
from torch import Tensor
from typing_extensions import override
from lightning.fabric.loggers.logger import Logger, rank_zero_experiment
from lightning.fabric.utilities.cloud_io import _is_dir, get_filesystem
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.
If the version is specified, and the directory already contains a metrics file for that version, it will be
overwritten.
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__()
root_dir = os.fspath(root_dir)
self._root_dir = root_dir
self._name = name or ""
self._version = version
self._prefix = prefix
self._fs = get_filesystem(root_dir)
self._experiment: Optional[_ExperimentWriter] = None
self._flush_logs_every_n_steps = flush_logs_every_n_steps
@property
@override
def name(self) -> str:
"""Gets the name of the experiment.
Returns:
The name of the experiment.
"""
return self._name
@property
@override
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
@override
def root_dir(self) -> str:
"""Gets the save directory where the versioned CSV experiments are saved."""
return self._root_dir
@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, self.name, version)
@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] @override
@rank_zero_only
def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: # type: ignore[override]
raise NotImplementedError("The `CSVLogger` does not yet support logging hyperparameters.")
[docs] @override
@rank_zero_only
def log_metrics( # type: ignore[override]
self, metrics: Dict[str, Union[Tensor, float]], step: Optional[int] = None
) -> None:
metrics = _add_prefix(metrics, self._prefix, self.LOGGER_JOIN_CHAR)
if step is None:
step = len(self.experiment.metrics)
self.experiment.log_metrics(metrics, step)
if (step + 1) % self._flush_logs_every_n_steps == 0:
self.save()
[docs] @override
@rank_zero_only
def save(self) -> None:
super().save()
self.experiment.save()
[docs] @override
@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:
versions_root = os.path.join(self._root_dir, self.name)
if not _is_dir(self._fs, versions_root, strict=True):
log.warning("Missing logger folder: %s", versions_root)
return 0
existing_versions = []
for d in self._fs.listdir(versions_root):
full_path = d["name"]
name = os.path.basename(full_path)
if _is_dir(self._fs, full_path) and name.startswith("version_"):
dir_ver = name.split("_")[1]
if dir_ver.isdigit():
existing_versions.append(int(dir_ver))
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.metrics_keys: List[str] = []
self._fs = get_filesystem(log_dir)
self.log_dir = log_dir
self.metrics_file_path = os.path.join(self.log_dir, self.NAME_METRICS_FILE)
self._check_log_dir_exists()
self._fs.makedirs(self.log_dir, exist_ok=True)
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
new_keys = self._record_new_keys()
file_exists = self._fs.isfile(self.metrics_file_path)
if new_keys and file_exists:
# we need to re-write the file if the keys (header) change
self._rewrite_with_new_header(self.metrics_keys)
with self._fs.open(self.metrics_file_path, mode=("a" if file_exists else "w"), newline="") as file:
writer = csv.DictWriter(file, fieldnames=self.metrics_keys)
if not file_exists:
# only write the header if we're writing a fresh file
writer.writeheader()
writer.writerows(self.metrics)
self.metrics = [] # reset
def _record_new_keys(self) -> Set[str]:
"""Records new keys that have not been logged before."""
current_keys = set().union(*self.metrics)
new_keys = current_keys - set(self.metrics_keys)
self.metrics_keys.extend(new_keys)
self.metrics_keys.sort()
return new_keys
def _rewrite_with_new_header(self, fieldnames: List[str]) -> None:
with self._fs.open(self.metrics_file_path, "r", newline="") as file:
metrics = list(csv.DictReader(file))
with self._fs.open(self.metrics_file_path, "w", newline="") as file:
writer = csv.DictWriter(file, fieldnames=fieldnames)
writer.writeheader()
writer.writerows(metrics)
def _check_log_dir_exists(self) -> None:
if self._fs.exists(self.log_dir) and self._fs.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!"
)
if self._fs.isfile(self.metrics_file_path):
self._fs.rm_file(self.metrics_file_path)
