Logging¶
Supported Loggers¶
The following are loggers we support:
Track your parameters, metrics, source code and more using Comet. |
|
Log to local file system in yaml and CSV format. |
|
Log using MLflow. |
|
Log using Neptune. |
|
Log to local file system in TensorBoard format. |
|
Log using Weights and Biases. |
The above loggers will normally plot an additional chart (global_step VS epoch). Depending on the loggers you use, there might be some additional charts too.
By default, Lightning uses TensorBoard
logger under the hood, and stores the logs to a directory (by default in lightning_logs/
).
from lightning.pytorch import Trainer
# Automatically logs to a directory (by default ``lightning_logs/``)
trainer = Trainer()
To see your logs:
tensorboard --logdir=lightning_logs/
To visualize tensorboard in a jupyter notebook environment, run the following command in a jupyter cell:
%reload_ext tensorboard
%tensorboard --logdir=lightning_logs/
You can also pass a custom Logger to the Trainer
.
from lightning.pytorch import loggers as pl_loggers
tb_logger = pl_loggers.TensorBoardLogger(save_dir="logs/")
trainer = Trainer(logger=tb_logger)
Choose from any of the others such as MLflow, Comet, Neptune, WandB, etc.
comet_logger = pl_loggers.CometLogger(save_dir="logs/")
trainer = Trainer(logger=comet_logger)
To use multiple loggers, simply pass in a list
or tuple
of loggers.
tb_logger = pl_loggers.TensorBoardLogger(save_dir="logs/")
comet_logger = pl_loggers.CometLogger(save_dir="logs/")
trainer = Trainer(logger=[tb_logger, comet_logger])
Note
By default, Lightning logs every 50 steps. Use Trainer flags to Control Logging Frequency.
Note
By default, all loggers log to os.getcwd()
. You can change the logging path using
Trainer(default_root_dir="/your/path/to/save/checkpoints")
without instantiating a logger.
Logging from a LightningModule¶
Lightning offers automatic log functionalities for logging scalars, or manual logging for anything else.
Automatic Logging¶
Use the log()
or log_dict()
methods to log from anywhere in a LightningModule and callbacks.
def training_step(self, batch, batch_idx):
self.log("my_metric", x)
# or a dict to log all metrics at once with individual plots
def training_step(self, batch, batch_idx):
self.log_dict({"acc": acc, "recall": recall})
Note
Everything explained below applies to both log()
or log_dict()
methods.
Depending on where the log()
method is called, Lightning auto-determines
the correct logging mode for you. Of course you can override the default behavior by manually setting the
log()
parameters.
def training_step(self, batch, batch_idx):
self.log("my_loss", loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
The log()
method has a few options:
on_step
: Logs the metric at the current step.on_epoch
: Automatically accumulates and logs at the end of the epoch.prog_bar
: Logs to the progress bar (Default:False
).logger
: Logs to the logger likeTensorboard
, or any other custom logger passed to theTrainer
(Default:True
).reduce_fx
: Reduction function over step values for end of epoch. Usestorch.mean()
by default and is not applied when atorchmetrics.Metric
is logged.enable_graph
: If True, will not auto detach the graph.sync_dist
: If True, reduces the metric across devices. Use with care as this may lead to a significant communication overhead.sync_dist_group
: The DDP group to sync across.add_dataloader_idx
: If True, appends the index of the current dataloader to the name (when using multiple dataloaders). If False, user needs to give unique names for each dataloader to not mix the values.batch_size
: Current batch size used for accumulating logs logged withon_epoch=True
. This will be directly inferred from the loaded batch, but for some data structures you might need to explicitly provide it.rank_zero_only
: Whether the value will be logged only on rank 0. This will prevent synchronization which would produce a deadlock as not all processes would perform this log call.
Hook |
on_step |
on_epoch |
---|---|---|
on_train_start, on_train_epoch_start, on_train_epoch_end |
False |
True |
on_before_backward, on_after_backward, on_before_optimizer_step, on_before_zero_grad |
True |
False |
on_train_batch_start, on_train_batch_end, training_step |
True |
False |
on_validation_start, on_validation_epoch_start, on_validation_epoch_end |
False |
True |
on_validation_batch_start, on_validation_batch_end, validation_step |
False |
True |
Note
While logging tensor metrics with on_epoch=True
inside step-level hooks and using mean-reduction (default) to accumulate the metrics across the current epoch, Lightning tries to extract the
batch size from the current batch. If multiple possible batch sizes are found, a warning is logged and if it fails to extract the batch size from the current batch, which is possible if
the batch is a custom structure/collection, then an error is raised. To avoid this, you can specify the batch_size
inside the self.log(... batch_size=batch_size)
call.
def training_step(self, batch, batch_idx):
# extracts the batch size from `batch`
self.log("train_loss", loss, on_epoch=True)
def validation_step(self, batch, batch_idx):
# uses `batch_size=10`
self.log("val_loss", loss, batch_size=10)
Note
The above config for
validation
applies fortest
hooks as well.Setting
on_epoch=True
will cache all your logged values during the full training epoch and perform a reduction inon_train_epoch_end
. We recommend using TorchMetrics, when working with custom reduction.Setting both
on_step=True
andon_epoch=True
will create two keys per metric you log with suffix_step
and_epoch
respectively. You can refer to these keys e.g. in the monitor argument ofModelCheckpoint
or in the graphs plotted to the logger of your choice.
If your work requires to log in an unsupported method, please open an issue with a clear description of why it is blocking you.
Manual Logging Non-Scalar Artifacts¶
If you want to log anything that is not a scalar, like histograms, text, images, etc., you may need to use the logger object directly.
def training_step(self):
...
# the logger you used (in this case tensorboard)
tensorboard = self.logger.experiment
tensorboard.add_image()
tensorboard.add_histogram(...)
tensorboard.add_figure(...)
Make a Custom Logger¶
You can implement your own logger by writing a class that inherits from Logger
.
Use the rank_zero_experiment()
and rank_zero_only()
decorators to make sure that only the first process in DDP training creates the experiment and logs the data respectively.
from lightning.pytorch.loggers.logger import Logger, rank_zero_experiment
from lightning.pytorch.utilities import rank_zero_only
class MyLogger(Logger):
@property
def name(self):
return "MyLogger"
@property
def version(self):
# Return the experiment version, int or str.
return "0.1"
@rank_zero_only
def log_hyperparams(self, params):
# params is an argparse.Namespace
# your code to record hyperparameters goes here
pass
@rank_zero_only
def log_metrics(self, metrics, step):
# metrics is a dictionary of metric names and values
# your code to record metrics goes here
pass
@rank_zero_only
def save(self):
# Optional. Any code necessary to save logger data goes here
pass
@rank_zero_only
def finalize(self, status):
# Optional. Any code that needs to be run after training
# finishes goes here
pass
If you write a logger that may be useful to others, please send a pull request to add it to Lightning!
Control Logging Frequency¶
Progress Bar¶
You can add any metric to the progress bar using log()
method, setting prog_bar=True
.
def training_step(self, batch, batch_idx):
self.log("my_loss", loss, prog_bar=True)
You could learn more about progress bars supported by Lightning here.
Modifying the Progress Bar¶
The progress bar by default already includes the training loss and version number of the experiment
if you are using a logger. These defaults can be customized by overriding the
get_metrics()
hook in your logger.
from lightning.pytorch.callbacks.progress import TQDMProgressBar
class CustomProgressBar(TQDMProgressBar):
def get_metrics(self, *args, **kwargs):
# don't show the version number
items = super().get_metrics()
items.pop("v_num", None)
return items
Configure Console Logging¶
Lightning logs useful information about the training process and user warnings to the console. You can retrieve the Lightning console logger and change it to your liking. For example, adjust the logging level or redirect output for certain modules to log files:
import logging
# configure logging at the root level of Lightning
logging.getLogger("lightning.pytorch").setLevel(logging.ERROR)
# configure logging on module level, redirect to file
logger = logging.getLogger("lightning.pytorch.core")
logger.addHandler(logging.FileHandler("core.log"))
Read more about custom Python logging here.
Logging Hyperparameters¶
When training a model, it is useful to know what hyperparams went into that model.
When Lightning creates a checkpoint, it stores a key "hyper_parameters"
with the hyperparams.
lightning_checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
hyperparams = lightning_checkpoint["hyper_parameters"]
Some loggers also allow logging the hyperparams used in the experiment. For instance,
when using the TensorBoardLogger
, all hyperparams will show
in the hparams tab at torch.utils.tensorboard.writer.SummaryWriter.add_hparams()
.
Note
If you want to track a metric in the tensorboard hparams tab, log scalars to the key hp_metric
. If tracking multiple metrics, initialize TensorBoardLogger
with default_hp_metric=False
and call log_hyperparams
only once with your metric keys and initial values. Subsequent updates can simply be logged to the metric keys. Refer to the examples below for setting up proper hyperparams metrics tracking within the LightningModule.
# Using default_hp_metric
def validation_step(self, batch, batch_idx):
self.log("hp_metric", some_scalar)
# Using custom or multiple metrics (default_hp_metric=False)
def on_train_start(self):
self.logger.log_hyperparams(self.hparams, {"hp/metric_1": 0, "hp/metric_2": 0})
def validation_step(self, batch, batch_idx):
self.log("hp/metric_1", some_scalar_1)
self.log("hp/metric_2", some_scalar_2)
In the example, using "hp/"
as a prefix allows for the metrics to be grouped under “hp” in the tensorboard scalar tab where you can collapse them.
Managing Remote Filesystems¶
Lightning supports saving logs to a variety of filesystems, including local filesystems and several cloud storage providers.
Check out the Remote Filesystems doc for more info.