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update docs on logging (#3916) 

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.. testsetup:: *
from pytorch_lightning.trainer.trainer import Trainer
from pytorch_lightning.core.lightning import LightningModule
.. _experiment_logging:
Experiment Logging
==================
Comet.ml
^^^^^^^^
`Comet.ml <https://www.comet.ml/site/>`_ is a third-party logger.
To use :class:`~pytorch_lightning.loggers.CometLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install comet-ml
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
import os
from pytorch_lightning.loggers import CometLogger
comet_logger = CometLogger(
api_key=os.environ.get('COMET_API_KEY'),
workspace=os.environ.get('COMET_WORKSPACE'), # Optional
save_dir='.', # Optional
project_name='default_project', # Optional
rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
experiment_name='default' # Optional
)
trainer = Trainer(logger=comet_logger)
The :class:`~pytorch_lightning.loggers.CometLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.CometLogger` docs.
----------------
MLflow
^^^^^^
`MLflow <https://mlflow.org/>`_ is a third-party logger.
To use :class:`~pytorch_lightning.loggers.MLFlowLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install mlflow
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
from pytorch_lightning.loggers import MLFlowLogger
mlf_logger = MLFlowLogger(
experiment_name="default",
tracking_uri="file:./ml-runs"
)
trainer = Trainer(logger=mlf_logger)
.. seealso::
:class:`~pytorch_lightning.loggers.MLFlowLogger` docs.
----------------
Neptune.ai
^^^^^^^^^^
`Neptune.ai <https://neptune.ai/>`_ is a third-party logger.
To use :class:`~pytorch_lightning.loggers.NeptuneLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install neptune-client
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
from pytorch_lightning.loggers import NeptuneLogger
neptune_logger = NeptuneLogger(
api_key='ANONYMOUS', # replace with your own
project_name='shared/pytorch-lightning-integration',
experiment_name='default', # Optional,
params={'max_epochs': 10}, # Optional,
tags=['pytorch-lightning', 'mlp'], # Optional,
)
trainer = Trainer(logger=neptune_logger)
The :class:`~pytorch_lightning.loggers.NeptuneLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.NeptuneLogger` docs.
----------------
Tensorboard
^^^^^^^^^^^
To use `TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your logger do the following.
.. testcode::
from pytorch_lightning.loggers import TensorBoardLogger
logger = TensorBoardLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)
The :class:`~pytorch_lightning.loggers.TensorBoardLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.TensorBoardLogger` docs.
----------------
Test Tube
^^^^^^^^^
`Test Tube <https://github.com/williamFalcon/test-tube>`_ is a
`TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ logger but with nicer file structure.
To use :class:`~pytorch_lightning.loggers.TestTubeLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install test_tube
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
from pytorch_lightning.loggers import TestTubeLogger
logger = TestTubeLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)
The :class:`~pytorch_lightning.loggers.TestTubeLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.TestTubeLogger` docs.
----------------
Weights and Biases
^^^^^^^^^^^^^^^^^^
`Weights and Biases <https://www.wandb.com/>`_ is a third-party logger.
To use :class:`~pytorch_lightning.loggers.WandbLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install wandb
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. code-block:: python
from pytorch_lightning.loggers import WandbLogger
wandb_logger = WandbLogger(offline=True)
trainer = Trainer(logger=wandb_logger)
The :class:`~pytorch_lightning.loggers.WandbLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.log({
"generated_images": [wandb.Image(some_img, caption="...")]
})
.. seealso::
:class:`~pytorch_lightning.loggers.WandbLogger` docs.
----------------
Multiple Loggers
^^^^^^^^^^^^^^^^
Lightning supports the use of multiple loggers, just pass a list to the
:class:`~pytorch_lightning.trainer.trainer.Trainer`.
.. testcode::
from pytorch_lightning.loggers import TensorBoardLogger, TestTubeLogger
logger1 = TensorBoardLogger('tb_logs', name='my_model')
logger2 = TestTubeLogger('tb_logs', name='my_model')
trainer = Trainer(logger=[logger1, logger2])
The loggers are available as a list anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
# Option 1
self.logger.experiment[0].add_image('generated_images', some_img, 0)
# Option 2
self.logger[0].experiment.add_image('generated_images', some_img, 0)

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.. testsetup:: *
from pytorch_lightning.trainer.trainer import Trainer
.. _experiment_reporting:
Experiment Reporting
=====================
Lightning supports many different experiment loggers. These loggers allow you to monitor losses, images, text, etc...
as training progresses. They usually provide a GUI to visualize and can sometimes even snapshot hyperparameters
used in each experiment.
----------
Control logging frequency
^^^^^^^^^^^^^^^^^^^^^^^^^
It may slow training down to log every single batch. Trainer has an option to log every k batches instead.
.. testcode::
k = 10
trainer = Trainer(row_log_interval=k)
----------
Control log writing frequency
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Writing to a logger can be expensive. In Lightning you can set the interval at which you
want to save logs to the filesystem using this trainer flag.
.. testcode::
k = 100
trainer = Trainer(log_save_interval=k)
Unlike the `row_log_interval`, this argument does not apply to all loggers.
The example shown here works with :class:`~pytorch_lightning.loggers.tensorboard.TensorBoardLogger`,
which is the default logger in Lightning.
----------
Log metrics
^^^^^^^^^^^
To plot metrics into whatever logger you passed in (tensorboard, comet, neptune, etc...)
1. training_epoch_end, validation_epoch_end, test_epoch_end will all log anything in the "log" key of the return dict.
.. testcode::
def training_epoch_end(self, outputs):
loss = some_loss()
...
logs = {'train_loss': loss}
results = {'log': logs}
return results
def validation_epoch_end(self, outputs):
loss = some_loss()
...
logs = {'val_loss': loss}
results = {'log': logs}
return results
def test_epoch_end(self, outputs):
loss = some_loss()
...
logs = {'test_loss': loss}
results = {'log': logs}
return results
2. In addition, you can also use any arbitrary functionality from a particular logger from within your LightningModule.
For instance, here we log images using tensorboard.
.. testcode::
:skipif: not TORCHVISION_AVAILABLE
def training_step(self, batch, batch_idx):
self.generated_imgs = self.decoder.generate()
sample_imgs = self.generated_imgs[:6]
grid = torchvision.utils.make_grid(sample_imgs)
self.logger.experiment.add_image('generated_images', grid, 0)
...
return results
----------
Modify progress bar
^^^^^^^^^^^^^^^^^^^
Each return dict from the
:meth:`~pytorch_lightning.core.lightning.LightningModule.training_step`,
:meth:`~pytorch_lightning.core.lightning.LightningModule.training_epoch_end`,
:meth:`~pytorch_lightning.core.lightning.LightningModule.validation_epoch_end` and
:meth:`~pytorch_lightning.core.lightning.LightningModule.test_epoch_end`
can also contain a key called `progress_bar`.
Here we show the validation loss in the progress bar:
.. testcode::
def validation_epoch_end(self, outputs):
loss = some_loss()
...
logs = {'val_loss': loss}
results = {'progress_bar': logs}
return results
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
:meth:`~pytorch_lightning.core.lightning.LightningModule.get_progress_bar_dict` hook in your module.
----------
Configure console logging
^^^^^^^^^^^^^^^^^^^^^^^^^
Lightning logs useful information about the training process and user warnings to the console.
You can retrieve the Lightning logger and change it to your liking. For example, increase the logging level
to see fewer messages like so:
.. code-block:: python
import logging
logging.getLogger("lightning").setLevel(logging.ERROR)
Read more about custom Python logging `here <https://docs.python.org/3/library/logging.html>`_.
----------
Snapshot hyperparameters
^^^^^^^^^^^^^^^^^^^^^^^^
When training a model, it's useful to know what hyperparams went into that model.
When Lightning creates a checkpoint, it stores a key "hparams" with the hyperparams.
.. code-block:: python
lightning_checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
hyperparams = lightning_checkpoint['hparams']
Some loggers also allow logging the hyperparams used in the experiment. For instance,
when using the TestTubeLogger or the TensorBoardLogger, all hyperparams will show
in the `hparams tab <https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_hparams>`_.
----------
Snapshot code
^^^^^^^^^^^^^
Loggers also allow you to snapshot a copy of the code used in this experiment.
For example, TestTubeLogger does this with a flag:
.. testcode::
from pytorch_lightning.loggers import TestTubeLogger
logger = TestTubeLogger('.', create_git_tag=True)

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docs/source/index.rst
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@ -37,7 +37,7 @@ PyTorch Lightning Documentation
callbacks callbacks
datamodules datamodules
loggers logging
metrics metrics
.. toctree:: .. toctree::
@ -87,8 +87,7 @@ PyTorch Lightning Documentation
slurm slurm
child_modules child_modules
debugging debugging
experiment_logging loggers
experiment_reporting
early_stopping early_stopping
fast_training fast_training
hooks hooks

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@ -1,211 +1,253 @@
.. testsetup:: * .. testsetup:: *
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.trainer.trainer import Trainer from pytorch_lightning.trainer.trainer import Trainer
from pytorch_lightning import loggers as pl_loggers from pytorch_lightning.core.lightning import LightningModule
.. role:: hidden
:class: hidden-section
.. _loggers: .. _loggers:
*******
Loggers Loggers
=========== *******
Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...).
To use a logger, simply pass it into the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
Lightning uses TensorBoard by default.
.. testcode:: Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...). TensorBoard is used by default,
but you can pass to the :class:`~pytorch_lightning.trainer.trainer.Trainer` any combintation of the following loggers.
from pytorch_lightning import loggers as pl_loggers
tb_logger = pl_loggers.TensorBoardLogger('logs/')
trainer = Trainer(logger=tb_logger)
Choose from any of the others such as MLflow, Comet, Neptune, WandB, ...
.. testcode::
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 ...
.. testcode::
tb_logger = pl_loggers.TensorBoardLogger('logs/')
comet_logger = pl_loggers.CometLogger(save_dir='logs/')
trainer = Trainer(logger=[tb_logger, comet_logger])
.. note:: .. note::
All loggers log by default to `os.getcwd()`. To change the path without creating a logger set All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
`Trainer(default_root_dir='/your/path/to/save/checkpoints')` `Trainer(default_root_dir='/your/path/to/save/checkpoints')`
---------- Read more about :ref:`logging` options.
Logging from a LightningModule Comet.ml
------------------------------ ========
Interact with loggers in two ways, automatically and/or manually.
Automatic logging `Comet.ml <https://www.comet.ml/site/>`_ is a third-party logger.
^^^^^^^^^^^^^^^^^ To use :class:`~pytorch_lightning.loggers.CometLogger` as your logger do the following.
Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to log from anywhere in a LightningModule. First, install the package:
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_metric', x)
The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a few options:
- on_step (logs the metric at that step in training)
- on_epoch (automatically accumulates and logs at the end of the epoch)
- prog_bar (logs to the progress bar)
- logger (logs to the logger like Tensorboard)
Depending on where log is called from, Lightning auto-determines the correct mode for you. But of course
you can override the default behavior by manually setting the flags
.. note:: Setting on_epoch=True will accumulate your logged values over the full training epoch.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
Once your training starts, you can view the logs by using your favorite logger or booting up the Tensorboard logs:
.. code-block:: bash .. code-block:: bash
tensorboard --logdir ./lightning_logs pip install comet-ml
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
Manual logging
^^^^^^^^^^^^^^
For certain things like histograms, text, images, etc... you may need to use the logger object directly.
.. code-block:: python
def training_step(...):
...
# the logger you used (in this case tensorboard)
tensorboard = self.logger.experiment
tensorboard.add_histogram(...)
tensorboard.add_figure(...)
----------
Logging from a Callback
-----------------------
To log from a callback, the :func:`~~pytorch_lightning.core.lightning.LightningModule.log`
method of the LightningModule.
.. code-block:: python
class MyCallback(Callback):
def on_train_epoch_end(self, trainer, pl_module):
pl_module.log('something', x)
or access the logger object directly
.. code-block:: python
class MyCallback(Callback):
def on_train_epoch_end(self, trainer, pl_module):
tensorboard = pl_module.logger.experiment
tensorboard.add_histogram(...)
tensorboard.add_figure(...)
----------
Make a Custom Logger
--------------------
You can implement your own logger by writing a class that inherits from
:class:`LightningLoggerBase`. Use the :func:`~pytorch_lightning.loggers.base.rank_zero_only`
decorator to make sure that only the first process in DDP training logs data.
.. testcode:: .. testcode::
from pytorch_lightning.utilities import rank_zero_only import os
from pytorch_lightning.loggers import LightningLoggerBase from pytorch_lightning.loggers import CometLogger
comet_logger = CometLogger(
api_key=os.environ.get('COMET_API_KEY'),
workspace=os.environ.get('COMET_WORKSPACE'), # Optional
save_dir='.', # Optional
project_name='default_project', # Optional
rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
experiment_name='default' # Optional
)
trainer = Trainer(logger=comet_logger)
class MyLogger(LightningLoggerBase): The :class:`~pytorch_lightning.loggers.CometLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
@rank_zero_only .. testcode::
def log_hyperparams(self, params):
# params is an argparse.Namespace
# your code to record hyperparameters goes here
pass
@rank_zero_only class MyModule(LightningModule):
def log_metrics(self, metrics, step): def any_lightning_module_function_or_hook(self):
# metrics is a dictionary of metric names and values some_img = fake_image()
# your code to record metrics goes here self.logger.experiment.add_image('generated_images', some_img, 0)
pass
def save(self): .. seealso::
# Optional. Any code necessary to save logger data goes here :class:`~pytorch_lightning.loggers.CometLogger` docs.
# If you implement this, remember to call `super().save()`
# at the start of the method (important for aggregation of metrics)
super().save()
@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 MLflow
a pull request to add it to Lightning! ======
---------- `MLflow <https://mlflow.org/>`_ is a third-party logger.
To use :class:`~pytorch_lightning.loggers.MLFlowLogger` as your logger do the following.
First, install the package:
Supported Loggers .. code-block:: bash
-----------------
The following are loggers we support
Comet pip install mlflow
^^^^^
.. autoclass:: pytorch_lightning.loggers.comet.CometLogger Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
:noindex:
CSVLogger .. testcode::
^^^^^^^^^
.. autoclass:: pytorch_lightning.loggers.csv_logs.CSVLogger from pytorch_lightning.loggers import MLFlowLogger
:noindex: mlf_logger = MLFlowLogger(
experiment_name="default",
tracking_uri="file:./ml-runs"
)
trainer = Trainer(logger=mlf_logger)
MLFlow .. seealso::
^^^^^^ :class:`~pytorch_lightning.loggers.MLFlowLogger` docs.
.. autoclass:: pytorch_lightning.loggers.mlflow.MLFlowLogger ----------------
:noindex:
Neptune Neptune.ai
^^^^^^^ ==========
.. autoclass:: pytorch_lightning.loggers.neptune.NeptuneLogger `Neptune.ai <https://neptune.ai/>`_ is a third-party logger.
:noindex: To use :class:`~pytorch_lightning.loggers.NeptuneLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install neptune-client
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
from pytorch_lightning.loggers import NeptuneLogger
neptune_logger = NeptuneLogger(
api_key='ANONYMOUS', # replace with your own
project_name='shared/pytorch-lightning-integration',
experiment_name='default', # Optional,
params={'max_epochs': 10}, # Optional,
tags=['pytorch-lightning', 'mlp'], # Optional,
)
trainer = Trainer(logger=neptune_logger)
The :class:`~pytorch_lightning.loggers.NeptuneLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.NeptuneLogger` docs.
----------------
Tensorboard Tensorboard
^^^^^^^^^^^^ ===========
.. autoclass:: pytorch_lightning.loggers.tensorboard.TensorBoardLogger To use `TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your logger do the following.
:noindex:
Test-tube .. testcode::
^^^^^^^^^
.. autoclass:: pytorch_lightning.loggers.test_tube.TestTubeLogger from pytorch_lightning.loggers import TensorBoardLogger
:noindex: logger = TensorBoardLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)
The :class:`~pytorch_lightning.loggers.TensorBoardLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.TensorBoardLogger` docs.
----------------
Test Tube
=========
`Test Tube <https://github.com/williamFalcon/test-tube>`_ is a
`TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ logger but with nicer file structure.
To use :class:`~pytorch_lightning.loggers.TestTubeLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install test_tube
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. testcode::
from pytorch_lightning.loggers import TestTubeLogger
logger = TestTubeLogger('tb_logs', name='my_model')
trainer = Trainer(logger=logger)
The :class:`~pytorch_lightning.loggers.TestTubeLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.add_image('generated_images', some_img, 0)
.. seealso::
:class:`~pytorch_lightning.loggers.TestTubeLogger` docs.
----------------
Weights and Biases Weights and Biases
^^^^^^^^^^^^^^^^^^ ==================
.. autoclass:: pytorch_lightning.loggers.wandb.WandbLogger `Weights and Biases <https://www.wandb.com/>`_ is a third-party logger.
:noindex: To use :class:`~pytorch_lightning.loggers.WandbLogger` as your logger do the following.
First, install the package:
.. code-block:: bash
pip install wandb
Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
.. code-block:: python
from pytorch_lightning.loggers import WandbLogger
wandb_logger = WandbLogger(offline=True)
trainer = Trainer(logger=wandb_logger)
The :class:`~pytorch_lightning.loggers.WandbLogger` is available anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
self.logger.experiment.log({
"generated_images": [wandb.Image(some_img, caption="...")]
})
.. seealso::
:class:`~pytorch_lightning.loggers.WandbLogger` docs.
----------------
Multiple Loggers
================
Lightning supports the use of multiple loggers, just pass a list to the
:class:`~pytorch_lightning.trainer.trainer.Trainer`.
.. testcode::
from pytorch_lightning.loggers import TensorBoardLogger, TestTubeLogger
logger1 = TensorBoardLogger('tb_logs', name='my_model')
logger2 = TestTubeLogger('tb_logs', name='my_model')
trainer = Trainer(logger=[logger1, logger2])
The loggers are available as a list anywhere except ``__init__`` in your
:class:`~pytorch_lightning.core.lightning.LightningModule`.
.. testcode::
class MyModule(LightningModule):
def any_lightning_module_function_or_hook(self):
some_img = fake_image()
# Option 1
self.logger.experiment[0].add_image('generated_images', some_img, 0)
# Option 2
self.logger[0].experiment.add_image('generated_images', some_img, 0)

362
docs/source/logging.rst Normal file
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@ -0,0 +1,362 @@
.. testsetup:: *
from pytorch_lightning.core.lightning import LightningModule
from pytorch_lightning.trainer.trainer import Trainer
from pytorch_lightning import loggers as pl_loggers
.. role:: hidden
:class: hidden-section
.. _logging:
#######
Logging
#######
Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...).
To use a logger, simply pass it into the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
Lightning uses TensorBoard by default.
.. testcode::
from pytorch_lightning import loggers as pl_loggers
tb_logger = pl_loggers.TensorBoardLogger('logs/')
trainer = Trainer(logger=tb_logger)
Choose from any of the others such as MLflow, Comet, Neptune, WandB, ...
.. testcode::
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 ...
.. testcode::
tb_logger = pl_loggers.TensorBoardLogger('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 :ref:`logging_frequency`.
.. note::
All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
`Trainer(default_root_dir='/your/path/to/save/checkpoints')`
----------
******************************
Logging from a LightningModule
******************************
Lightning offers automatic log functionalities for logging scalars, or manual logging for anything else.
Automatic logging
=================
Use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method to log from anywhere in a :class:`~pytorch_lightning.core.LightningModule`.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_metric', x)
Depending on where log is called from, Lightning auto-determines the correct logging mode for you.\
But of course you can override the default behavior by manually setting the :func:`~~pytorch_lightning.core.lightning.LightningModule.log` parameters.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_loss', loss, on_step=True, on_epoch=True, prog_bar=True, logger=True)
The :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method has a few options:
* on_step: Logs the metric at the current step. Defaults to True in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_step`, and :func:`~pytorch_lightning.core.lightning.LightningModule.training_step_end`.
* on_epoch: Automatically accumulates and logs at the end of the epoch. Defaults to True anywhere in validation or test loops, and in :func:`~~pytorch_lightning.core.lightning.LightningModule.training_epoch_end`.
* prog_bar: Logs to the progress bar.
* logger: Logs to the logger like Tensorboard, or any other custom logger passed to the :class:`~pytorch_lightning.trainer.trainer.Trainer`.
.. note:: Setting on_epoch=True will accumulate your logged values over the full training epoch.
Manual logging
==============
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.
.. code-block:: python
def training_step(...):
...
# the logger you used (in this case tensorboard)
tensorboard = self.logger.experiment
tensorboard.add_image()
tensorboard.add_histogram(...)
tensorboard.add_figure(...)
Access your logs
================
Once your training starts, you can view the logs by using your favorite logger or booting up the Tensorboard logs:
.. code-block:: bash
tensorboard --logdir ./lightning_logs
----------
***********************
Logging from a Callback
***********************
To log from a callback, use the :func:`~~pytorch_lightning.core.lightning.LightningModule.log`
method of the :class:`~pytorch_lightning.core.LightningModule`.
.. code-block:: python
class MyCallback(Callback):
def on_train_epoch_end(self, trainer, pl_module):
pl_module.log('something', x)
or access the logger object directly for manual logging
.. code-block:: python
class MyCallback(Callback):
def on_train_epoch_end(self, trainer, pl_module):
tensorboard = pl_module.logger.experiment
tensorboard.add_histogram(...)
tensorboard.add_figure(...)
----------
********************
Make a custom logger
********************
You can implement your own logger by writing a class that inherits from
:class:`LightningLoggerBase`. Use the :func:`~pytorch_lightning.loggers.base.rank_zero_only`
decorator to make sure that only the first process in DDP training logs data.
.. testcode::
from pytorch_lightning.utilities import rank_zero_only
from pytorch_lightning.loggers import LightningLoggerBase
class MyLogger(LightningLoggerBase):
def name(self):
return 'MyLogger'
def experiment(self):
# Return the experiment object associated with this logger.
pass
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
def save(self):
# Optional. Any code necessary to save logger data goes here
# If you implement this, remember to call `super().save()`
# at the start of the method (important for aggregation of metrics)
super().save()
@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!
----------
.. _logging_frequency:
*************************
Control logging frequency
*************************
Logging frequency
=================
It may slow training down to log every single batch. By default, Lightning logs every 50 rows, or 50 training steps.
To change this behaviour, set the `log_every_n_steps` :class:`~pytorch_lightning.trainer.trainer.Trainer` flag.
.. testcode::
k = 10
trainer = Trainer(log_every_n_steps=k)
Log writing frequency
=====================
Writing to a logger can be expensive, so by default Lightning write logs to disc or to the given logger every 100 training steps.
To change this behaviour, set the interval at which you wish to flush logs to the filesystem using `log_every_n_steps` :class:`~pytorch_lightning.trainer.trainer.Trainer` flag.
.. testcode::
k = 100
trainer = Trainer(flush_logs_every_n_steps=k)
Unlike the `log_every_n_steps`, this argument does not apply to all loggers.
The example shown here works with :class:`~pytorch_lightning.loggers.tensorboard.TensorBoardLogger`,
which is the default logger in Lightning.
----------
************
Progress Bar
************
You can add any metric to the progress bar using :func:`~~pytorch_lightning.core.lightning.LightningModule.log`
method, setting `prog_bar=True`.
.. code-block:: python
def training_step(self, batch, batch_idx):
self.log('my_loss', loss, prog_bar=True)
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
:func:`~pytorch_lightning.core.lightning.LightningModule.get_progress_bar_dict` hook in your module.
.. code-block:: python
def get_progress_bar_dict(self):
# don't show the version number
items = super().get_progress_bar_dict()
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 logger and change it to your liking. For example, increase the logging level
to see fewer messages like so:
.. code-block:: python
import logging
logging.getLogger("lightning").setLevel(logging.ERROR)
Read more about custom Python logging `here <https://docs.python.org/3/library/logging.html>`_.
----------
***********************
Logging hyperparameters
***********************
When training a model, it's useful to know what hyperparams went into that model.
When Lightning creates a checkpoint, it stores a key "hparams" with the hyperparams.
.. code-block:: python
lightning_checkpoint = torch.load(filepath, map_location=lambda storage, loc: storage)
hyperparams = lightning_checkpoint['hparams']
Some loggers also allow logging the hyperparams used in the experiment. For instance,
when using the TestTubeLogger or the TensorBoardLogger, all hyperparams will show
in the `hparams tab <https://pytorch.org/docs/stable/tensorboard.html#torch.utils.tensorboard.writer.SummaryWriter.add_hparams>`_.
----------
*************
Snapshot code
*************
Loggers also allow you to snapshot a copy of the code used in this experiment.
For example, TestTubeLogger does this with a flag:
.. testcode::
from pytorch_lightning.loggers import TestTubeLogger
logger = TestTubeLogger('.', create_git_tag=True)
----------
*****************
Supported Loggers
*****************
The following are loggers we support
Comet
=====
.. autoclass:: pytorch_lightning.loggers.comet.CometLogger
:noindex:
CSVLogger
=========
.. autoclass:: pytorch_lightning.loggers.csv_logs.CSVLogger
:noindex:
MLFlow
======
.. autoclass:: pytorch_lightning.loggers.mlflow.MLFlowLogger
:noindex:
Neptune
=======
.. autoclass:: pytorch_lightning.loggers.neptune.NeptuneLogger
:noindex:
Tensorboard
============
.. autoclass:: pytorch_lightning.loggers.tensorboard.TensorBoardLogger
:noindex:
Test-tube
=========
.. autoclass:: pytorch_lightning.loggers.test_tube.TestTubeLogger
:noindex:
Weights and Biases
==================
.. autoclass:: pytorch_lightning.loggers.wandb.WandbLogger
:noindex:

4
pytorch_lightning/trainer/__init__.py
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@ -620,7 +620,7 @@ Writes logs to disk this often.
trainer = Trainer(flush_logs_every_n_steps=100) trainer = Trainer(flush_logs_every_n_steps=100)
See Also: See Also:
- :ref:`Experiment Reporting <experiment_reporting>` - :ref:`logging`
logger logger
^^^^^^ ^^^^^^
@ -955,7 +955,7 @@ How often to add logging rows (does not write to disk)
trainer = Trainer(log_every_n_steps=50) trainer = Trainer(log_every_n_steps=50)
See Also: See Also:
- :ref:`Experiment Reporting <experiment_reporting>` - :ref:`logging`
sync_batchnorm sync_batchnorm