263 lines
7.8 KiB
ReStructuredText
263 lines
7.8 KiB
ReStructuredText
.. testsetup:: *
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from pytorch_lightning.trainer.trainer import Trainer
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from pytorch_lightning.core.lightning import LightningModule
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.. _loggers:
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*******
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Loggers
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*******
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Lightning supports the most popular logging frameworks (TensorBoard, Comet, etc...). TensorBoard is used by default,
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but you can pass to the :class:`~pytorch_lightning.trainer.trainer.Trainer` any combination of the following loggers.
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.. note::
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All loggers log by default to `os.getcwd()`. To change the path without creating a logger set
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`Trainer(default_root_dir='/your/path/to/save/checkpoints')`
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Read more about :ref:`logging` options.
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To log arbitrary artifacts like images or audio samples use the `trainer.log_dir` property to resolve
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the path.
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.. code-block:: python
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def training_step(self, batch, batch_idx):
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img = ...
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log_image(img, self.trainer.log_dir)
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Comet.ml
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========
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`Comet.ml <https://www.comet.ml/site/>`_ is a third-party logger.
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To use :class:`~pytorch_lightning.loggers.CometLogger` as your logger do the following.
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First, install the package:
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.. code-block:: bash
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pip install comet-ml
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. testcode::
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import os
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from pytorch_lightning.loggers import CometLogger
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comet_logger = CometLogger(
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api_key=os.environ.get('COMET_API_KEY'),
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workspace=os.environ.get('COMET_WORKSPACE'), # Optional
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save_dir='.', # Optional
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project_name='default_project', # Optional
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rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
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experiment_name='default' # Optional
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)
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trainer = Trainer(logger=comet_logger)
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The :class:`~pytorch_lightning.loggers.CometLogger` is available anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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self.logger.experiment.add_image('generated_images', some_img, 0)
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.. seealso::
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:class:`~pytorch_lightning.loggers.CometLogger` docs.
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----------------
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MLflow
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======
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`MLflow <https://mlflow.org/>`_ is a third-party logger.
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To use :class:`~pytorch_lightning.loggers.MLFlowLogger` as your logger do the following.
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First, install the package:
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.. code-block:: bash
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pip install mlflow
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. code-block:: python
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from pytorch_lightning.loggers import MLFlowLogger
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mlf_logger = MLFlowLogger(
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experiment_name="default",
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tracking_uri="file:./ml-runs"
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)
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trainer = Trainer(logger=mlf_logger)
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.. seealso::
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:class:`~pytorch_lightning.loggers.MLFlowLogger` docs.
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----------------
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Neptune.ai
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==========
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`Neptune.ai <https://neptune.ai/>`_ is a third-party logger.
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To use :class:`~pytorch_lightning.loggers.NeptuneLogger` as your logger do the following.
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First, install the package:
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.. code-block:: bash
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pip install neptune-client
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. testcode::
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from pytorch_lightning.loggers import NeptuneLogger
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neptune_logger = NeptuneLogger(
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api_key='ANONYMOUS', # replace with your own
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project_name='shared/pytorch-lightning-integration',
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experiment_name='default', # Optional,
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params={'max_epochs': 10}, # Optional,
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tags=['pytorch-lightning', 'mlp'], # Optional,
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)
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trainer = Trainer(logger=neptune_logger)
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The :class:`~pytorch_lightning.loggers.NeptuneLogger` is available anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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self.logger.experiment.add_image('generated_images', some_img, 0)
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.. seealso::
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:class:`~pytorch_lightning.loggers.NeptuneLogger` docs.
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----------------
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Tensorboard
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===========
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To use `TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your logger do the following.
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.. testcode::
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from pytorch_lightning.loggers import TensorBoardLogger
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logger = TensorBoardLogger('tb_logs', name='my_model')
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trainer = Trainer(logger=logger)
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The :class:`~pytorch_lightning.loggers.TensorBoardLogger` is available anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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self.logger.experiment.add_image('generated_images', some_img, 0)
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.. seealso::
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:class:`~pytorch_lightning.loggers.TensorBoardLogger` docs.
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----------------
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Test Tube
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=========
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`Test Tube <https://github.com/williamFalcon/test-tube>`_ is a
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`TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ logger but with nicer file structure.
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To use :class:`~pytorch_lightning.loggers.TestTubeLogger` as your logger do the following.
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First, install the package:
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.. code-block:: bash
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pip install test_tube
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. code-block:: python
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from pytorch_lightning.loggers import TestTubeLogger
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logger = TestTubeLogger('tb_logs', name='my_model')
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trainer = Trainer(logger=logger)
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The :class:`~pytorch_lightning.loggers.TestTubeLogger` is available anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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self.logger.experiment.add_image('generated_images', some_img, 0)
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.. seealso::
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:class:`~pytorch_lightning.loggers.TestTubeLogger` docs.
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----------------
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Weights and Biases
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==================
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`Weights and Biases <https://www.wandb.com/>`_ is a third-party logger.
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To use :class:`~pytorch_lightning.loggers.WandbLogger` as your logger do the following.
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First, install the package:
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.. code-block:: bash
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pip install wandb
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. code-block:: python
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from pytorch_lightning.loggers import WandbLogger
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wandb_logger = WandbLogger(offline=True)
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trainer = Trainer(logger=wandb_logger)
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The :class:`~pytorch_lightning.loggers.WandbLogger` is available anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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self.logger.experiment.log({
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"generated_images": [wandb.Image(some_img, caption="...")]
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})
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.. seealso::
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:class:`~pytorch_lightning.loggers.WandbLogger` docs.
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----------------
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Multiple Loggers
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================
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Lightning supports the use of multiple loggers, just pass a list to the
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:class:`~pytorch_lightning.trainer.trainer.Trainer`.
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.. code-block:: python
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from pytorch_lightning.loggers import TensorBoardLogger, TestTubeLogger
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logger1 = TensorBoardLogger('tb_logs', name='my_model')
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logger2 = TestTubeLogger('tb_logs', name='my_model')
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trainer = Trainer(logger=[logger1, logger2])
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The loggers are available as a list anywhere except ``__init__`` in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. testcode::
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class MyModule(LightningModule):
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def any_lightning_module_function_or_hook(self):
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some_img = fake_image()
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# Option 1
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self.logger.experiment[0].add_image('generated_images', some_img, 0)
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# Option 2
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self.logger[0].experiment.add_image('generated_images', some_img, 0)
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