mirror of
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Improved docs for Loggers (#1484)
* improve __init__ * improve logger base * improve comet logger docs * improved docs for mlflow * improved nepune logger docs * fix matplotlib import issue * improve tensorboard docs * improve docs for test tube * improved trains logger docs * improve wandb logger docs * improved docs in experiment_logging.rst * added MLflow to the list of loggers * fix too long lines * fix trains doctest * fix neptune doctest * fix mlflow doctest * Apply suggestions from code review Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Apply suggestions from code review * fix whitespace * try bypass mode for neptune (fix doctest api key error) * try "test" as api key * Revert "try "test" as api key" This reverts commit fd77db26d551f08b4b4a12bb93cbd8f7a0814f29. * try test as api key * update neptune docs * bump neptune minimal version * revert unnecessary bypass code * test if CI runs doctests in .rst files * Revert "test if CI runs doctests in .rst files" This reverts commit a45aeb460a8c4b7445a35dd7b49265f48d11c485. * add doctest directive * neptune demo links * added tutorial link for W&B * fix line too long * fix merge error * fix merge error * add instructions how to install loggers * add instructions how to install the loggers * hide _abc_impl property from docs * review Borda, 4 spaces * indentation in example sections * blank Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
This commit is contained in:
Adrian Wälchli
co-authored by
Jirka Borovec
Jirka Borovec
parent
3c549e8ae3
commit
6e1d72d98a
+1
-1
@@ -385,7 +385,7 @@ autodoc_default_options = {
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'methods': None,
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# 'attributes': None,
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'special-members': '__call__',
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# 'exclude-members': '__weakref__',
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'exclude-members': '_abc_impl',
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'show-inheritance': True,
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'private-members': True,
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'noindex': True,
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+204
-134
@@ -1,201 +1,271 @@
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Experiment Logging
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===================
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==================
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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 CometLogger as your logger do the following.
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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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.. doctest::
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>>> import os
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>>> from pytorch_lightning import Trainer
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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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.. doctest::
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>>> from pytorch_lightning import LightningModule
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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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.. code-block:: python
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MLflow
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^^^^^^
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from pytorch_lightning.loggers import CometLogger
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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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comet_logger = CometLogger(
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api_key=os.environ["COMET_KEY"],
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workspace=os.environ["COMET_WORKSPACE"], # Optional
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project_name="default_project", # Optional
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rest_api_key=os.environ["COMET_REST_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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.. code-block:: bash
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The CometLogger is available anywhere except ``__init__`` in your LightningModule
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pip install mlflow
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.. code-block:: python
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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class MyModule(pl.LightningModule):
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.. doctest::
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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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>>> from pytorch_lightning import Trainer
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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:/."
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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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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 Neptune.ai as your logger do the following.
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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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.. doctest::
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>>> from pytorch_lightning import Trainer
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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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.. doctest::
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>>> from pytorch_lightning import LightningModule
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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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.. code-block:: python
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from pytorch_lightning.loggers import NeptuneLogger
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neptune_logger = NeptuneLogger(
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project_name="USER_NAME/PROJECT_NAME",
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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 Neptune.ai is available anywhere except ``__init__`` in your LightningModule
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.. code-block:: python
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class MyModule(pl.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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allegro.ai TRAINS
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^^^^^^^^^^^^^^^^^
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`allegro.ai <https://github.com/allegroai/trains/>`_ is a third-party logger.
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To use TRAINS as your logger do the following.
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To use :class:`~pytorch_lightning.loggers.TrainsLogger` 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 trains
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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.. doctest::
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>>> from pytorch_lightning import Trainer
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>>> from pytorch_lightning.loggers import TrainsLogger
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>>> trains_logger = TrainsLogger(
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... project_name='examples',
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... task_name='pytorch lightning test',
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... ) # doctest: +ELLIPSIS
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TRAINS Task: ...
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TRAINS results page: ...
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>>> trainer = Trainer(logger=trains_logger)
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The :class:`~pytorch_lightning.loggers.TrainsLogger` is available anywhere in your
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:class:`~pytorch_lightning.core.lightning.LightningModule`.
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.. doctest::
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>>> from pytorch_lightning import LightningModule
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>>> class MyModule(LightningModule):
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... def __init__(self):
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... some_img = fake_image()
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... self.logger.experiment.log_image('debug', 'generated_image_0', some_img, 0)
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.. seealso::
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:class:`~pytorch_lightning.loggers.TrainsLogger` docs.
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.. code-block:: python
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from pytorch_lightning.loggers import TrainsLogger
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trains_logger = TrainsLogger(
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project_name="examples",
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task_name="pytorch lightning test"
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)
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trainer = Trainer(logger=trains_logger)
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The TrainsLogger is available anywhere in your LightningModule
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.. code-block:: python
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class MyModule(pl.LightningModule):
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def __init__(self, ...):
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some_img = fake_image()
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self.logger.log_image('debug', 'generated_image_0', some_img, 0)
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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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To use `TensorBoard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your logger do the following.
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.. doctest::
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>>> from pytorch_lightning import Trainer
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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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.. doctest::
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>>> from pytorch_lightning import LightningModule
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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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.. code-block:: python
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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 TensorBoardLogger is available anywhere except ``__init__`` in your LightningModule
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.. code-block:: python
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class MyModule(pl.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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Test Tube
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^^^^^^^^^
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`Test Tube <https://github.com/williamFalcon/test-tube>`_ is a tensorboard logger but with nicer file structure.
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To use TestTube as your logger do the following.
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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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.. doctest::
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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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.. doctest::
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>>> from pytorch_lightning import LightningModule
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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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.. code-block:: python
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Weights and Biases
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^^^^^^^^^^^^^^^^^^
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from pytorch_lightning.loggers import TestTubeLogger
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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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logger = TestTubeLogger("tb_logs", name="my_model")
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trainer = Trainer(logger=logger)
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.. code-block:: bash
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The TestTubeLogger is available anywhere except ``__init__`` in your LightningModule
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pip install wandb
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.. code-block:: python
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Then configure the logger and pass it to the :class:`~pytorch_lightning.trainer.trainer.Trainer`:
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class MyModule(pl.LightningModule):
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.. doctest::
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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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>>> from pytorch_lightning.loggers import WandbLogger
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>>> wandb_logger = WandbLogger()
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>>> trainer = Trainer(logger=wandb_logger)
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Wandb
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^^^^^
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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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`Wandb <https://www.wandb.com/>`_ is a third-party logger.
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To use Wandb as your logger do the following.
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.. doctest::
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>>> from pytorch_lightning import LightningModule
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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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.. code-block:: python
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from pytorch_lightning.loggers import WandbLogger
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wandb_logger = WandbLogger()
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trainer = Trainer(logger=wandb_logger)
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The Wandb logger is available anywhere except ``__init__`` in your LightningModule
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.. code-block:: python
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class MyModule(pl.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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Multiple Loggers
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^^^^^^^^^^^^^^^^
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PyTorch-Lightning supports use of multiple loggers, just pass a list to the `Trainer`.
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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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.. doctest::
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from pytorch_lightning.loggers import TensorBoardLogger, TestTubeLogger
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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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logger1 = TensorBoardLogger("tb_logs", name="my_model")
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logger2 = TestTubeLogger("tt_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 LightningModule
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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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.. code-block:: python
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.. doctest::
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class MyModule(pl.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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>>> from pytorch_lightning import LightningModule
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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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