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CI: Force docs warnings to be raised as errors (+ fix all) (#1191)

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* add argument to force warn

* fix automodule error

* fix permalink error

* fix indentation warning

* fix warning

* fix import warnings

* fix duplicate label warning

* fix bullet point indentation warning

* fix duplicate label warning

* fix "import not top level" warning

* line too long

* fix indentation

* fix bullet points indentation warning

* fix hooks warnings

* fix reference problem with excluded test_tube

* fix indentation in print

* change imports for trains logger

* remove pandas type annotation

* Update pytorch_lightning/core/lightning.py

* include bullet points inside note

* remove old quick start guide (unused)

* fix unused warning

* fix formatting

* fix duplicate label issue

* fix duplicate label warning (replaced by class ref)

* fix tick

* fix indentation warnings

* docstring ticks

* remove obsolete docstring typing

* Revert "remove old quick start guide (unused)"

This reverts commit d51bb40695442c8fa11bc9df74f6db56264f7509.

* added old quick start guide to navigation

* remove unused  tutorials file

* ignore some modules that got deprecated and are not used anymore

* fix duplicate label warning

* move examples doc and exclude pl_examples from autodoc

* fix formatting for configure_optimizer

* fix no blank line warnings

* fix "see also" labels and add paramref extension

* fix more reference problems

* fix multi-gpu reference

* fix weird warning

* fix indentation and unrecognized characters in code block

* fix warning "... not included in toctree"

* fix PIL import error

* fix duplicate target "here" warning

* fix broken link

* revert accidentally moved pl_examples

* changelog

* stdout

* note some things to know

Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com>
Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
This commit is contained in:
Adrian Wälchli
2020-03-20 20:49:01 +01:00
committed by GitHub
co-authored by Jirka Borovec J. Borovec
parent 732eaee4d7
commit 792962ecc9
30 changed files with 281 additions and 199 deletions
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docs/source/debugging.rst
+16 -13
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@@ -8,6 +8,9 @@ This flag runs a "unit test" by running 1 training batch and 1 validation batch.
The point is to detect any bugs in the training/validation loop without having to wait for
a full epoch to crash.
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.fast_dev_run`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
trainer = pl.Trainer(fast_dev_run=True)
@@ -16,6 +19,9 @@ Inspect gradient norms
----------------------
Logs (to a logger), the norm of each weight matrix.
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.track_grad_norm`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
# the 2-norm
@@ -25,7 +31,8 @@ Log GPU usage
-------------
Logs (to a logger) the GPU usage for each GPU on the master machine.
(See: :ref:`trainer`)
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.log_gpu_memory`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
@@ -37,7 +44,8 @@ Make model overfit on subset of data
A good debugging technique is to take a tiny portion of your data (say 2 samples per class),
and try to get your model to overfit. If it can't, it's a sign it won't work with large datasets.
(See: :ref:`trainer`)
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.overfit_pct`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
@@ -48,28 +56,23 @@ Print the parameter count by layer
Whenever the .fit() function gets called, the Trainer will print the weights summary for the lightningModule.
To disable this behavior, turn off this flag:
(See: :ref:`trainer.weights_summary`)
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_summary`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
trainer = pl.Trainer(weights_summary=None)
Print which gradients are nan
-----------------------------
Prints the tensors with nan gradients.
(See: :meth:`trainer.print_nan_grads`)
.. code-block:: python
trainer = pl.Trainer(print_nan_grads=False)
Set the number of validation sanity steps
-----------------------------------------
Lightning runs a few steps of validation in the beginning of training.
This avoids crashing in the validation loop sometime deep into a lengthy training loop.
(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.num_sanity_val_steps`
argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
.. code-block:: python
# DEFAULT
trainer = Trainer(nb_sanity_val_steps=5)
trainer = Trainer(num_sanity_val_steps=5)