mirror of
https://github.com/wassname/pytorch-lightning.git
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CI: Force docs warnings to be raised as errors (+ fix all) (#1191)
* 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
co-authored by
Jirka Borovec
J. Borovec
Jirka Borovec
J. Borovec
parent
732eaee4d7
commit
792962ecc9
@@ -1,6 +1,8 @@
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.. role:: hidden
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:class: hidden-section
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.. _callbacks:
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Callbacks
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=========
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+28
-5
@@ -73,7 +73,7 @@ needs_sphinx = '1.4'
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# ones.
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extensions = [
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'sphinx.ext.autodoc',
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'sphinxcontrib.mockautodoc',
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# 'sphinxcontrib.mockautodoc', # raises error: directive 'automodule' is already registered ...
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# 'sphinxcontrib.fulltoc', # breaks pytorch-theme with unexpected kw argument 'titles_only'
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'sphinx.ext.doctest',
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'sphinx.ext.intersphinx',
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@@ -87,6 +87,7 @@ extensions = [
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# 'm2r',
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'nbsphinx',
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'sphinx_autodoc_typehints',
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'sphinx_paramlinks',
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]
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# Add any paths that contain templates here, relative to this directory.
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@@ -125,7 +126,20 @@ language = None
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# List of patterns, relative to source directory, that match files and
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# directories to ignore when looking for source files.
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# This pattern also affects html_static_path and html_extra_path.
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exclude_patterns = ['*.test_*']
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exclude_patterns = [
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'pytorch_lightning.rst',
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'pl_examples.*',
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'modules.rst',
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# deprecated/renamed:
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'pytorch_lightning.loggers.comet_logger.rst', # TODO: remove in v0.8.0
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'pytorch_lightning.loggers.mlflow_logger.rst', # TODO: remove in v0.8.0
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'pytorch_lightning.loggers.test_tube_logger.rst', # TODO: remove in v0.8.0
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'pytorch_lightning.callbacks.pt_callbacks.*', # TODO: remove in v0.8.0
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'pytorch_lightning.pt_overrides.*', # TODO: remove in v0.8.0
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'pytorch_lightning.root_module.*', # TODO: remove in v0.8.0
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'pytorch_lightning.logging.*', # TODO: remove in v0.8.0
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]
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# The name of the Pygments (syntax highlighting) style to use.
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pygments_style = None
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@@ -297,8 +311,17 @@ with open(os.path.join(PATH_ROOT, 'requirements.txt'), 'r') as fp:
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MOCK_REQUIRE_PACKAGES.append(pkg.rstrip())
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# TODO: better parse from package since the import name and package name may differ
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MOCK_MANUAL_PACKAGES = ['torch', 'torchvision', 'test_tube',
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'mlflow', 'comet_ml', 'wandb', 'neptune', 'trains']
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MOCK_MANUAL_PACKAGES = [
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'torch',
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'torchvision',
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'PIL',
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'test_tube',
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'mlflow',
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'comet_ml',
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'wandb',
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'neptune',
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'trains',
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]
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autodoc_mock_imports = MOCK_REQUIRE_PACKAGES + MOCK_MANUAL_PACKAGES
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# for mod_name in MOCK_REQUIRE_PACKAGES:
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# sys.modules[mod_name] = mock.Mock()
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@@ -369,7 +392,7 @@ autodoc_default_options = {
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# This value determines the text for the permalink; it defaults to "¶". Set it to None or the empty
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# string to disable permalinks.
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# https://www.sphinx-doc.org/en/master/usage/configuration.html#confval-html_add_permalinks
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html_add_permalinks = True
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html_add_permalinks = "¶"
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# True to prefix each section label with the name of the document it is in, followed by a colon.
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# For example, index:Introduction for a section called Introduction that appears in document index.rst.
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+16
-13
@@ -8,6 +8,9 @@ This flag runs a "unit test" by running 1 training batch and 1 validation batch.
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The point is to detect any bugs in the training/validation loop without having to wait for
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a full epoch to crash.
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.fast_dev_run`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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trainer = pl.Trainer(fast_dev_run=True)
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@@ -16,6 +19,9 @@ Inspect gradient norms
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----------------------
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Logs (to a logger), the norm of each weight matrix.
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.track_grad_norm`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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# the 2-norm
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@@ -25,7 +31,8 @@ Log GPU usage
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-------------
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Logs (to a logger) the GPU usage for each GPU on the master machine.
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(See: :ref:`trainer`)
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.log_gpu_memory`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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@@ -37,7 +44,8 @@ Make model overfit on subset of data
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A good debugging technique is to take a tiny portion of your data (say 2 samples per class),
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and try to get your model to overfit. If it can't, it's a sign it won't work with large datasets.
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(See: :ref:`trainer`)
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.overfit_pct`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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@@ -48,28 +56,23 @@ Print the parameter count by layer
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Whenever the .fit() function gets called, the Trainer will print the weights summary for the lightningModule.
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To disable this behavior, turn off this flag:
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(See: :ref:`trainer.weights_summary`)
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.weights_summary`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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trainer = pl.Trainer(weights_summary=None)
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Print which gradients are nan
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-----------------------------
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Prints the tensors with nan gradients.
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(See: :meth:`trainer.print_nan_grads`)
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.. code-block:: python
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trainer = pl.Trainer(print_nan_grads=False)
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Set the number of validation sanity steps
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-----------------------------------------
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Lightning runs a few steps of validation in the beginning of training.
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This avoids crashing in the validation loop sometime deep into a lengthy training loop.
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(See: :paramref:`~pytorch_lightning.trainer.trainer.Trainer.num_sanity_val_steps`
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argument of :class:`~pytorch_lightning.trainer.trainer.Trainer`)
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.. code-block:: python
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# DEFAULT
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trainer = Trainer(nb_sanity_val_steps=5)
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trainer = Trainer(num_sanity_val_steps=5)
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@@ -11,7 +11,8 @@ Enable Early Stopping
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---------------------
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There are two ways to enable early stopping.
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.. seealso:: :ref:`trainer`
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.. seealso::
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:class:`~pytorch_lightning.trainer.trainer.Trainer`
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.. code-block:: python
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@@ -35,4 +36,5 @@ To disable early stopping pass ``False`` to the `early_stop_callback`.
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Note that ``None`` will not disable early stopping but will lead to the
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default behaviour.
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.. seealso:: :ref:`trainer`
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.. seealso::
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:class:`~pytorch_lightning.trainer.trainer.Trainer`
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@@ -1,18 +0,0 @@
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.. toctree::
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:maxdepth: 1
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:name: Community Examples
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:caption: Community Examples
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Contextual Emotion Detection (DoubleDistilBert) <https://github.com/PyTorchLightning/emotion_transformer>
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Generative Adversarial Network <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=TyYOdg8g77P0>
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Hyperparameter optimization with Optuna <https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py>
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Image Inpainting using Partial Convolutions <https://github.com/ryanwongsa/Image-Inpainting>
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MNIST on TPU <https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3#scrollTo=BHBz1_AnamN_>
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NER (transformers, TPU) <https://colab.research.google.com/drive/1dBN-wwYUngLYVt985wGs_OKPlK_ANB9D>
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NeuralTexture (CVPR) <https://github.com/PyTorchLightning/neuraltexture>
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Recurrent Attentive Neural Process <https://github.com/PyTorchLightning/attentive-neural-processes>
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Siamese Nets for One-shot Image Recognition <https://github.com/PyTorchLightning/Siamese-Neural-Networks>
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Speech Transformers <https://github.com/PyTorchLightning/speech-transformer-pytorch_lightning>
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Transformers transfer learning (Huggingface) <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=yr7eaxkF-djf>
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Transformers text classification <https://github.com/ricardorei/lightning-text-classification>
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VAE Library of over 18+ VAE flavors <https://github.com/AntixK/PyTorch-VAE>
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@@ -7,7 +7,8 @@ Comet.ml
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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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.. seealso:: :ref:`comet` docs.
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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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@@ -38,7 +39,8 @@ Neptune.ai
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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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.. seealso:: :ref:`neptune` docs.
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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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@@ -68,7 +70,8 @@ allegro.ai TRAINS
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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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.. seealso:: :ref:`trains` docs.
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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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@@ -95,7 +98,8 @@ Tensorboard
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To use `Tensorboard <https://pytorch.org/docs/stable/tensorboard.html>`_ as your logger do the following.
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.. seealso:: TensorBoardLogger :ref:`tf-logger`
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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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@@ -121,7 +125,8 @@ Test Tube
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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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.. seealso:: TestTube :ref:`testTube`
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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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@@ -146,7 +151,8 @@ Wandb
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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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.. seealso:: :ref:`wandb` docs
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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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@@ -167,7 +173,7 @@ The Wandb logger is available anywhere except ``__init__`` in your LightningModu
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Multiple Loggers
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^^^^^^^^^^^^^^^^^
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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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@@ -22,7 +22,8 @@ Control log writing frequency
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Writing to a logger can be expensive. In Lightning you can set the interval at which you
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want to log using this trainer flag.
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.. seealso:: :ref:`trainer`
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.. seealso::
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:class:`~pytorch_lightning.trainer.trainer.Trainer`
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.. code-block:: python
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@@ -16,7 +16,8 @@ Force training for min or max epochs
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-------------------------------------
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It can be useful to force training for a minimum number of epochs or limit to a max number.
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.. seealso:: :ref:`trainer`
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.. seealso::
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:class:`~pytorch_lightning.trainer.trainer.Trainer`
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.. code-block:: python
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@@ -2,6 +2,7 @@ Hooks
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=====
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.. automodule:: pytorch_lightning.core.hooks
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:noindex:
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Hooks lifecycle
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---------------
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+28
-2
@@ -11,6 +11,7 @@ PyTorch Lightning Documentation
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:name: start
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:caption: Start Here
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new-project
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introduction_guide
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.. toctree::
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@@ -24,13 +25,24 @@ PyTorch Lightning Documentation
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loggers
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trainer
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.. toctree::
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:maxdepth: 1
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:name: Community Examples
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:caption: Community Examples
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examples
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Contextual Emotion Detection (DoubleDistilBert) <https://github.com/PyTorchLightning/emotion_transformer>
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Generative Adversarial Network <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=TyYOdg8g77P0>
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Hyperparameter optimization with Optuna <https://github.com/optuna/optuna/blob/master/examples/pytorch_lightning_simple.py>
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Image Inpainting using Partial Convolutions <https://github.com/ryanwongsa/Image-Inpainting>
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MNIST on TPU <https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3#scrollTo=BHBz1_AnamN_>
|
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NER (transformers, TPU) <https://colab.research.google.com/drive/1dBN-wwYUngLYVt985wGs_OKPlK_ANB9D>
|
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NeuralTexture (CVPR) <https://github.com/PyTorchLightning/neuraltexture>
|
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Recurrent Attentive Neural Process <https://github.com/PyTorchLightning/attentive-neural-processes>
|
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Siamese Nets for One-shot Image Recognition <https://github.com/PyTorchLightning/Siamese-Neural-Networks>
|
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Speech Transformers <https://github.com/PyTorchLightning/speech-transformer-pytorch_lightning>
|
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Transformers transfer learning (Huggingface) <https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=yr7eaxkF-djf>
|
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Transformers text classification <https://github.com/ricardorei/lightning-text-classification>
|
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VAE Library of over 18+ VAE flavors <https://github.com/AntixK/PyTorch-VAE>
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.. toctree::
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:maxdepth: 1
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@@ -83,3 +95,17 @@ Indices and tables
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* :ref:`genindex`
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* :ref:`modindex`
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* :ref:`search`
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.. This is here to make sphinx aware of the modules but not throw an error/warning
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.. toctree::
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:hidden:
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pytorch_lightning.core
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pytorch_lightning.callbacks
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pytorch_lightning.loggers
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pytorch_lightning.overrides
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pytorch_lightning.profiler
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pytorch_lightning.trainer
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pytorch_lightning.utilities
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@@ -472,7 +472,7 @@ First, change the runtime to TPU (and reinstall lightning).
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Next, install the required xla library (adds support for PyTorch on TPUs)
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.. code-block::
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.. code-block:: python
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import collections
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from datetime import datetime, timedelta
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@@ -504,6 +504,8 @@ Next, install the required xla library (adds support for PyTorch on TPUs)
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update = threading.Thread(target=update_server_xrt)
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update.start()
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.. code-block::
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# Install Colab TPU compat PyTorch/TPU wheels and dependencies
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!pip uninstall -y torch torchvision
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!gsutil cp "$DIST_BUCKET/$TORCH_WHEEL" .
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@@ -981,7 +983,8 @@ And pass the callbacks into the trainer
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Trainer(callbacks=[MyPrintingCallback()])
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.. note:: See full list of 12+ hooks in the `Callback docs <callbacks.rst#callback-class>`_
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.. note::
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See full list of 12+ hooks in the :ref:`callbacks`.
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---------
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@@ -1,5 +1,7 @@
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.. _multi-gpu-training:
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Multi-GPU training
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===================
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==================
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Lightning supports multiple ways of doing distributed training.
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Preparing your code
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@@ -235,7 +237,7 @@ Validation and test step also have the same option when using dp
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...
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Implement Your Own Distributed (DDP) training
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.core.LightningModule.`.
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|
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If you also need to use your own DDP implementation, override: :meth:`pytorch_lightning.core.LightningModule.configure_ddp`.
|
||||
|
||||
+50
-49
@@ -41,65 +41,66 @@ hosted on GCP.
|
||||
|
||||
To get a TPU on colab, follow these steps:
|
||||
|
||||
1. Go to https://colab.research.google.com/.
|
||||
1. Go to `https://colab.research.google.com/ <https://colab.research.google.com/>`_.
|
||||
|
||||
2. Click "new notebook" (bottom right of pop-up).
|
||||
2. Click "new notebook" (bottom right of pop-up).
|
||||
|
||||
3. Click runtime > change runtime settings. Select Python 3,
|
||||
and hardware accelerator "TPU". This will give you a TPU with 8 cores.
|
||||
3. Click runtime > change runtime settings. Select Python 3, and hardware accelerator "TPU".
|
||||
This will give you a TPU with 8 cores.
|
||||
|
||||
4. Next, insert this code into the first cell and execute. This
|
||||
will install the xla library that interfaces between PyTorch and
|
||||
the TPU.
|
||||
4. Next, insert this code into the first cell and execute.
|
||||
This will install the xla library that interfaces between PyTorch and the TPU.
|
||||
|
||||
.. code-block:: python
|
||||
.. code-block:: python
|
||||
|
||||
import collections
|
||||
from datetime import datetime, timedelta
|
||||
import os
|
||||
import requests
|
||||
import threading
|
||||
import collections
|
||||
from datetime import datetime, timedelta
|
||||
import os
|
||||
import requests
|
||||
import threading
|
||||
|
||||
_VersionConfig = collections.namedtuple('_VersionConfig', 'wheels,server')
|
||||
VERSION = "xrt==1.15.0" #@param ["xrt==1.15.0", "torch_xla==nightly"]
|
||||
CONFIG = {
|
||||
'xrt==1.15.0': _VersionConfig('1.15', '1.15.0'),
|
||||
'torch_xla==nightly': _VersionConfig('nightly', 'XRT-dev{}'.format(
|
||||
(datetime.today() - timedelta(1)).strftime('%Y%m%d'))),
|
||||
}[VERSION]
|
||||
DIST_BUCKET = 'gs://tpu-pytorch/wheels'
|
||||
TORCH_WHEEL = 'torch-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
TORCH_XLA_WHEEL = 'torch_xla-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
TORCHVISION_WHEEL = 'torchvision-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
_VersionConfig = collections.namedtuple('_VersionConfig', 'wheels,server')
|
||||
VERSION = "xrt==1.15.0" #@param ["xrt==1.15.0", "torch_xla==nightly"]
|
||||
CONFIG = {
|
||||
'xrt==1.15.0': _VersionConfig('1.15', '1.15.0'),
|
||||
'torch_xla==nightly': _VersionConfig('nightly', 'XRT-dev{}'.format(
|
||||
(datetime.today() - timedelta(1)).strftime('%Y%m%d'))),
|
||||
}[VERSION]
|
||||
DIST_BUCKET = 'gs://tpu-pytorch/wheels'
|
||||
TORCH_WHEEL = 'torch-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
TORCH_XLA_WHEEL = 'torch_xla-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
TORCHVISION_WHEEL = 'torchvision-{}-cp36-cp36m-linux_x86_64.whl'.format(CONFIG.wheels)
|
||||
|
||||
# Update TPU XRT version
|
||||
def update_server_xrt():
|
||||
print('Updating server-side XRT to {} ...'.format(CONFIG.server))
|
||||
url = 'http://{TPU_ADDRESS}:8475/requestversion/{XRT_VERSION}'.format(
|
||||
TPU_ADDRESS=os.environ['COLAB_TPU_ADDR'].split(':')[0],
|
||||
XRT_VERSION=CONFIG.server,
|
||||
)
|
||||
print('Done updating server-side XRT: {}'.format(requests.post(url)))
|
||||
# Update TPU XRT version
|
||||
def update_server_xrt():
|
||||
print('Updating server-side XRT to {} ...'.format(CONFIG.server))
|
||||
url = 'http://{TPU_ADDRESS}:8475/requestversion/{XRT_VERSION}'.format(
|
||||
TPU_ADDRESS=os.environ['COLAB_TPU_ADDR'].split(':')[0],
|
||||
XRT_VERSION=CONFIG.server,
|
||||
)
|
||||
print('Done updating server-side XRT: {}'.format(requests.post(url)))
|
||||
|
||||
update = threading.Thread(target=update_server_xrt)
|
||||
update.start()
|
||||
update = threading.Thread(target=update_server_xrt)
|
||||
update.start()
|
||||
|
||||
# Install Colab TPU compat PyTorch/TPU wheels and dependencies
|
||||
!pip uninstall -y torch torchvision
|
||||
!gsutil cp "$DIST_BUCKET/$TORCH_WHEEL" .
|
||||
!gsutil cp "$DIST_BUCKET/$TORCH_XLA_WHEEL" .
|
||||
!gsutil cp "$DIST_BUCKET/$TORCHVISION_WHEEL" .
|
||||
!pip install "$TORCH_WHEEL"
|
||||
!pip install "$TORCH_XLA_WHEEL"
|
||||
!pip install "$TORCHVISION_WHEEL"
|
||||
!sudo apt-get install libomp5
|
||||
update.join()
|
||||
.. code-block::
|
||||
|
||||
5. Once the above is done, install PyTorch Lightning (v 0.7.0+).
|
||||
# Install Colab TPU compat PyTorch/TPU wheels and dependencies
|
||||
!pip uninstall -y torch torchvision
|
||||
!gsutil cp "$DIST_BUCKET/$TORCH_WHEEL" .
|
||||
!gsutil cp "$DIST_BUCKET/$TORCH_XLA_WHEEL" .
|
||||
!gsutil cp "$DIST_BUCKET/$TORCHVISION_WHEEL" .
|
||||
!pip install "$TORCH_WHEEL"
|
||||
!pip install "$TORCH_XLA_WHEEL"
|
||||
!pip install "$TORCHVISION_WHEEL"
|
||||
!sudo apt-get install libomp5
|
||||
update.join()
|
||||
|
||||
.. code-block::
|
||||
5. Once the above is done, install PyTorch Lightning (v 0.7.0+).
|
||||
|
||||
! pip install pytorch-lightning
|
||||
.. code-block::
|
||||
|
||||
!pip install pytorch-lightning
|
||||
|
||||
6. Then set up your LightningModule as normal.
|
||||
|
||||
@@ -147,8 +148,8 @@ for TPU use
|
||||
|
||||
return loader
|
||||
|
||||
8. Configure the number of TPU cores in the trainer. You can only choose
|
||||
1 or 8. To use a full TPU pod skip to the TPU pod section.
|
||||
Configure the number of TPU cores in the trainer. You can only choose 1 or 8.
|
||||
To use a full TPU pod skip to the TPU pod section.
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
|
||||
@@ -7,7 +7,7 @@ Accumulate gradients
|
||||
Accumulated gradients runs K small batches of size N before doing a backwards pass.
|
||||
The effect is a large effective batch size of size KxN.
|
||||
|
||||
.. seealso:: :ref:`trainer`
|
||||
.. seealso:: :class:`~pytorch_lightning.trainer.trainer.Trainer`
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
@@ -20,7 +20,7 @@ Gradient Clipping
|
||||
Gradient clipping may be enabled to avoid exploding gradients. Specifically, this will `clip the gradient
|
||||
norm <https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_>`_ computed over all model parameters together.
|
||||
|
||||
.. seealso:: :ref:`trainer`
|
||||
.. seealso:: :class:`~pytorch_lightning.trainer.trainer.Trainer`
|
||||
|
||||
.. code-block:: python
|
||||
|
||||
|
||||
@@ -1,4 +0,0 @@
|
||||
From PyTorch to Lightning
|
||||
=========================
|
||||
|
||||
Talk about how to convert
|
||||
Reference in New Issue
Block a user