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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>
78 lines
2.5 KiB
ReStructuredText
78 lines
2.5 KiB
ReStructuredText
Debugging
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=========
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The following are flags that make debugging much easier.
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Fast dev run
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------------
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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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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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trainer = pl.Trainer(track_grad_norm=2)
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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: :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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trainer = pl.Trainer(log_gpu_memory=True)
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Make model overfit on subset of data
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------------------------------------
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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: :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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trainer = pl.Trainer(overfit_pct=0.01)
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Print the parameter count by layer
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----------------------------------
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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: :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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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(num_sanity_val_steps=5) |