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Update DDP docs (#5046) 

* Fix flake8 error to fix CI

* Correct weights-loading to use correct callbacks

* Fix dangling links

Co-authored-by: chaton <thomas@grid.ai>
Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
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Alan Du 2020-12-10 12:26:02 -05:00 committed by GitHub
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2 changed files with 3 additions and 3 deletions
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4
docs/source/multi_gpu.rst
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@ -593,9 +593,9 @@ Below are the possible configurations we support.
Implement Your Own Distributed (DDP) training
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.core.LightningModule.`.
If you need your own way to init PyTorch DDP you can override :meth:`pytorch_lightning.plugins.ddp_plugin.DDPPlugin.init_ddp_connection`.
If you also need to use your own DDP implementation, override: :meth:`pytorch_lightning.core.LightningModule.configure_ddp`.
If you also need to use your own DDP implementation, override: :meth:`pytorch_lightning.plugins.ddp_plugin.DDPPlugin.configure_ddp`.
----------

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docs/source/weights_loading.rst
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@ -46,7 +46,7 @@ You can customize the checkpointing behavior to monitor any quantity of your tra
1. Calculate any metric or other quantity you wish to monitor, such as validation loss.
2. Log the quantity using :func:`~~pytorch_lightning.core.lightning.LightningModule.log` method, with a key such as `val_loss`.
3. Initializing the :class:`~pytorch_lightning.callbacks.ModelCheckpoint` callback, and set `monitor` to be the key of your quantity.
4. Pass the callback to `checkpoint_callback` :class:`~pytorch_lightning.trainer.Trainer` flag.
4. Pass the callback to the `callbacks` :class:`~pytorch_lightning.trainer.Trainer` flag.
.. code-block:: python