Fix typo in Quick Start/Step-by-step walk-through (#3007)
* Fix typo in Quick Start/Step-by-step walk-through * Fix typo in Quick Start/Step-by-step walk-through * Fix snippets in lightning module * Remove testblock doctest does not have torch with CUDA, so x.cuda() will fail * Remove test code "..." is not python, so doctests fail * Fix #3005 * Fix indentation, stage in docs Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com> Co-authored-by: Teddy Koker <teddy.koker@gmail.com> Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com>
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@ -1,6 +1,7 @@
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.. testsetup:: *
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.. testsetup:: *
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.datamodule import LightningDataModule
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from pytorch_lightning.trainer.trainer import Trainer
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from pytorch_lightning.trainer.trainer import Trainer
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.. _introduction-guide:
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.. _introduction-guide:
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@ -259,9 +260,9 @@ In this case, it's better to group the full definition of a dataset into a `Data
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- Val dataloader(s)
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- Val dataloader(s)
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- Test dataloader(s)
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- Test dataloader(s)
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.. code-block:: python
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.. testcode:: python
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class MyDataModule(pl.DataModule):
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class MyDataModule(LightningDataModule):
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def __init__(self):
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def __init__(self):
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super().__init__()
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super().__init__()
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@ -51,7 +51,7 @@ Notice a few things.
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# or to init a new tensor
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# or to init a new tensor
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new_x = torch.Tensor(2, 3)
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new_x = torch.Tensor(2, 3)
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new_x = new_x.type_as(x.type())
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new_x = new_x.type_as(x)
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5. There are no samplers for distributed, Lightning also does this for you.
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5. There are no samplers for distributed, Lightning also does this for you.
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@ -1,6 +1,7 @@
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.. testsetup:: *
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.. testsetup:: *
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.core.datamodule import LightningDataModule
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from pytorch_lightning.trainer.trainer import Trainer
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from pytorch_lightning.trainer.trainer import Trainer
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import os
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import os
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import torch
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import torch
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@ -357,9 +358,9 @@ And the matching code:
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.. code-block::
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.. testcode:: python
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class MNISTDataModule(pl.LightningDataModule):
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class MNISTDataModule(LightningDataModule):
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def __init__(self, batch_size=32):
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def __init__(self, batch_size=32):
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super().__init__()
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super().__init__()
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@ -407,7 +408,7 @@ over download/prepare/splitting data
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.. code-block:: python
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.. code-block:: python
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class MyDataModule(pl.DataModule):
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class MyDataModule(LightningDataModule):
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def prepare_data(self):
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def prepare_data(self):
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# called only on 1 GPU
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# called only on 1 GPU
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@ -415,7 +416,7 @@ over download/prepare/splitting data
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tokenize()
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tokenize()
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etc()
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etc()
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def setup(self):
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def setup(self, stage=None):
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# called on every GPU (assigning state is OK)
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# called on every GPU (assigning state is OK)
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self.train = ...
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self.train = ...
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self.val = ...
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self.val = ...
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@ -432,7 +433,7 @@ First, define the information that you might need.
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.. code-block:: python
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.. code-block:: python
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class MyDataModule(pl.DataModule):
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class MyDataModule(LightningDataModule):
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def __init__(self):
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def __init__(self):
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super().__init__()
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super().__init__()
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@ -444,7 +445,7 @@ First, define the information that you might need.
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tokenize()
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tokenize()
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build_vocab()
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build_vocab()
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def setup(self):
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def setup(self, stage=None):
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vocab = load_vocab
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vocab = load_vocab
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self.vocab_size = len(vocab)
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self.vocab_size = len(vocab)
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