Update docs (#1656)

* edit doc

mentioned in #646

* edit doc

* underline

* class reference

Co-authored-by: Adrian Wälchli <aedu.waelchli@gmail.com>

docs/source/fast_training.rst

@@ -42,45 +42,26 @@ Must use an int if using an IterableDataset.
     # check every 100 train batches (ie: for IterableDatasets or fixed frequency)
     trainer = Trainer(val_check_interval=100)
 
-Use training data subset
-------------------------
-If you don't want to check 100% of the training set (for debugging or if it's huge), set this flag.
+Use data subset for training, validation and test
+-------------------------------------------------
+If you don't want to check 100% of the training/validation/test set (for debugging or if it's huge), set these flags.
 
 .. code-block:: python
 
    # DEFAULT
-   trainer = Trainer(train_percent_check=1.0)
+   trainer = Trainer(
+       train_percent_check=1.0,
+       val_percent_check=1.0,
+       test_percent_check=1.0
+   )
 
-   # check 10% only
-   trainer = Trainer(train_percent_check=0.1)
+   # check 10%, 20%, 30% only, respectively for training, validation and test set
+   trainer = Trainer(
+       train_percent_check=0.1,
+       val_percent_check=0.2,
+       test_percent_check=0.3
+   )
 
-.. note:: ``train_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
+.. note:: ``train_percent_check``, ``val_percent_check`` and ``test_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0. ``val_percent_check`` will be ignored if ``fast_dev_run=True``.
 
-Use test data subset
---------------------
-If you don't want to check 100% of the test set (for debugging or if it's huge), set this flag.
-
-.. code-block:: python
-
-    # DEFAULT
-    trainer = Trainer(test_percent_check=1.0)
-
-    # check 10% only
-    trainer = Trainer(test_percent_check=0.1)
-
-.. note:: ``test_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0.
-
-Use validation data subset
---------------------------
-If you don't want to check 100% of the validation set (for debugging or if it's huge), set this flag.
-
-.. code-block:: python
-
-    # DEFAULT
-    trainer = Trainer(val_percent_check=1.0)
-
-    # check 10% only
-    trainer = Trainer(val_percent_check=0.1)
-
-.. note:: ``val_percent_check`` will be overwritten by ``overfit_pct`` if ``overfit_pct`` > 0 and ignored if
-    ``fast_dev_run=True``.
+.. note:: If you set ``val_percent_check=0``, validation will be disabled.

docs/source/slurm.rst

@@ -11,17 +11,14 @@ To train a model using multiple-nodes do the following:
 
 1. Design your LightningModule.
 
-2. Add `torch.DistributedSampler <https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler>`_
-   which enables access to a subset of your full dataset to each GPU.
-
-3. Enable ddp in the trainer
+2. Enable ddp in the trainer
 
 .. code-block:: python
 
    # train on 32 GPUs across 4 nodes
    trainer = Trainer(gpus=8, num_nodes=4, distributed_backend='ddp')
 
-4. It's a good idea to structure your train.py file like this:
+3. It's a good idea to structure your train.py file like this:
 
 .. code-block:: python
 
@@ -91,6 +88,8 @@ To train a model using multiple-nodes do the following:
 
     sbatch submit.sh
 
+.. note:: using :class:`~torch.utils.data.distributed.DistributedSampler` is already handled by Lightning.
+
 Walltime auto-resubmit
 -----------------------------------
 When you use Lightning in a SLURM cluster, lightning automatically detects when it is about