Some fixes to the trainer docstring (#9227)

Author: ethanwharris

pytorch_lightning/trainer/trainer.py

@@ -159,7 +159,7 @@ def __init__(
         stochastic_weight_avg: bool = False,
     ):
         r"""
-        Customize every aspect of training via flags
+        Customize every aspect of training via flags.
 
         Args:
 
@@ -168,7 +168,7 @@ def __init__(
 
             accumulate_grad_batches: Accumulates grads every k batches or as set up in the dict.
 
-            amp_backend: The mixed precision backend to use ("native" or "apex")
+            amp_backend: The mixed precision backend to use ("native" or "apex").
 
             amp_level: The optimization level to use (O1, O2, etc...).
 
@@ -207,34 +207,36 @@ def __init__(
             devices: Will be mapped to either `gpus`, `tpu_cores`, `num_processes` or `ipus`,
                 based on the accelerator type.
 
-            distributed_backend: deprecated. Please use 'accelerator'
+            distributed_backend: Deprecated. Please use 'accelerator'.
 
-            fast_dev_run: runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
+            fast_dev_run: Runs n if set to ``n`` (int) else 1 if set to ``True`` batch(es)
                 of train, val and test to find any bugs (ie: a sort of unit test).
 
             flush_logs_every_n_steps: How often to flush logs to disk (defaults to every 100 steps).
 
-            gpus: number of gpus to train on (int) or which GPUs to train on (list or str) applied per node
+            gpus: Number of GPUs to train on (int) or which GPUs to train on (list or str) applied per node
 
-            gradient_clip_val: 0 means don't clip.
+            gradient_clip_val: The value at which to clip gradients. Passing ``gradient_clip_val=0`` disables gradient
+                clipping.
 
-            gradient_clip_algorithm: 'value' means clip_by_value, 'norm' means clip_by_norm. Default: 'norm'
+            gradient_clip_algorithm: The gradient clipping algorithm to use. Pass ``gradient_clip_algorithm="value"``
+                for clip_by_value, and ``gradient_clip_algorithm="norm"`` for clip_by_norm.
 
-            limit_train_batches: How much of training dataset to check (float = fraction, int = num_batches)
+            limit_train_batches: How much of training dataset to check (float = fraction, int = num_batches).
 
-            limit_val_batches: How much of validation dataset to check (float = fraction, int = num_batches)
+            limit_val_batches: How much of validation dataset to check (float = fraction, int = num_batches).
 
-            limit_test_batches: How much of test dataset to check (float = fraction, int = num_batches)
+            limit_test_batches: How much of test dataset to check (float = fraction, int = num_batches).
 
-            limit_predict_batches: How much of prediction dataset to check (float = fraction, int = num_batches)
+            limit_predict_batches: How much of prediction dataset to check (float = fraction, int = num_batches).
 
             logger: Logger (or iterable collection of loggers) for experiment tracking. A ``True`` value uses
                 the default ``TensorBoardLogger``. ``False`` will disable logging. If multiple loggers are
                 provided and the `save_dir` property of that logger is not set, local files (checkpoints,
                 profiler traces, etc.) are saved in ``default_root_dir`` rather than in the ``log_dir`` of any
                 of the individual loggers.
 
-            log_gpu_memory: None, 'min_max', 'all'. Might slow performance
+            log_gpu_memory: None, 'min_max', 'all'. Might slow performance.
 
             log_every_n_steps: How often to log within steps (defaults to every 50 steps).
 
@@ -245,7 +247,7 @@ def __init__(
                     Deprecated in v1.5.0 and will be removed in v1.7.0
                     Please set ``prepare_data_per_node`` in LightningDataModule or LightningModule directly instead.
 
-            process_position: orders the progress bar when running multiple models on same machine.
+            process_position: Orders the progress bar when running multiple models on same machine.
 
                 .. deprecated:: v1.5
                     ``process_position`` has been deprecated in v1.5 and will be removed in v1.7.
@@ -280,15 +282,14 @@ def __init__(
                 :class:`datetime.timedelta`, or a dictionary with keys that will be passed to
                 :class:`datetime.timedelta`.
 
-            num_nodes: number of GPU nodes for distributed training.
+            num_nodes: Number of GPU nodes for distributed training.
 
-            num_processes: number of processes for distributed training with distributed_backend="ddp_cpu"
+            num_processes: Number of processes for distributed training with ``accelerator="ddp_cpu"``.
 
             num_sanity_val_steps: Sanity check runs n validation batches before starting the training routine.
                 Set it to `-1` to run all batches in all validation dataloaders.
 
             reload_dataloaders_every_n_epochs: Set to a non-negative integer to reload dataloaders every n epochs.
-                Default: 0
 
             reload_dataloaders_every_epoch: Set to True to reload dataloaders every epoch.
 
@@ -336,7 +337,7 @@ def __init__(
                 reload when reaching the minimum length of datasets.
 
             stochastic_weight_avg: Whether to use `Stochastic Weight Averaging (SWA)
-                <https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/>_`
+                <https://pytorch.org/blog/pytorch-1.6-now-includes-stochastic-weight-averaging/>`_.
 
         """
         super().__init__()