[train/docs] Mini reference fixes (ray-project#39463)

Fixing some references to API docs or other documents in the PyTorch getting started guides.

Signed-off-by: Kai Fricke <kai@anyscale.com>

doc/source/train/getting-started-pytorch-lightning.rst

@@ -29,9 +29,9 @@ For reference, the final code is as follows:
     trainer = TorchTrainer(train_func, scaling_config=scaling_config)
     result = trainer.fit()
 
-1. Your `train_func` is the Python code that each distributed training :ref:`worker <train-overview-worker>` executes.
-2. Your `ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
-3. Your `TorchTrainer` launches the distributed training job.
+1. `train_func` is the Python code that executes on each distributed training worker.
+2. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
+3. :class:`~ray.train.torch.TorchTrainer` launches the distributed training job.
 
 Compare a PyTorch Lightning training script with and without Ray Train.
 
@@ -84,6 +84,7 @@ Compare a PyTorch Lightning training script with and without Ray Train.
     .. group-tab:: PyTorch Lightning + Ray Train
 
         .. code-block:: python
+            :emphasize-lines: 8-10, 34, 43, 48-50, 52, 53, 55-60
 
             import torch
             from torchvision.models import resnet18

doc/source/train/getting-started-pytorch.rst

@@ -31,8 +31,8 @@ For reference, the final code is as follows:
     result = trainer.fit()
 
 1. `train_func` is the Python code that executes on each distributed training worker.
-2. `ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
-3. `TorchTrainer` launches the distributed training job.
+2. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
+3. :class:`~ray.train.torch.TorchTrainer` launches the distributed training job.
 
 Compare a PyTorch training script with and without Ray Train.
 
@@ -80,7 +80,8 @@ Compare a PyTorch training script with and without Ray Train.
     .. group-tab:: PyTorch + Ray Train
 
         .. code-block:: python
-       
+            :emphasize-lines: 9, 10, 12, 17, 18, 26, 27, 41, 42, 44-49
+
             import tempfile
             import torch
             from torchvision.models import resnet18
@@ -244,8 +245,8 @@ Configure scale and GPUs
 
 Outside of your training function, create a :class:`~ray.train.ScalingConfig` object to configure:
 
-1. `num_workers` - The number of distributed training worker processes.
-2. `use_gpu` - Whether each worker should use a GPU (or CPU).
+1. :class:`num_workers <ray.train.ScalingConfig>` - The number of distributed training worker processes.
+2. :class:`use_gpu <ray.train.ScalingConfig>` - Whether each worker should use a GPU (or CPU).
 
 .. code-block:: python
 

doc/source/train/getting-started-transformers.rst

@@ -28,8 +28,8 @@ For reference, the final code follows:
     trainer = TorchTrainer(train_func, scaling_config=scaling_config)
     result = trainer.fit()
 
-1. `train_func` is the Python code that executes on each distributed training :ref:`worker <train-overview-worker>`.
-2. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and computing resources (e.g. GPUs).
+1. `train_func` is the Python code that executes on each distributed training worker.
+2. :class:`~ray.train.ScalingConfig` defines the number of distributed training workers and whether to use GPUs.
 3. :class:`~ray.train.torch.TorchTrainer` launches the distributed training job.
 
 Compare a Hugging Face Transformers training script with and without Ray Train.
@@ -96,6 +96,7 @@ Compare a Hugging Face Transformers training script with and without Ray Train.
     .. group-tab:: Hugging Face Transformers + Ray Train
 
         .. code-block:: python
+            :emphasize-lines: 11-13, 15-18, 55-72
 
             import numpy as np
             import evaluate

doc/source/train/overview.rst

@@ -47,7 +47,7 @@ Worker
 
 Ray Train distributes model training compute to individual worker processes across the cluster. 
 Each worker is a process that executes the `train_func`.
-The number of workers determines the parallelism of the training job and is configured in the `ScalingConfig`.
+The number of workers determines the parallelism of the training job and is configured in the :class:`~ray.train.ScalingConfig`.
 
 .. _train-overview-scaling-config:
 
@@ -57,8 +57,8 @@ Scaling configuration
 The :class:`~ray.train.ScalingConfig` is the mechanism for defining the scale of the training job.
 Specify two basic parameters for worker parallelism and compute resources:
 
-* `num_workers`: The number of workers to launch for a distributed training job.
-* `use_gpu`: Whether each worker should use a GPU or CPU. 
+* :class:`num_workers <ray.train.ScalingConfig>`: The number of workers to launch for a distributed training job.
+* :class:`use_gpu <ray.train.ScalingConfig>`: Whether each worker should use a GPU or CPU.
 
 .. code-block:: python
 
@@ -80,9 +80,9 @@ Trainer
 
 The Trainer ties the previous three concepts together to launch distributed training jobs.
 Ray Train provides :ref:`Trainer classes <train-api>` for different frameworks. 
-Calling the `fit()` method executes the training job by:
+Calling the :meth:`fit() <ray.train.trainer.BaseTrainer.fit>` method executes the training job by:
 
-#. Launching workers as defined by the `scaling_config`.
+#. Launching workers as defined by the :ref:`scaling_config <train-overview-scaling-config>`.
 #. Setting up the framework's distributed environment on all workers.
 #. Running the `train_func` on all workers.