[tune/docs] Add PTL example to tune docs/examples (#11474)

doc/source/tune/_tutorials/tune-pytorch-lightning.rst

@@ -26,7 +26,7 @@ use it plug and play for your existing models, assuming their parameters are con
 
     .. code-block:: bash
 
-        $ pip install ray torch torchvision pytorch-lightning
+        $ pip install "ray[tune]" torch torchvision pytorch-lightning
 
 .. contents::
     :local:

doc/source/tune/examples/index.rst

@@ -44,6 +44,7 @@ PyTorch Examples
 ~~~~~~~~~~~~~~~~
 
 - :doc:`/tune/examples/mnist_pytorch`: Converts the PyTorch MNIST example to use Tune with the function-based API. Also shows how to easily convert something relying on argparse to use Tune.
+- :doc:`/tune/examples/mnist_pytorch_lightning`: Uses `Pytorch Lightning <https://github.com/PyTorchLightning/pytorch-lightning>`_ to train a MNIST model. This example utilizes the Ray Tune-provided :ref:`PyTorch Lightning callbacks <tune-integration-pytorch-lightning>`. See also :ref:`this tutorial for a full walkthrough <tune-pytorch-lightning>`.
 - :doc:`/tune/examples/mnist_pytorch_trainable`: Converts the PyTorch MNIST example to use Tune with Trainable API. Also uses the HyperBandScheduler and checkpoints the model at the end.
 - :doc:`/tune/examples/ddp_mnist_torch`: An example showing how to use DistributedDataParallel with Ray Tune. This enables both distributed training and distributed hyperparameter tuning.
 

doc/source/tune/examples/mnist_pytorch_lightning.rst

@@ -0,0 +1,6 @@
+:orphan:
+
+mnist_pytorch_lightning
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. literalinclude:: /../../python/ray/tune/examples/mnist_pytorch_lightning.py