diff --git a/doc/source/tune/_tutorials/tune-pytorch-lightning.rst b/doc/source/tune/_tutorials/tune-pytorch-lightning.rst index 4cb35a2e5..829d8ea73 100644 --- a/doc/source/tune/_tutorials/tune-pytorch-lightning.rst +++ b/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: diff --git a/doc/source/tune/examples/index.rst b/doc/source/tune/examples/index.rst index 7bbdac8ed..44d8003ff 100644 --- a/doc/source/tune/examples/index.rst +++ b/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 `_ to train a MNIST model. This example utilizes the Ray Tune-provided :ref:`PyTorch Lightning callbacks `. See also :ref:`this tutorial for a full walkthrough `. - :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. diff --git a/doc/source/tune/examples/mnist_pytorch_lightning.rst b/doc/source/tune/examples/mnist_pytorch_lightning.rst new file mode 100644 index 000000000..6bd5ed2f2 --- /dev/null +++ b/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