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Co-authored-by: matthewdeng <[email protected]>
Signed-off-by: angelinalg <[email protected]>

doc/source/train/examples.rst

@@ -28,7 +28,7 @@ Beginner
  * - DeepSpeed
  - :ref:`Train with DeepSpeed ZeRO-3 <deepspeed_example>`
  * - TensorFlow
- - :ref:`Train with TensorFlow MNIST <tensorflow_mnist_example>`
+ - :ref:`Train an MNIST Image Classifier with TensorFlow <tensorflow_mnist_example>`
  * - Horovod
  - :ref:`Train with Horovod and PyTorch <horovod_example>`
 

doc/source/train/examples/accelerate/accelerate_example.rst

@@ -6,7 +6,7 @@ Distributed Training with Hugging Face Accelerate
 =================================================
 
 This example does distributed data parallel training
-with Hugging Face (HF) Accelerate, Ray Train, and Ray Data.
+with Hugging Face Accelerate, Ray Train, and Ray Data.
 It fine-tunes a BERT model and is adapted from
 https://github.com/huggingface/accelerate/blob/main/examples/nlp_example.py
 

doc/source/train/examples/deepspeed/deepspeed_example.rst

@@ -6,7 +6,7 @@ Train with DeepSpeed ZeRO-3 and Ray Train
 =========================================
 
 This is an intermediate example that shows how to do distributed training with DeepSpeed ZeRO-3 and Ray Train.
-It demonstrates how to use :ref:`Ray Dataset <data>` with DeepSpeed ZeRO-3 and Ray Train.
+It demonstrates how to use :ref:`Ray Data <data>` with DeepSpeed ZeRO-3 and Ray Train.
 If you just want to quickly convert your existing TorchTrainer scripts into Ray Train, you can refer to the :ref:`Train with DeepSpeed <train-deepspeed>`.
 
 
@@ -21,4 +21,4 @@ See also
 
 * :ref:`Ray Train Examples <train-examples>` for more use cases.
 
-* :ref:`Get Started with DeepSpeed <train-horovod>` for a tutorial. 
+* :ref:`Get Started with DeepSpeed <train-deepspeed>` for a tutorial. 

doc/source/train/examples/lightning/lightning_cola_advanced.ipynb

@@ -11,7 +11,7 @@
  "\n",
  ":::{note}\n",
  "\n",
- "This is an intermediate example demonstrates how to use [Ray Dataset](data) with PyTorch Lightning in Ray Train.\n",
+ "This is an intermediate example demonstrates how to use [Ray Data](data) with PyTorch Lightning in Ray Train.\n",
  "\n",
  "If you just want to quickly convert your existing PyTorch Lightning scripts into Ray Train, you can refer to the [Lightning Quick Start Guide](train-pytorch-lightning).\n",
  "\n",

doc/source/train/examples/pytorch/dreambooth_finetuning.rst

@@ -6,8 +6,7 @@ Fine-tune of Stable Diffusion with DreamBooth and Ray Train
 ===========================================================
 
 This is an intermediate example that shows how to do DreamBooth fine-tuning of a Stable Diffusion model using Ray Train.
-It demonstrates how to use :ref:`Ray Dataset <data>` with PyTorch Lightning in Ray Train.
-If you just want to quickly convert your existing Transformer scripts into Ray Train, you can refer to the :ref:`Getting Started with Transformers <train-pytorch-transformers>`.
+It demonstrates how to use :ref:`Ray Data <data>` with PyTorch Lightning in Ray Train.
 
 
 See the original `DreamBooth project homepage <https://dreambooth.github.io/>`_ for more details on what this fine-tuning method achieves.

doc/source/train/huggingface-accelerate.rst

@@ -50,7 +50,7 @@ You only need to run your existing training code with a TorchTrainer. You can ex
  Model and data preparation for distributed training is completely handled by the `Accelerator <https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator>`_ 
  object and its `Accelerator.prepare() <https://huggingface.co/docs/accelerate/main/en/package_reference/accelerator#accelerate.Accelerator.prepare>`_ method.
 
- Unlike with native PyTorch, PyTorch Lightning, or HuggingFace Transformers, **don't** call any additional Ray Train utilities 
+ Unlike with native PyTorch, PyTorch Lightning, or Hugging Face Transformers, **don't** call any additional Ray Train utilities 
  like :meth:`~ray.train.torch.prepare_model` or :meth:`~ray.train.torch.prepare_data_loader` in your training function. 
 
 Configure Accelerate