Merge branch 'main' into master

Author: woongjoonchoi

index.rst

@@ -3,11 +3,11 @@ Welcome to PyTorch Tutorials
 
 **What's new in PyTorch tutorials?**
 
-* `Using User-Defined Triton Kernels with torch.compile <https://pytorch.org/tutorials/recipes/torch_compile_user_defined_triton_kernel_tutorial.html>`__
-* `Large Scale Transformer model training with Tensor Parallel (TP) <https://pytorch.org/tutorials/intermediate/TP_tutorial.html>`__
-* `Accelerating BERT with semi-structured (2:4) sparsity <https://pytorch.org/tutorials/advanced/semi_structured_sparse.html>`__
-* `torch.export Tutorial with torch.export.Dim <https://pytorch.org/tutorials/intermediate/torch_export_tutorial.html>`__
-* `Extension points in nn.Module for load_state_dict and tensor subclasses <https://pytorch.org/tutorials/recipes/recipes/swap_tensors.html>`__
+* `Introduction to Distributed Pipeline Parallelism <https://pytorch.org/tutorials/intermediate/pipelining_tutorial.html>`__
+* `Introduction to Libuv TCPStore Backend <https://pytorch.org/tutorials/intermediate/TCPStore_libuv_backend.html>`__ 
+* `Asynchronous Saving with Distributed Checkpoint (DCP) <https://pytorch.org/tutorials/recipes/distributed_async_checkpoint_recipe.html>`__
+* `Python Custom Operators <https://pytorch.org/tutorials/advanced/python_custom_ops.html>`__
+* Updated `Getting Started with DeviceMesh <https://pytorch.org/tutorials/recipes/distributed_device_mesh.html>`__
 
 .. raw:: html
 

intermediate_source/TCPStore_libuv_backend.rst

@@ -8,7 +8,8 @@ Introduction to Libuv TCPStore Backend
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
-    :class-card: card-prerequisites
+      :class-card: card-prerequisites
+  
       *  What is the new TCPStore backend
       *  Compare the new libuv backend against the legacy backend
       *  How to enable to use the legacy backend

intermediate_source/pipelining_tutorial.rst

@@ -12,13 +12,15 @@ APIs.
 .. grid:: 2
 
    .. grid-item-card:: :octicon:`mortar-board;1em;` What you will learn
+      :class-card: card-prerequisites
 
       *  How to use ``torch.distributed.pipelining`` APIs
       *  How to apply pipeline parallelism to a transformer model
       *  How to utilize different schedules on a set of microbatches
 
 
    .. grid-item-card:: :octicon:`list-unordered;1em;` Prerequisites
+      :class-card: card-prerequisites
 
       * Familiarity with `basic distributed training  <https://pytorch.org/tutorials/beginner/dist_overview.html>`__ in PyTorch
 

recipes_source/intel_extension_for_pytorch.rst

@@ -12,8 +12,8 @@ easy GPU acceleration for Intel discrete GPUs with PyTorch*.
 Intel® Extension for PyTorch* has been released as an open–source project
 at `Github <https://github.com/intel/intel-extension-for-pytorch>`_.
 
-- Source code for CPU is available at `master branch <https://github.com/intel/intel-extension-for-pytorch/tree/master>`_.
-- Source code for GPU is available at `xpu-master branch <https://github.com/intel/intel-extension-for-pytorch/tree/xpu-master>`_.
+- Source code for CPU is available at `main branch <https://github.com/intel/intel-extension-for-pytorch/tree/main>`_.
+- Source code for GPU is available at `xpu-main branch <https://github.com/intel/intel-extension-for-pytorch/tree/xpu-main>`_.
 
 Features
 --------