Merge branch 'main' into pull-2.6-binaries

Author: svekars

CONTRIBUTING.md

@@ -218,9 +218,8 @@ described in the preceding sections:
 - [NLP From Scratch: Generating Names with a Character-Level RNN
 Tutorial](https://pytorch.org/tutorials/intermediate/char_rnn_generation_tutorial.html)
 
-If you are creating a recipe, we recommend that you use [this
-template](https://github.com/pytorch/tutorials/blob/tutorials_refresh/recipes_source/recipes/example_recipe.py)
-as a guide.
+If you are creating a recipe, [this is a good
+example.](https://github.com/pytorch/tutorials/blob/main/recipes_source/recipes/what_is_state_dict.py)
 
 
 # Submission Process #

advanced_source/cpp_autograd.rst

@@ -255,9 +255,9 @@ Out:
   [ CPUFloatType{3,4} ]
 
 Please see the documentation for ``torch::autograd::backward``
-(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html>`_)
+(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_)
 and ``torch::autograd::grad``
-(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html>`_)
+(`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_)
 for more information on how to use them.
 
 Using custom autograd function in C++
@@ -394,9 +394,9 @@ C++ using the following table:
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | Python                         | C++                                                                                                                                                                    |
 +================================+========================================================================================================================================================================+
-| ``torch.autograd.backward``    | ``torch::autograd::backward`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1afa9b5d4329085df4b6b3d4b4be48914b.html>`_)                  |
+| ``torch.autograd.backward``    | ``torch::autograd::backward`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1403bf65b1f4f8c8506a9e6e5312d030.html>`_)                  |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
-| ``torch.autograd.grad``        | ``torch::autograd::grad`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1a1e03c42b14b40c306f9eb947ef842d9c.html>`_)                      |
+| ``torch.autograd.grad``        | ``torch::autograd::grad`` (`link <https://pytorch.org/cppdocs/api/function_namespacetorch_1_1autograd_1ab9fa15dc09a8891c26525fb61d33401a.html>`_)                      |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+
 | ``torch.Tensor.detach``        | ``torch::Tensor::detach`` (`link <https://pytorch.org/cppdocs/api/classat_1_1_tensor.html#_CPPv4NK2at6Tensor6detachEv>`_)                                              |
 +--------------------------------+------------------------------------------------------------------------------------------------------------------------------------------------------------------------+

beginner_source/pytorch_with_examples.rst

@@ -149,7 +149,7 @@ which will be optimized during learning.
 
 In TensorFlow, packages like
 `Keras <https://github.com/fchollet/keras>`__,
-`TensorFlow-Slim <https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim>`__,
+`TensorFlow-Slim <https://github.com/google-research/tf-slim>`__,
 and `TFLearn <http://tflearn.org/>`__ provide higher-level abstractions
 over raw computational graphs that are useful for building neural
 networks.

intermediate_source/rpc_tutorial.rst

@@ -59,7 +59,7 @@ Distributed Reinforcement Learning using RPC and RRef
 -----------------------------------------------------
 
 This section describes steps to build a toy distributed reinforcement learning
-model using RPC to solve CartPole-v1 from `OpenAI Gym <https://gym.openai.com>`__.
+model using RPC to solve CartPole-v1 from `OpenAI Gym <https://www.gymlibrary.dev/environments/classic_control/cart_pole/>`__.
 The policy code is mostly borrowed from the existing single-thread
 `example <https://github.com/pytorch/examples/blob/master/reinforcement_learning>`__
 as shown below. We will skip details of the ``Policy`` design, and focus on RPC
@@ -156,7 +156,7 @@ send commands. Applications don't need to worry about the lifetime of ``RRefs``.
 The owner of each ``RRef`` maintains a reference counting map to track its
 lifetime, and guarantees the remote data object will not be deleted as long as
 there is any live user of that ``RRef``. Please refer to the ``RRef``
-`design doc <https://pytorch.org/docs/master/notes/rref.html>`__ for details.
+`design doc <https://pytorch.org/docs/stable/rpc/rref.html>`__ for details.
 
 
 .. code:: python
@@ -531,7 +531,7 @@ the given arguments (i.e., ``lr=0.05``).
 In the training loop, it first creates a distributed autograd context, which
 will help the distributed autograd engine to find gradients and involved RPC
 send/recv functions. The design details of the distributed autograd engine can
-be found in its `design note <https://pytorch.org/docs/master/notes/distributed_autograd.html>`__.
+be found in its `design note <https://pytorch.org/docs/stable/rpc/distributed_autograd.html>`__.
 Then, it kicks off the forward pass as if it is a local
 model, and run the distributed backward pass. For the distributed backward, you
 only need to specify a list of roots, in this case, it is the loss ``Tensor``.

recipes_source/recipes_index.rst

@@ -157,6 +157,13 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/torch_export_aoti_python.html
    :tags: Basics
 
+.. customcarditem::
+   :header: Demonstration of torch.export flow, common challenges and the solutions to address them
+   :card_description: Learn how to export models for popular usecases
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/torch_export_challenges_solutions.html
+   :tags: Compiler,TorchCompile
+
 .. Interpretability
 
 .. customcarditem::
@@ -472,3 +479,4 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    /recipes/distributed_optim_torchscript
    /recipes/mobile_interpreter
    /recipes/distributed_comm_debug_mode
+   /recipes/torch_export_challenges_solutions