From 5d69b493ec2e15ff7bc21fa2a2d3d3ac31038427 Mon Sep 17 00:00:00 2001
From: Sahdev Zala <spzala@us.ibm.com>
Date: Wed, 1 Nov 2023 15:55:45 -0400
Subject: [PATCH 1/2] Change links to the newest examples (#2635)

The current links are poiting to older examples on an individual
repo. This PR replaces those links with the PyTorch upstream
examples repo. Also, adding consistency in how examples are
referenced to point to the links.

Signed-off-by: Sahdev Zala <spzala@us.ibm.com>
---
 intermediate_source/FSDP_adavnced_tutorial.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/intermediate_source/FSDP_adavnced_tutorial.rst b/intermediate_source/FSDP_adavnced_tutorial.rst
index 748c859330..5a0cb5376d 100644
--- a/intermediate_source/FSDP_adavnced_tutorial.rst
+++ b/intermediate_source/FSDP_adavnced_tutorial.rst
@@ -74,8 +74,8 @@ summarization using WikiHow dataset.  The main focus of this tutorial is to
 highlight different available features in FSDP that are helpful for training
 large scale model above 3B parameters. Also, we cover specific features for
 Transformer based models. The code for this tutorial is available in  `Pytorch
-Examples
-<https://github.com/HamidShojanazeri/examples/tree/FSDP_example/distributed/FSDP/>`__.
+examples
+<https://github.com/pytorch/examples/tree/main/distributed/FSDP/>`__.
 
 
 *Setup*
@@ -97,13 +97,13 @@ Please create a `data` folder, download the WikiHow dataset from `wikihowAll.csv
 `wikihowSep.cs <https://ucsb.app.box.com/s/7yq601ijl1lzvlfu4rjdbbxforzd2oag>`__,
 and place them in the `data` folder.  We will use the wikihow dataset from
 `summarization_dataset
-<https://github.com/HamidShojanazeri/examples/blob/FSDP_example/distributed/FSDP/summarization_dataset.py>`__.
+<https://github.com/pytorch/examples/blob/main/distributed/FSDP/summarization_dataset.py>`__.
 
 Next, we add the following code snippets to a Python script “T5_training.py”.
 
 .. note::
    The full source code for this tutorial is available in `PyTorch examples
-   <https://github.com/HamidShojanazeri/examples/tree/FSDP_example/distributed/FSDP>`__.
+   <https://github.com/pytorch/examples/tree/main/distributed/FSDP/>`__.
 
 1.3  Import necessary packages:
 

From 53e41420e0fa2bfb69e151d16a366da36236a4b3 Mon Sep 17 00:00:00 2001
From: Mark Sturdevant <mark.sturdevant@ibm.com>
Date: Wed, 1 Nov 2023 12:57:19 -0700
Subject: [PATCH 2/2] Remove broken links in tuning guide (#2636)

A couple of "for more details" links to external sites are no longer finding
details. Better to remove them.

Signed-off-by: markstur <mark.sturdevant@ibm.com>
Co-authored-by: Svetlana Karslioglu <svekars@meta.com>
---
 recipes_source/recipes/tuning_guide.py | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/recipes_source/recipes/tuning_guide.py b/recipes_source/recipes/tuning_guide.py
index dd615714a2..39fa667bc1 100644
--- a/recipes_source/recipes/tuning_guide.py
+++ b/recipes_source/recipes/tuning_guide.py
@@ -193,15 +193,12 @@ def fused_gelu(x):
 #
 #    numactl --cpunodebind=N --membind=N python <pytorch_script>
 
-###############################################################################
-# More detailed descriptions can be found `here <https://software.intel.com/content/www/us/en/develop/articles/how-to-get-better-performance-on-pytorchcaffe2-with-intel-acceleration.html>`_.
-
 ###############################################################################
 # Utilize OpenMP
 # ~~~~~~~~~~~~~~
 # OpenMP is utilized to bring better performance for parallel computation tasks.
 # ``OMP_NUM_THREADS`` is the easiest switch that can be used to accelerate computations. It determines number of threads used for OpenMP computations.
-# CPU affinity setting controls how workloads are distributed over multiple cores. It affects communication overhead, cache line invalidation overhead, or page thrashing, thus proper setting of CPU affinity brings performance benefits. ``GOMP_CPU_AFFINITY`` or ``KMP_AFFINITY`` determines how to bind OpenMP* threads to physical processing units. Detailed information can be found `here <https://software.intel.com/content/www/us/en/develop/articles/how-to-get-better-performance-on-pytorchcaffe2-with-intel-acceleration.html>`_.
+# CPU affinity setting controls how workloads are distributed over multiple cores. It affects communication overhead, cache line invalidation overhead, or page thrashing, thus proper setting of CPU affinity brings performance benefits. ``GOMP_CPU_AFFINITY`` or ``KMP_AFFINITY`` determines how to bind OpenMP* threads to physical processing units.
 
 ###############################################################################
 # With the following command, PyTorch run the task on N OpenMP threads.