Merge branch 'main' into pruning_tutorial-fix

Author: ChanBong

Diff for: 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:
 

Diff for: 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.