Removing refernces to deleted tutorial.

Author: AlannaBurke

index.rst

@@ -619,13 +619,6 @@ Welcome to PyTorch Tutorials
    :link: intermediate/torchserve_with_ipex
    :tags: Model-Optimization,Production
 
-.. customcarditem::
-   :header: Grokking PyTorch Intel CPU Performance from First Principles (Part 2)
-   :card_description: A case study on the TorchServe inference framework optimized with Intel® Extension for PyTorch (Part 2).
-   :image: _static/img/thumbnails/cropped/generic-pytorch-logo.png
-   :link: intermediate/torchserve_with_ipex_2
-   :tags: Model-Optimization,Production
-
 .. customcarditem::
    :header: Multi-Objective Neural Architecture Search with Ax
    :card_description: Learn how to use Ax to search over architectures find optimal tradeoffs between accuracy and latency.

recipes_source/xeon_run_cpu.rst

@@ -214,12 +214,12 @@ The generic option settings (knobs) include the following:
      - default value
      - help
    * - ``-h``, ``--help``
-     - 
-     - 
+     -
+     -
      - To show the help message and exit.
    * - ``-m``, ``--module``
-     - 
-     - 
+     -
+     -
      - To change each process to interpret the launch script as a python module, executing with the same behavior as "python -m".
    * - ``--no-python``
      - bool
@@ -323,7 +323,7 @@ Knobs for controlling instance number and compute resource allocation are:
      - bool
      - False
      - To disable the usage of ``taskset`` command.
-	 
+
 .. note::
 
    Environment variables that will be set by this script include the following:
@@ -344,13 +344,13 @@ Knobs for controlling instance number and compute resource allocation are:
         - Value of ``ncores_per_instance``
       * - MALLOC_CONF
         - If libjemalloc.so is preloaded, MALLOC_CONF will be set to ``"oversize_threshold:1,background_thread:true,metadata_thp:auto"``.
-		
+
    Please note that the script respects environment variables set preliminarily. For example, if you have set the environment variables mentioned above before running the script, the values of the variables will not be overwritten by the script.
 
 Conclusion
 ----------
 
-In this tutorial, we explored a variety of advanced configurations and tools designed to optimize PyTorch inference performance on Intel® Xeon® Scalable Processors. 
+In this tutorial, we explored a variety of advanced configurations and tools designed to optimize PyTorch inference performance on Intel® Xeon® Scalable Processors.
 By leveraging the ``torch.backends.xeon.run_cpu`` script, we demonstrated how to fine-tune thread and memory management to achieve peak performance.
 We covered essential concepts such as NUMA access control, optimized memory allocators like ``TCMalloc`` and ``JeMalloc``, and the use of Intel® OpenMP for efficient multithreading.
 
@@ -361,4 +361,4 @@ See also:
 
 * `PyTorch Performance Tuning Guide <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#cpu-specific-optimizations>`__
 * `PyTorch Multiprocessing Best Practices <https://pytorch.org/docs/stable/notes/multiprocessing.html#cpu-in-multiprocessing>`__
-* Grokking PyTorch Intel CPU performance: `Part 1 <https://pytorch.org/tutorials/intermediate/torchserve_with_ipex>`__  `Part 2 <https://pytorch.org/tutorials/intermediate/torchserve_with_ipex_2>`__
+* Grokking PyTorch Intel CPU performance: `Part 1 <https://pytorch.org/tutorials/intermediate/torchserve_with_ipex>`__