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>

Author: markstur

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.