Merge branch 'main' into pianpwk/test_datadep_pr

Author: pianpwk

.lycheeignore

@@ -15,3 +15,6 @@ http://localhost:6006
 
 # Ignore local host link from recipes_source/deployment_with_flask.rst
 http://localhost:5000/predict 
+
+# Ignore local host link from advanced_source/cpp_frontend.rst 
+https://www.uber.com/blog/deep-neuroevolution/

advanced_source/cpp_frontend.rst

@@ -57,7 +57,7 @@ the right tool for the job. Examples for such environments include:
   Multiprocessing is an alternative, but not as scalable and has significant
   shortcomings. C++ has no such constraints and threads are easy to use and
   create. Models requiring heavy parallelization, like those used in `Deep
-  Neuroevolution <https://eng.uber.com/deep-neuroevolution/>`_, can benefit from
+  Neuroevolution <https://www.uber.com/blog/deep-neuroevolution/>`_, can benefit from
   this.
 - **Existing C++ Codebases**: You may be the owner of an existing C++
   application doing anything from serving web pages in a backend server to
@@ -662,7 +662,7 @@ Defining the DCGAN Modules
 We now have the necessary background and introduction to define the modules for
 the machine learning task we want to solve in this post. To recap: our task is
 to generate images of digits from the `MNIST dataset
-<http://yann.lecun.com/exdb/mnist/>`_. We want to use a `generative adversarial
+<https://huggingface.co/datasets/ylecun/mnist>`_. We want to use a `generative adversarial
 network (GAN)
 <https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf>`_ to solve
 this task. In particular, we'll use a `DCGAN architecture

intermediate_source/ddp_series_minGPT.rst

@@ -6,7 +6,7 @@ training <ddp_series_multinode.html>`__ \|\| **minGPT Training**
 Training “real-world” models with DDP
 =====================================
 
-Authors: `Suraj Subramanian <https://github.com/suraj813>`__
+Authors: `Suraj Subramanian <https://github.com/subramen>`__
 
 .. grid:: 2
 

intermediate_source/ddp_series_multinode.rst

@@ -6,7 +6,7 @@ training** \|\| `minGPT Training <ddp_series_minGPT.html>`__
 Multinode Training
 ==================
 
-Authors: `Suraj Subramanian <https://github.com/suraj813>`__
+Authors: `Suraj Subramanian <https://github.com/subramen>`__
 
 .. grid:: 2
 

intermediate_source/dynamic_quantization_bert_tutorial.rst

@@ -138,7 +138,7 @@ the following helper functions: one for converting the text examples
 into the feature vectors; The other one for measuring the F1 score of
 the predicted result.
 
-The `glue_convert_examples_to_features <https://github.com/huggingface/transformers/blob/master/transformers/data/processors/glue.py>`_ function converts the texts into input features:
+The `glue_convert_examples_to_features <https://github.com/huggingface/transformers/blob/main/src/transformers/data/datasets/glue.py>`_ function converts the texts into input features:
 
 -  Tokenize the input sequences;
 -  Insert [CLS] in the beginning;
@@ -147,7 +147,7 @@ The `glue_convert_examples_to_features <https://github.com/huggingface/transform
 -  Generate token type ids to indicate whether a token belongs to the
    first sequence or the second sequence.
 
-The `glue_compute_metrics <https://github.com/huggingface/transformers/blob/master/transformers/data/processors/glue.py>`_  function has the compute metrics with
+The `glue_compute_metrics <https://github.com/huggingface/transformers/blob/main/src/transformers/data/metrics/__init__.py#L60>`_  function has the compute metrics with
 the `F1 score <https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html>`_, which
 can be interpreted as a weighted average of the precision and recall,
 where an F1 score reaches its best value at 1 and worst score at 0. The
@@ -273,7 +273,7 @@ We load the tokenizer and fine-tuned BERT sequence classifier model
 2.3 Define the tokenize and evaluation function
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
-We reuse the tokenize and evaluation function from `HuggingFace <https://github.com/huggingface/transformers/blob/master/examples/run_glue.py>`_.
+We reuse the tokenize and evaluation function from `HuggingFace <https://github.com/huggingface/transformers/blob/main/examples/legacy/pytorch-lightning/run_glue.py>`_.
 
 .. code:: python
 

intermediate_source/torchserve_with_ipex.rst

@@ -379,8 +379,8 @@ For interested readers, please check out the following documents:
 
 - `CPU specific optimizations <https://pytorch.org/tutorials/recipes/recipes/tuning_guide.html#cpu-specific-optimizations>`_
 - `Maximize Performance of Intel® Software Optimization for PyTorch* on CPU <https://www.intel.com/content/www/us/en/developer/articles/technical/how-to-get-better-performance-on-pytorchcaffe2-with-intel-acceleration.html>`_
-- `Performance Tuning Guide <https://intel.github.io/intel-extension-for-pytorch/tutorials/performance_tuning/tuning_guide.html>`_
-- `Launch Script Usage Guide <https://intel.github.io/intel-extension-for-pytorch/tutorials/performance_tuning/launch_script.html>`_
+- `Performance Tuning Guide <https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html>`_
+- `Launch Script Usage Guide <https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/launch_script.html>`_
 - `Top-down Microarchitecture Analysis Method <https://www.intel.com/content/www/us/en/develop/documentation/vtune-cookbook/top/methodologies/top-down-microarchitecture-analysis-method.html>`_
 - `Configuring oneDNN for Benchmarking <https://oneapi-src.github.io/oneDNN/dev_guide_performance_settings.html#benchmarking-settings>`_
 - `Intel® VTune™ Profiler <https://www.intel.com/content/www/us/en/developer/tools/oneapi/vtune-profiler.html#gs.tcbgpa>`_