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examples/cpu/inference/python/jupyter-notebooks/README.md

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 Intel® Extension for PyTorch (IPEX) extends PyTorch* with optimizations for extra performance boost on Intel® hardware. While most of the optimizations will be upstreamed in future PyTorch* releases, the extension delivers up-to-date features and optimizations for PyTorch workloads on Intel® hardware. The optimization approaches generally include operator optimization, graph optimization and runtime optimization.
 
+Before selecting a sample, please make sure to (1) Check [Prerequisites](#prerequisites), (2) complete [Environment Setup](#environment-setup), and (3) see instructions to [Run the Sample](#run-the-sample).
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 ## Jupyter Notebooks Overview
 
 | Sample name | Description | Time to Complete             | Category   | Validated for AI Tools Selector |
 |---|---|---|---|---|
 [Getting Started with Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/inference/python/jupyter-notebooks/IPEX_Getting_Started.ipynb ) | This code sample demonstrates how to begin using the Intel® Extension for PyTorch* (IPEX). It will guide users how to run a PyTorch inference workload on CPU by using oneAPI AI Analytics Toolkit and also analyze the CPU usage via oneDNN verbose logs.| 15 minutes| Getting Started | Y | 
-[PyTorch Inference Optimizations with Intel® Advanced Matrix Extensions (Intel® AMX) Bfloat16 Integer8](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb) | This code sample demonstrates how to perform inference using the ResNet50 and BERT models using the Intel® Extension for PyTorch* (IPEX). IPEX allows you to speed up inference on Intel® Xeon Scalable processors with lower precision data formats and specialized computer instructions. The bfloat16 (BF16) data format uses half the bit width of floating-point-32 (FP32), which lessens the amount of memory needed and execution time to process. Likewise, the integer8 (INT8) data format uses half the bit width of BF16. | 5 minutes     | Code Optimization | Y|
-[Interactive Chat Based on DialoGPT Model Using Intel® Extension for PyTorch* Quantization](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Interactive_Chat_Quantization.ipynb)| This code sample demonstrates how to create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch* (IPEX) quantization to it. The sample shows how to create interactive chat based on the pre-trained DialoGPT model from HuggingFace and how to add INT8 dynamic quantization to it. The Intel® Extension for PyTorch* (IPEX) gives users the ability to speed up operations on processors with INT8 data format and specialized computer instructions.| 10 minutes | Concepts and Functionality| Y|
-[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX) Quantization](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Quantization.ipynb)|This code sample demonstrates how to quantize a ResNet50 model that is calibrated by the CIFAR10 dataset using the Intel® Extension for PyTorch* (IPEX). IPEX gives users the ability to speed up inference on Intel® Xeon Scalable processors with INT8 data format and specialized computer instructions. The INT8 data format uses quarter the bit width of floating-point-32 (FP32), lowering the amount of memory needed and execution time to process.| 5 minutes| Concepts and Functionality| Y|
-[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX)](https://github.com/intel-innersource/frameworks.ai.pytorch.ipex-cpu/blob/cpu-device/examples/cpu/inference/python/jupyter-notebooks/optimize_pytorch_models_with_ipex.ipynb)| This sample notebook shows how to get started with Intel® Extension for PyTorch* (IPEX) for sample Computer Vision and NLP workloads. The sample starts by loading two models from the PyTorch hub: Faster-RCNN (Faster R-CNN) and distilbert (DistilBERT). After loading the models, the sample applies sequential optimizations from Intel® Extension for PyTorch* (IPEX) and examines performance gains for each incremental change.| 30 minutes | Code Optimization |Y|
+[PyTorch Inference Optimizations with Intel® Advanced Matrix Extensions (Intel® AMX) Bfloat16 Integer8](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPyTorch_InferenceOptimizations_AMX_BF16_INT8.ipynb) | This code sample demonstrates how to perform inference using the ResNet50 and BERT models using the Intel® Extension for PyTorch* (IPEX). IPEX allows you to speed up inference on Intel® Xeon Scalable processors with lower precision data formats and specialized computer instructions. The bfloat16 (BF16) data format uses half the bit width of floating-point-32 (FP32), which lessens the amount of memory needed and execution time to process. Likewise, the integer8 (INT8) data format uses half the bit width of BF16. | 5 minutes     | Code Optimization | Y|
+[Interactive Chat Based on DialoGPT Model Using Intel® Extension for PyTorch* Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Interactive_Chat_Quantization.ipynb)| This code sample demonstrates how to create interactive chat based on pre-trained DialoGPT model and add the Intel® Extension for PyTorch* (IPEX) quantization to it. The sample shows how to create interactive chat based on the pre-trained DialoGPT model from HuggingFace and how to add INT8 dynamic quantization to it. The Intel® Extension for PyTorch* (IPEX) gives users the ability to speed up operations on processors with INT8 data format and specialized computer instructions.| 10 minutes | Concepts and Functionality| Y|
+[Optimize PyTorch Models using Intel® Extension for PyTorch (IPEX) Quantization](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IntelPytorch_Quantization.ipynb)|This code sample demonstrates how to quantize a ResNet50 model that is calibrated by the CIFAR10 dataset using the Intel® Extension for PyTorch* (IPEX). IPEX gives users the ability to speed up inference on Intel® Xeon Scalable processors with INT8 data format and specialized computer instructions. The INT8 data format uses quarter the bit width of floating-point-32 (FP32), lowering the amount of memory needed and execution time to process.| 5 minutes| Concepts and Functionality| Y|
+[Optimize PyTorch Models using Intel® Extension for PyTorch* (IPEX)](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/optimize_pytorch_models_with_ipex.ipynb)| This sample notebook shows how to get started with Intel® Extension for PyTorch* (IPEX) for sample Computer Vision and NLP workloads. The sample starts by loading two models from the PyTorch hub: Faster-RCNN (Faster R-CNN) and distilbert (DistilBERT). After loading the models, the sample applies sequential optimizations from Intel® Extension for PyTorch* (IPEX) and examines performance gains for each incremental change.| 30 minutes | Code Optimization |Y|
 
 >**Note**: For Key Implementation Details, please refer to the .ipynb file of a sample.