From 0ccb765aeb584474d1e280faaf5bc5db572398e9 Mon Sep 17 00:00:00 2001 From: ishaghosh27 <94150575+ishaghosh27@users.noreply.github.com> Date: Thu, 24 Oct 2024 14:04:07 -0400 Subject: [PATCH] Update README.md --- .../cpu/inference/python/jupyter-notebooks/README.md | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/examples/cpu/inference/python/jupyter-notebooks/README.md b/examples/cpu/inference/python/jupyter-notebooks/README.md index 5dec72e43..47e02ee23 100644 --- a/examples/cpu/inference/python/jupyter-notebooks/README.md +++ b/examples/cpu/inference/python/jupyter-notebooks/README.md @@ -2,15 +2,17 @@ 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). + ## 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.