Ai and analytics features and functionality intel tensor flow enabling auto mixed precision for transfer learning (#1461)

* Fixes for 2023.1 AI Kit  (#1409)

* Intel Python Numpy Numba_dpes kNN sample (#1292)

* *.py and *.ipynb files with implementation
* README.md and sample.json files with documentation
* License and thir party programs

* Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample (#1293)

* add IntelPytorch Quantization code samples (#1301)

* add IntelPytorch Quantization code samples

* fix the spelling error in the README file

* use john's README with grammar fix and title change

* Rename third-party-grograms.txt to third-party-programs.txt

Co-authored-by: Jimmy Wei <[email protected]>

* AMX bfloat16 mixed precision learning TensorFlow Transformer sample (#1317)

* [New Sample] Intel Extension for TensorFlow Getting Started (#1313)

* first draft

* Update README.md

* remove redunant file

* [New Sample] [oneDNN] Benchdnn tutorial  (#1315)

* New Sample: benchDNN tutorial

* Update readme: new sample

* Rename sample to benchdnn_tutorial

* Name fix

* Add files via upload (#1320)

* [New Sample] oneCCL Bindings for PyTorch Getting Started (#1316)

* Update README.md

* [New Sample] oneCCL Bindings for PyTorch Getting Started

* Update README.md

* add torch-ccl version check

* [New Sample] Intel Extension for PyTorch Getting Started (#1314)

* add new ipex GSG notebook for dGPU

* Update sample.json

for expertise field

* Update requirements.txt

Update package versions to comply with Snyk  tool

* Updated title field in sample.json in TF Transformer AMX bfloat16 Mixed Precision sample to fit within character length range (#1327)

* add arch checker class (#1332)

* change gpu.patch to convert the code samples from cpu to gpu correctly (#1334)

* Fixes for spelling in AMX bfloat16 transformer sample and printing error in python code in numpy vs numba sample (#1335)

* 2023.1 ai kit itex get started example fix (#1338)

* Fix the typo

* Update ResNet50_Inference.ipynb

* fix resnet inference demo link (#1339)

* Fix printing issue in numpy vs numba AI sample (#1356)

* Fix Invalid Kmeans parameters on oneAPI 2023 (#1345)

* Update README to add new samples into the list (#1366)

* PyTorch AMX BF16 Training sample: remove graphs and performance numbers (#1408)

* Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample

* remove performance graphs, update README

* remove graphs from README and folder

* update top README in Features and Functionality

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Co-authored-by: krzeszew <[email protected]>
Co-authored-by: alexsin368 <[email protected]>
Co-authored-by: ZhaoqiongZ <[email protected]>
Co-authored-by: Louie Tsai <[email protected]>
Co-authored-by: Orel Yehuda <[email protected]>
Co-authored-by: yuning <[email protected]>
Co-authored-by: Wang, Kai Lawrence <[email protected]>
Co-authored-by: xiguiw <[email protected]>

* Updated Enable Auto-Mixed Precision for Transfer Learning with TensorFlow readme

Updated slightly since I had already restructured a version of this readme prior to original submission. Updated name in readme to match the sample.json. Corrected some branding and formatting.

---------

Co-authored-by: Jimmy Wei <[email protected]>
Co-authored-by: krzeszew <[email protected]>
Co-authored-by: alexsin368 <[email protected]>
Co-authored-by: ZhaoqiongZ <[email protected]>
Co-authored-by: Louie Tsai <[email protected]>
Co-authored-by: Orel Yehuda <[email protected]>
Co-authored-by: yuning <[email protected]>
Co-authored-by: Wang, Kai Lawrence <[email protected]>
Co-authored-by: xiguiw <[email protected]>

Author: 10 people

AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_Enabling_Auto_Mixed_Precision_for_TransferLearning/README.md

@@ -1,19 +1,21 @@
-# Enabling Auto-Mixed Precision for Transfer Learning with TensorFlow
-This tutorial guides you through the process of enabling auto-mixed precision to use low-precision datatypes, like bfloat16, for transfer learning with TensorFlow* (TF).
+# `Enable Auto-Mixed Precision for Transfer Learning with TensorFlow*` Sample
 
-This sample demonstrates the end-to-end pipeline tasks typically performed in a deep learning use-case: training (and retraining), inference optimization, and serving the model with TensorFlow Serving.
+The `Enable Auto-Mixed Precision for Transfer Learning with TensorFlow*` sample guides you through the process of enabling auto-mixed precision to use low-precision datatypes, like bfloat16, for transfer learning with TensorFlow* (TF).
+
+The sample demonstrates the end-to-end pipeline tasks typically performed in a deep learning use-case: training (and retraining), inference optimization, and serving the model with TensorFlow Serving.
 
 | Area                    | Description
 |:---                     |:---
-| What you will learn     | Enable Auto-Mixed Precision for Transfer Learning with TensorFlow
+| What you will learn     | Enable Auto-Mixed Precision for Transfer Learning with TensorFlow*
 | Time to complete        | 30 minutes
+| Category                | Code Optimization
 
 ## Purpose
 
-Through the implementation of end-to-end deep learning example, this sample demonstrates three important concepts:
-1. The benefits of using auto-mixed precision to accelerate tasks like transfer learning, with minimal changes to existing scripts.
-2. The importance of inference optimization on performance.
-3. The ease of using Intel® optimizations in TensorFlow, which are enabled by default in 2.9.0 and newer.
+Through the implementation of end-to-end deep learning example, this sample demonstrates important concepts:
+- The benefits of using auto-mixed precision to accelerate tasks like transfer learning, with minimal changes to existing scripts.
+- The importance of inference optimization on performance.
+- The ease of using Intel® optimizations in TensorFlow, which are enabled by default in 2.9.0 and newer.
 
 ## Prerequisites
 
@@ -64,7 +66,10 @@ The sample tutorial contains one Jupyter Notebook and two Python scripts.
 |`freeze_optimize_v2.py`   |The script optimizes a pre-trained TensorFlow model PB file.
 |`tf_benchmark.py`         |The script measures inference performance of a model using dummy data.
 
-## Run the Sample on Linux*
+## Run the Enable Auto-Mixed Precision for Transfer Learning with TensorFlow* 
+
+### On Linux*
+
 1. Launch Jupyter Notebook.
    ```
    jupyter notebook --ip=0.0.0.0
@@ -78,7 +83,7 @@ The sample tutorial contains one Jupyter Notebook and two Python scripts.
 5. Run every cell in the Notebook in sequence.
 
 
-### Run the Sample on Intel® DevCloud
+### Run the Sample on Intel® DevCloud (Optional)
 
 1. If you do not already have an account, request an Intel® DevCloud account at [*Create an Intel® DevCloud Account*](https://intelsoftwaresites.secure.force.com/DevCloud/oneapi).
 2. On a Linux* system, open a terminal.
@@ -102,7 +107,8 @@ If you receive an error message, troubleshoot the problem using the **Diagnostic
 
 
 ## Example Output
-You will see diagrams that compare performance and analysis. 
+
+You will see diagrams comparing performance and analysis. 
 
 The following image illustrates performance comparison for training speedup obtained by enabling auto-mixed precision.
 
@@ -112,7 +118,6 @@ For performance analysis, you will see histograms showing different Tensorflow*
 
 ![Inference Speedup](images/inference-perf-comp.png)
 
-
 ## License
 
 Code samples are licensed under the MIT license. See