Update README.md

jimmytwei · web-flow · commit 3a70325e7f9c · 2024-06-20T07:40:26.000-07:00
diff --git a/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/README.md b/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow/README.md
@@ -1,35 +1,36 @@
-# `Intel® Neural Compressor TensorFlow* Getting Started*` Sample
+# `Intel® Neural Compressor TensorFlow* Getting Started` Sample
 
-This sample demonstrates using the Intel® Neural Compressor, which is part of the Intel® AI Tools with the with Intel® Optimizations for TensorFlow* to speed up inference by simplifying the process of converting the FP32 model to INT8/BF16.
+This sample demonstrates using the Intel® Neural Compressor, which is part of the AI Tools with the with Intel® Extension for TensorFlow* to speed up inference by simplifying the process of converting the FP32 model to INT8/BF16.
 
-| Property                 | Description
-|:---                      |:---
-| Category                 | Getting Started
-| What you will learn      | How to use Intel® Neural Compressor tool to quantize the AI model based on TensorFlow* and speed up the inference on Intel® Xeon® CPUs
-| Time to complete         | 10 minutes
+| Property                 | Description |
+|:---                      |:--          |
+| Category                 | Getting Started |
+| What you will learn      | How to use Intel® Neural Compressor tool to quantize the AI model based on TensorFlow* and speed up the inference on Intel® Xeon® CPUs |
+| Time to complete         | 10 minutes |
 
 
 ## Purpose
 
-This sample shows the process of building a convolutional neural network (CNN) model to recognize handwritten numbers and demonstrates how to increase the inference performance by using Intel® Neural Compressor. Low-precision optimizations can speed up inference. Intel® Neural Compressor simplifies the process of converting the FP32 model to INT8/BF16. At the same time, Intel® Neural Compressor tunes the quantization method to reduce the accuracy loss, which is a big blocker for low-precision inference.
+This sample shows the process of building a convolution neural network (CNN) model to recognize handwritten numbers and demonstrates how to increase the inference performance by using Intel® Neural Compressor. Low-precision optimizations can speed up inference. Intel® Neural Compressor simplifies the process of converting the FP32 model to INT8/BF16. At the same time, Intel® Neural Compressor tunes the quantization method to reduce the accuracy loss, which is a big blocker for low-precision inference.
 
 You can achieve higher inference performance by converting the FP32 model to INT8 or BF16 model. Additionally, Intel® Deep Learning Boost (Intel® DL Boost) in Intel® Xeon® Scalable processors and Xeon® processors provides hardware acceleration for INT8 and BF16 models.
 
 You will learn how to train a CNN model with Keras and TensorFlow*, use Intel® Neural Compressor to quantize the model, and compare the performance to see the benefit of Intel® Neural Compressor.
 
 ## Prerequisites
 
-| Optimized for                     | Description
-|:---                               |:---
-| OS                                | Ubuntu* 20.04 (or newer) <br> Windows 11, 10*
-| Hardware                          | Intel® Core™ Gen10 Processor <br> Intel® Xeon® Scalable Performance processors
-| Software                          | Intel® Neural Compressor, Intel Optimization for TensorFlow
+| Optimized for                     | Description |
+|:---                               |:---         |
+| OS                                | Ubuntu* 20.04 (or newer) <br> Windows 11, 10* |
+| Hardware                          | Intel® Core™ Gen10 Processor <br> Intel® Xeon® Scalable Performance processors |
+| Software                          | Intel® Neural Compressor, Intel® Extension for TensorFlow* |
+> **Note**: AI and Analytics samples are validated on AI Tools Offline Installer. For the full list of validated platforms refer to [Platform Validation](https://github.com/oneapi-src/oneAPI-samples/tree/master?tab=readme-ov-file#platform-validation).
 
 ### Intel® Neural Compressor and Sample Code Versions
 
 >**Note**: See the [Intel® Neural Compressor](https://github.com/intel/neural-compressor) GitHub repository for more information and recent changes.
 
-This sample is updated regularly to match the Intel® Neural Compressor version in the latest Intel® AI Tools release. If you want to get the sample code for an earlier toolkit release, check out the corresponding git tag.
+This sample is updated regularly to match the Intel® Neural Compressor version in the latest AI Tools release. If you want to get the sample code for an earlier toolkit release, check out the corresponding git tag.
 
 1. List the available git tags.
    ```
@@ -43,22 +44,6 @@ This sample is updated regularly to match the Intel® Neural Compressor version
    git checkout 2022.3.0
    ```
 
-### For Local Development Environments
-
-You will need to download and install the following toolkits, tools, and components to use the sample.
-
-- **Intel® AI Tools **
-
-  You can get the AI Kit from [Intel® oneAPI Toolkits](https://www.intel.com/content/www/us/en/developer/tools/oneapi/toolkits.html#analytics-kit). <br> See [*Get Started with the Intel® AI Tools for Linux**](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux) for AI Tools installation information and post-installation steps and scripts.
-
-  Intel® Extension for TensorFlow* is included in the Intel AI Tools Offline Installer package.
-
-- **Jupyter Notebook**
-
-  Install using PIP: `$pip -m install notebook`. <br> Alternatively, see [*Installing Jupyter*](https://jupyter.org/install) for detailed installation instructions.
-
-- **TensorFlow\* 2.2** (or newer)
-
 ## Key Implementation Details
 
 The sample demonstrates how to:
@@ -72,131 +57,101 @@ The sample demonstrates how to:
 - Test the performance of the FP32 model and INT8 (quantization) model.
 
 ## Environment Setup
+
+You will need to download and install the following toolkits, tools, and components to use the sample.
+
 If you have already set up the PIP or Conda environment and installed AI Tools go directly to Run the Notebook.
 
-### On Linux* 
+### 1. Get AI Tools
+
+Required AI Tools: Intel® Neural Compressor, Intel® Extension for TensorFlow* (CPU)
 
-#### Setup Conda Environment
+If you have not already, select and install these Tools via [AI Tools Selector](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html). AI and Analytics samples are validated on AI Tools Offline Installer. It is recommended to select Offline Installer option in AI Tools Selector.
 
-You can list the available conda environments using a command similar to the following.
+>**Note**: If Docker option is chosen in AI Tools Selector, refer to [Working with Preset Containers](https://github.com/intel/ai-containers/tree/main/preset) to learn how to run the docker and samples.
 
-##### Option 1: Clone Conda Environment from AI Toolkit Conda Environment
+### 2. (Offline Installer) Activate the AI Tools bundle base environment
 
-Please confirm to install Intel AI Toolkit!
+If the default path is used during the installation of AI Tools:
 
 ```
-conda info -e
-# conda environments:
-#
-base                  *  /opt/intel/oneapi/intelpython/latest
-pytorch                  /opt/intel/oneapi/intelpython/latest/envs/pytorch
-pytorch-1.7.0            /opt/intel/oneapi/intelpython/latest/envs/pytorch-1.7.0
-tensorflow               /opt/intel/oneapi/intelpython/latest/envs/tensorflow
-tensorflow-2.3.0         /opt/intel/oneapi/intelpython/latest/envs/tensorflow-2.3.0
-                         /opt/intel/oneapi/pytorch/1.7.0
-                         /opt/intel/oneapi/tensorflow/2.3.0
+source $HOME/intel/oneapi/intelpython/bin/activate
 ```
-1. Activate the conda environment with Intel® Optimizations for TensorFlow*.
-
-   By default, the Intel® AI Analytics Toolkit is installed in
-   the `/opt/intel/oneapi` folder, which requires root privileges to manage it.
-
-   1. If you have the root access to your oneAPI installation path:
-       ```
-       conda activate tensorflow
-       (tensorflow) xxx@yyy:
-       ```
-
-   2. If you do not have the root access to your oneAPI installation path, clone the `tensorflow` conda environment using the following command:
-      ```
-      conda create --name usr_tensorflow --clone tensorflow
-       ```
-
-   3. Activate your conda environment with the following command:
-      ```
-      source activate usr_tensorflow
-      ```
-2. Install Intel® Neural Compressor from the local channel.
-   ```
-   conda install -c ${ONEAPI_ROOT}/conda_channel neural-compressor -y --offline  
-   ```
 
-3. Install dependencies**
+If a non-default path is used:
+
 ```
-pip install -r requirements.txt
+source <custom_path>/bin/activate
 ```
-**Install Jupyter Notebook** by running `pip install notebook`. Alternatively, see [Installing Jupyter](https://jupyter.org/install) for detailed installation instructions.
 
+### 3. (Offline Installer) Activate relevant Conda environment
 
-##### Option 2: Create Conda Environment
+```
+conda activate tensorflow
+```
 
-Configure Conda for **user_tensorflow** by entering commands similar to the following:
-   ```
-   conda deactivate
-   conda env remove -n user_tensorflow
-   conda create -n user_tensorflow python=3.9 -y
-   conda activate user_tensorflow
-   conda install -n user_tensorflow pycocotools -c esri -y
-   conda install -n user_tensorflow neural-compressor tensorflow -c conda-forge -c intel -y
-   conda install -n user_tensorflow jupyter runipy notebook -y
-   conda install -c anaconda ipykernel
-   python -m ipykernel install --user --nam=user_tensorflow
-   ```
+### 4. Clone the GitHub repository
 
+```
+git clone https://github.com/oneapi-src/oneAPI-samples.git
+cd oneAPI-samples/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow
+```
 
-#### Configure Jupyter Notebook
+### 5. Install dependencies
 
-Create a new kernel for the Jupyter notebook based on your activated conda environment.
-   ```
-   conda install ipykernel
-   python -m ipykernel install --user --name usr_tensorflow
-   ```
-   This step is optional if you plan to open the notebook on your local server.
+Install for Jupyter Notebook:
 
-## Run the `Intel® Neural Compressor TensorFlow* Getting Started*` Sample
+```
+pip install -r requirements.txt
+```
+
+For Jupyter Notebook, refer to [Installing Jupyter](https://jupyter.org/install) for detailed installation instructions.
+
+
+## Run the Sample
 
 > **Note**: Before running the sample, make sure [Environment Setup](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-TensorFlow#environment-setup) is completed.
 >
-> Linux*:
 Go to the section which corresponds to the installation method chosen in [AI Tools Selector](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html) to see relevant instructions:
+
 * [AI Tools Offline Installer (Validated)](#ai-tools-offline-installer-validated)
-* [Conda/PIP](#condapip) 
+* [Conda/PIP](#condapip)
 * [Docker](#docker)
 
-### AI Tools Offline Installer (Validated)  
-1. If you have not already done so, activate the AI Tools bundle base environment. 
-If you used the default location to install AI Tools, open a terminal and type the following
+
+### AI Tools Offline Installer (Validated)
+
+#### 1. Register Conda kernel to Jupyter Notebook kernel
+
+> **Note**: If you have done this step before, skip it.
+
+If the default path is used during the installation of AI Tools:
+
 ```
-source $HOME/intel/oneapi/intelpython/bin/activate
+$HOME/intel/oneapi/intelpython/envs/<offline-conda-env-name>/bin/python -m ipykernel install --user --name=tensorflow
 ```
-If you used a separate location, open a terminal and type the following
+
+If a non-default path is used:
+
 ```
-source <custom_path>/bin/activate
+<custom_path>/bin/python -m ipykernel install --user --name=tensorflow
 ```
 
-### Active Conda Environment
+#### 2. Launch Jupyter Notebook
 
-1. Ensure you activate the conda environment.
-   ```
-   source /opt/intel/oneapi/setvars.sh
-   conda activate tensorflow
-   ```
-   or
-   ```
-   conda activate usr_tensorflow
-   ```
-2. Change to the sample directory.
+- Option A: Launch Jupyter Notebook.
 
-### Run the Notebook
-1. Launch Jupyter Notebook.
    ```
    jupyter notebook --ip=0.0.0.0
    ```
-2. Alternatively, you can launch Jupyter Notebook by running the script located in the sample code directory.
+
+- Option B: You can launch Jupyter Notebook by running the script located in the sample code directory.
+
 	```
 	./run_jupyter.sh
 	```
-	The Jupyter Server shows the URLs of the web application in your terminal.
+
+The Jupyter Server shows the URLs of the web application in your terminal.
 
 	```
 	(tensorflow) xxx@yyy:$ [I 09:48:12.622 NotebookApp] Serving notebooks from local directory:
@@ -216,15 +171,91 @@ source <custom_path>/bin/activate
 	[IPKernelApp] ERROR | No such comm target registered: jupyter.widget.version
 
 	```
-   In a web browser, open the link that the Jupyter server displayed when you started it. For example:
-   **http://yyy:8888/?token=146761d9317552c43e0d6b8b6b9e1108053d465f6ca32fca**.
 
-3. Locate and select the Notebook.
+#### 3. Follow the instructions to open the URL with the token in your browser
+
+#### 4. Select the Notebook
+
+```
+inc_sample_tensorflow.ipynb
+```
+
+#### 5. Change the kernel to `tensorflow`
+
+#### 6. Run every cell in the Notebook in sequence
+
+### Conda/PIP
+
+> **Note**: Before running the instructions below, make sure your Conda/Python environment with AI Tools installed is activated
+
+#### 1. Register Conda/Python kernel to Jupyter Notebook kernel
+
+> **Note**: If you have done this step before, skip it.
+
+For Conda:
+```
+<CONDA_PATH_TO_ENV>/bin/python -m ipykernel install --user --name=tensorflow
+```
+
+To know <CONDA_PATH_TO_ENV>, run conda env list and find your Conda environment path.
+
+For PIP:
+
+```
+python -m ipykernel install --user --name=tensorflow
+```
+
+#### 2. Launch Jupyter Notebook
+
+
+- Option A: Launch Jupyter Notebook.
+
    ```
-   inc_sample_tensorflow.ipynb
+   jupyter notebook --ip=0.0.0.0
    ```
-4. Change the kernel to **user_tensorflow**.
-5. Run every cell in the Notebook in sequence.
+
+- Option B: You can launch Jupyter Notebook by running the script located in the sample code directory.
+
+	```
+	./run_jupyter.sh
+	```
+
+The Jupyter Server shows the URLs of the web application in your terminal.
+
+	```
+	(tensorflow) xxx@yyy:$ [I 09:48:12.622 NotebookApp] Serving notebooks from local directory:
+	...
+	[I 09:48:12.622 NotebookApp] Jupyter Notebook 6.1.4 is running at:
+	[I 09:48:12.622 NotebookApp] http://yyy:8888/?token=146761d9317552c43e0d6b8b6b9e1108053d465f6ca32fca
+	[I 09:48:12.622 NotebookApp]  or http://127.0.0.1:8888/?token=146761d9317552c43e0d6b8b6b9e1108053d465f6ca32fca
+	[I 09:48:12.622 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
+	[C 09:48:12.625 NotebookApp]
+
+		To access the notebook, open this file in a browser:
+			...
+		Or copy and paste one of these URLs:
+			http://yyy:8888/?token=146761d9317552c43e0d6b8b6b9e1108053d465f6ca32fca
+		 or http://127.0.0.1:8888/?token=146761d9317552c43e0d6b8b6b9e1108053d465f6ca32fca
+	[I 09:48:26.128 NotebookApp] Kernel started: bc5b0e60-058b-4a4f-8bad-3f587fc080fd, name: python3
+	[IPKernelApp] ERROR | No such comm target registered: jupyter.widget.version
+
+	```
+
+#### 3. Follow the instructions to open the URL with the token in your browser
+
+#### 4. Select the Notebook
+
+```
+inc_sample_tensorflow.ipynb
+```
+
+#### 5. Change the kernel to `tensorflow`
+
+#### 6. Run every cell in the Notebook in sequence
+
+### Docker
+
+AI Tools Docker images already have Get Started samples pre-installed. Refer to [Working with Preset Containers](https://github.com/intel/ai-containers/tree/main/preset) to learn how to run the docker and samples.
 
 ## Example Output
 
@@ -263,3 +294,5 @@ for details.
 
 Third party program Licenses can be found here:
 [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt).
+
+*Other names and brands may be claimed as the property of others. [Trademarks](https://www.intel.com/content/www/us/en/legal/trademarks.html)
