Merge pull request #2466 from oneapi-src/release/2024.2_AITools

README updates for 2024.2 AI Tools release

AI-and-Analytics/Getting-Started-Samples/Intel_oneCCL_Bindings_For_PyTorch_GettingStarted/README.md

@@ -2,8 +2,9 @@
 
 The oneAPI Collective Communications Library Bindings for PyTorch* (oneCCL Bindings for PyTorch*) holds PyTorch bindings maintained by Intel for the Intel® oneAPI Collective Communications Library (oneCCL).
 
-| Area                  | Description
+| Property              | Description
 |:---                   |:---
+| Category              | Getting Started
 | What you will learn   | How to get started with oneCCL Bindings for PyTorch*
 | Time to complete      | 60 minutes
 
@@ -34,39 +35,107 @@ The Jupyter Notebook also demonstrates how to change PyTorch* distributed worklo
 >- [Intel® oneCCL Bindings for PyTorch*](https://github.com/intel/torch-ccl) 
 >- [Distributed Training with oneCCL in PyTorch*](https://github.com/intel/optimized-models/tree/master/pytorch/distributed)
 
+## Environment Setup
+You will need to download and install the following toolkits, tools, and components to use the sample.
+<!-- Use numbered steps instead of subheadings -->
 
-## Run the `oneCCL Bindings for PyTorch* Getting Started` Sample
+**1. Get AI Tools**
 
-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)
-* [Docker](#docker)
+Required AI Tools:  Intel® Extension for PyTorch* - (CPU or GPU) 
+
+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.
+
+>**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.
+
+**2. (Offline Installer) Activate the AI Tools bundle base environment**
 
-### 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
+If the default path is used during the installation of AI Tools:
 ```
 source $HOME/intel/oneapi/intelpython/bin/activate
 ```
-If you used a separate location, open a terminal and type the following
+If a non-default path is used:
 ```
 source <custom_path>/bin/activate
 ```
-2. Clone the GitHub repository and install required packages:
+
+**3. (Offline Installer) Activate relevant Conda environment** 
+
+For CPU
+```
+conda activate pytorch  
+``` 
+For GPU
+```
+conda activate pytorch-gpu
+```
+
+**4. Clone the GitHub repository**
+
 ``` 
 git clone https://github.com/oneapi-src/oneAPI-samples.git
-cd oneAPI-samples/AI-and-Analytics/Getting-Started-Samples/Intel_oneCCL_Bindings_For_PyTorch_GettingStarted/
+cd oneAPI-samples/AI-and-Analytics/Getting-Started-Samples/Intel_oneCCL_Bindings_For_PyTorch_GettingStarted
+```
+
+**5. Install dependencies**
+
+>**Note**: Before running the following commands, make sure your Conda/Python environment with AI Tools installed is activated
+
+```
 pip install -r requirements.txt
+pip install notebook
+``` 
+For Jupyter Notebook, refer to [Installing Jupyter](https://jupyter.org/install) for detailed installation instructions.
+
+## Run the Sample
+>**Note**: Before running the sample, make sure [Environment Setup](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/INC-Quantization-Sample-for-PyTorch#environment-setup) is completed.
+
+Go to the section which corresponds to the installation method chosen in [AI Tools Selector](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-tools-selector.html) to see relevant instructions:
+* [AI Tools Offline Installer (Validated)](#ai-tools-offline-installer-validated)
+* [Docker](#docker)
+
+### AI Tools Offline Installer (Validated) 
+
+**1. Register Conda kernel to Jupyter Notebook kernel**
+
+**For CPU**
+
+If the default path is used during the installation of AI Tools:
+
 ```
-3. Launch Jupyter Notebook.
+$HOME/intel/oneapi/intelpython/envs/pytorch/bin/python -m ipykernel install --user --name=pytorch
+```
+
+If a non-default path is used:
+```
+<custom_path>/bin/python -m ipykernel install --user --name=pytorch
+```
+
+**For GPU**
+
+If the default path is used during the installation of AI Tools:
+
+```
+$HOME/intel/oneapi/intelpython/envs/pytorch-gpu/bin/python -m ipykernel install --user --name=pytorch-gpu
+```
+
+If a non-default path is used:
+```
+<custom_path>/bin/python -m ipykernel install --user --name=pytorch-gpu
+```
+**2. Launch Jupyter Notebook.**
 ```
 jupyter notebook --ip=0.0.0.0 --port 8888 --allow-root
 ```
-4. Follow the instructions to open the URL with the token in your browser.
-5. Locate and select the Notebook.
+**3. Follow the instructions to open the URL with the token in your browser.**
+
+**4. Select the Notebook.**
      ```
      oneCCL_Bindings_GettingStarted.ipynb
      ```
-6. Change your Jupyter Notebook kernel to **PyTorch** or **PyTorch-GPU**.
-7. Run every cell in the Notebook in sequence.
+
+**5. Change kernel to ``pytorch`` or ``pytorch-gpu``.**
+
+**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.
@@ -77,3 +146,5 @@ Code samples are licensed under the MIT license. See
 [License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) 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)

AI-and-Analytics/Getting-Started-Samples/README.md

@@ -23,7 +23,7 @@ Third party program Licenses can be found here: [third-party-programs.txt](https
 |Classical Machine Learning| Intel® Optimization for XGBoost* | [IntelPython_XGBoost_GettingStarted](IntelPython_XGBoost_GettingStarted)                     | Set up and trains an XGBoost* model on datasets for prediction.
 |Classical Machine Learning| daal4py | [IntelPython_daal4py_GettingStarted](IntelPython_daal4py_GettingStarted)                     | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL).
 |Deep Learning <br/> Inference Optimization| Intel® Optimization for TensorFlow* | [IntelTensorFlow_GettingStarted](IntelTensorFlow_GettingStarted)               | A simple training example for TensorFlow.
-|Deep Learning <br/> Inference Optimization|Intel® Extension of PyTorch | [IntelPyTorch_GettingStarted](Intel_Extension_For_PyTorch_GettingStarted) | A simple training example for Intel® Extension of PyTorch.
+|Deep Learning <br/> Inference Optimization|Intel® Extension of PyTorch | [IntelPyTorch_GettingStarted]([https://github.com/intel/intel-extension-for-pytorch/tree/main/examples/cpu/inference/python/jupyter-notebooks](https://github.com/intel/intel-extension-for-pytorch/blob/main/examples/cpu/inference/python/jupyter-notebooks/IPEX_Getting_Started.ipynb)| A simple training example for Intel® Extension of PyTorch.
 |Classical Machine Learning| Scikit-learn (OneDAL) | [Intel_Extension_For_SKLearn_GettingStarted](Intel_Extension_For_SKLearn_GettingStarted) | Speed up a scikit-learn application using Intel oneDAL.
 |Deep Learning <br/> Inference Optimization|Intel® Extension of TensorFlow | [Intel® Extension For TensorFlow GettingStarted](Intel_Extension_For_TensorFlow_GettingStarted)         | Guides users how to run a TensorFlow inference workload on both GPU and CPU.
 |Deep Learning Inference Optimization|oneCCL Bindings for PyTorch | [Intel oneCCL Bindings For PyTorch GettingStarted](Intel_oneCCL_Bindings_For_PyTorch_GettingStarted)         | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. |