Bdmoore1 add vscode 0928 (oneapi-src#683)

* Added VS Code to vector-add

* Added terminal instructions

Added a overview of the steps to downloading and building a sample. Steps are focused on using the VS Code extensions.

* Add VS Code instructions

In most of the sample README files, I added instructions for how to learn more about using VS Code. If the sample seemed too complicated for VS Code (i.e., setting conda env or building folder trees), or the sample was not in the oneapi samples browser extension, then I did not include the VS Code instructions.

Author: bdmoore1

AI-and-Analytics/End-to-end-Workloads/LidarObjectDetection-PointPillars/README.md

@@ -19,7 +19,7 @@ PointPillars is an AI algorithm that uses LIDAR point clouds to detect and class
 3. Afterward, the pre-processed data is used by a so-called Pillar Feature Extraction (PFE) CNN to create a 2D image-like representation of the sensor environment. For the inference, this sample uses the Intel® Distribution of OpenVINO™ toolkit. The output of this CNN is a list of dense tensors (learned pillar features).
 4. To convert these dense tensors into an pseudo-image, a scatter operation is performed. This operation is again realized with SYCL and DPCPP.
 5. This pseudo-image is consumed by the second CNN, the so-called Region Proposal Network (RPN). The inference is performed with the help of the Intel® Distribution of OpenVINO™ toolkit. The output is an unfiltered list of possible object detections, their position, dimensions and classifications.
-6. Finally, this output data (object list) is post-processed with the help of the anchors created in the 2nd step. The anchors are used to decode the object position, dimension and class. Afterwards, a Non-Maximum-Suppression (NMS) is used to filter out redundant/clutter objects. Finally, the objects are sorted according to their likelihood, and then provided as output. All of these steps are implemented as SYCL and DPCPP kernels. 
+6. Finally, this output data (object list) is post-processed with the help of the anchors created in the 2nd step. The anchors are used to decode the object position, dimension and class. Afterwards, a Non-Maximum-Suppression (NMS) is used to filter out redundant/clutter objects. Finally, the objects are sorted according to their likelihood, and then provided as output. All of these steps are implemented as SYCL and DPCPP kernels.
 
 By default, the application will use 'host' as the execution device for SYCL/DPCPP kernels and CPU (single-threaded) for Intel® Distribution of OpenVINO™ toolkit inferencing part. The execution device and the inferencing device are displayed in the output, along with the elapsed time of each of the five steps described above. For more details refer to section: [Execution Options for the Sample Program](#execution-options-for-the-sample-program).
 
@@ -30,7 +30,7 @@ This sample demonstrates a real-world, end-to-end example that uses a combinatio
  - You will learn how to implement oneAPI-based function kernels that can be executed on the host system, on a multi-threaded CPU or a GPU.
  - You will learn how to implement standard algorithms for AI-based object detection, for example, _Non-Maximum-Suppression_, using oneAPI.
 
-## License  
+## License
 Code samples are licensed under the MIT license. See
 [License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
 
@@ -49,17 +49,32 @@ To build and run the PointPillars sample, the following libraries have to be ins
 3. Boost (including `boost::program_options` and `boost::filesystem` library). For Ubuntu, you may install the libboost-all-dev package.
 4. Optional: If the sample should be run on an Intel GPU, it might be necessary to upgrade the corresponding drivers. Therefore, please consult the following page: https://github.com/intel/compute-runtime/releases/
 
+### Using Visual Studio Code*  (VS Code)
+
+You can use VS Code extensions to set your environment, create launch configurations,
+and browse and download samples.
+
+The basic steps to build and run a sample using VS Code include:
+ - Download a sample using the extension **Code Sample Browser for Intel oneAPI Toolkits**.
+ - Configure the oneAPI environment with the extension **Environment Configurator for Intel oneAPI Toolkits**.
+ - Open a Terminal in VS Code (**Terminal>New Terminal**).
+ - Run the sample in the VS Code terminal using the instructions below.
+
+To learn more about the extensions and how to configure the oneAPI environment, see
+[Using Visual Studio Code with Intel® oneAPI Toolkits](https://software.intel.com/content/www/us/en/develop/documentation/using-vs-code-with-intel-oneapi/top.html).
+
+After learning how to use the extensions for Intel oneAPI Toolkits, return to this readme for instructions on how to build and run a sample.
 
 ### Build process (Local or Remote Host Installation)
 Perform the following steps:
 1. Prepare the environment to be able to use the Intel® Distribution of OpenVINO™ toolkit and oneAPI
-``` 
+```
 $ source /opt/intel/openvino_2021/bin/setupvars.sh
 $ source /opt/intel/oneapi/setvars.sh
 ```
 
-2. Build the program using the following `cmake` commands. 
-``` 
+2. Build the program using the following `cmake` commands.
+```
 $ mkdir build && cd build
 $ cmake ..
 $ make
@@ -134,8 +149,8 @@ In order to run on the DevCloud, you need to request a compute node using node p
 | FPGA Runtime      | qsub -l nodes=1:fpga\_runtime:ppn=2 -d . hello-world.sh |
 
 ### Build process (DevCloud)
-1. Build the program using the following `cmake` commands. 
-``` 
+1. Build the program using the following `cmake` commands.
+```
 $ mkdir build && cd build
 $ cmake ..
 $ make

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

@@ -1,5 +1,5 @@
 # `Intel® Python XGBoost Performance Sample`
-This sample code illustrates how to analyze the performance benefit from using Intel optimizations upstreamed by Intel to latest XGBoost compared to un-optimized XGBoost 0.81. It demonstrates how to use software products that can be found in the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code illustrates how to analyze the performance benefit from using Intel optimizations upstreamed by Intel to latest XGBoost compared to un-optimized XGBoost 0.81. It demonstrates how to use software products that can be found in the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -14,11 +14,11 @@ This sample code illustrates how to analyze the performance benefit from using I
 XGBoost is a widely used gradient boosting library in the classical ML area. Designed for flexibility, performance, and portability, XGBoost includes optimized distributed gradient boosting frameworks and implements Machine Learning algorithms underneath.
 
 In this sample, you will an XGBoost model and prediction using Intel optimizations upstreamed by Intel to the latest XGBoost package and the un-optimized XGBoost 0.81 for comparison.
-  
-## Key Implementation Details 
+
+## Key Implementation Details
 This XGBoost sample code is implemented for the CPU using the Python language. The example assumes you XGBoost installed inside a conda environment, similar to what is delivered with the installation of the Intel® Distribution for Python* as part of the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/en-us/oneapi/ai-kit). It also assumes you have set up an additional XGBoost 0.81 conda environment, with details on how to do so explained within the sample and this README.
- 
-## License  
+
+## License
 Code samples are licensed under the MIT license. See
 [License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
 
@@ -34,7 +34,7 @@ You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi)
 
 ### Activate conda environment With Root Access
 
-Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
+Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
 
 Intel Python environment will be active by default. However, if you activated another environment, you can return with the following command:
 
@@ -100,9 +100,24 @@ Run the Program
 
 `python IntelPython_XGBoost_Performance.py`
 
-The output files of the script will be saved in the included models and result directories. 
+The output files of the script will be saved in the included models and result directories.
 
 ##### Expected Printed Output (with similar numbers):
 ```
 [CODE_SAMPLE_COMPLETED_SUCCESFULLY]
 ```
+### Using Visual Studio Code*  (VS Code)
+
+You can use VS Code extensions to set your environment, create launch configurations,
+and browse and download samples.
+
+The basic steps to build and run a sample using VS Code include:
+ - Download a sample using the extension **Code Sample Browser for Intel oneAPI Toolkits**.
+ - Configure the oneAPI environment with the extension **Environment Configurator for Intel oneAPI Toolkits**.
+ - Open a Terminal in VS Code (**Terminal>New Terminal**).
+ - Run the sample in the VS Code terminal using the instructions below.
+
+To learn more about the extensions and how to configure the oneAPI environment, see
+[Using Visual Studio Code with Intel® oneAPI Toolkits](https://software.intel.com/content/www/us/en/develop/documentation/using-vs-code-with-intel-oneapi/top.html).
+
+After learning how to use the extensions for Intel oneAPI Toolkits, return to this readme for instructions on how to build and run a sample.

AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction/README.md

@@ -1,5 +1,5 @@
 # `Intel® Python XGBoost daal4py Prediction Sample`
-This sample code illustrates how to analyze the performance benefit of minimal code changes to port pre-trained XGBoost model to daal4py prediction for much faster prediction than XGBoost prediction. It demonstrates how to use software products that can be found in the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code illustrates how to analyze the performance benefit of minimal code changes to port pre-trained XGBoost model to daal4py prediction for much faster prediction than XGBoost prediction. It demonstrates how to use software products that can be found in the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -16,11 +16,11 @@ XGBoost is a widely used gradient boosting library in the classical ML area. Des
 This sample code illustrates how to analyze the performance benefit of minimal code changes to port pre-trained XGBoost models to daal4py prediction for much faster prediction than XGBoost prediction.
 
 In this sample, you will run an XGBoost model with daal4py prediction and XGBoost API prediction to see the performance benefit of daal4py gradient boosting prediction. You will also learn how to port a pre-trained XGBoost model to daal4py prediction.
-  
-## Key Implementation Details 
-This sample code is implemented for CPU using the Python language. The example assumes you have XGboost and daal4py installed inside a conda environment, similar to what is delivered with the installation of the Intel® Distribution for Python* as part of the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/en-us/oneapi/ai-kit).  
- 
-## License  
+
+## Key Implementation Details
+This sample code is implemented for CPU using the Python language. The example assumes you have XGboost and daal4py installed inside a conda environment, similar to what is delivered with the installation of the Intel® Distribution for Python* as part of the [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/en-us/oneapi/ai-kit).
+
+## License
 Code samples are licensed under the MIT license. See
 [License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
 
@@ -35,7 +35,7 @@ You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi)
 
 ### Activate conda environment With Root Access
 
-Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder. 
+Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script. Then navigate in Linux shell to your oneapi installation path, typically `/opt/intel/oneapi/` when installed as root or sudo, and `~/intel/oneapi/` when not installed as a superuser. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
 
 Intel Python environment will be active by default. However, if you activated another environment, you can return with the following command:
 
@@ -97,9 +97,24 @@ Run the Program
 
 `python IntelPython_XGBoost_Performance.py`
 
-The output files of the script will be saved in the included models and result directories. 
+The output files of the script will be saved in the included models and result directories.
 
 ##### Expected Printed Output (with similar numbers):
 ```
 [CODE_SAMPLE_COMPLETED_SUCCESFULLY]
 ```
+### Using Visual Studio Code*  (VS Code)
+
+You can use VS Code extensions to set your environment, create launch configurations,
+and browse and download samples.
+
+The basic steps to build and run a sample using VS Code include:
+ - Download a sample using the extension **Code Sample Browser for Intel oneAPI Toolkits**.
+ - Configure the oneAPI environment with the extension **Environment Configurator for Intel oneAPI Toolkits**.
+ - Open a Terminal in VS Code (**Terminal>New Terminal**).
+ - Run the sample in the VS Code terminal using the instructions below.
+
+To learn more about the extensions and how to configure the oneAPI environment, see
+[Using Visual Studio Code with Intel® oneAPI Toolkits](https://software.intel.com/content/www/us/en/develop/documentation/using-vs-code-with-intel-oneapi/top.html).
+
+After learning how to use the extensions for Intel oneAPI Toolkits, return to this readme for instructions on how to build and run a sample.