Ipldt2021.4 (oneapi-src#642)

* IPLDT changes

* Update README.md

* update

update

* adding "conda deactivate"

* update json

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* CLEANUP

removing changes to json that were introduced during testing of ci,

* Update sample.json

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* round 1 changes

* pass 2

* round3

* round4

* Backing out changes, as someone else is makingextensive changes

* updates

* Revert "updates"

This reverts commit e174711.

* Revert "Backing out changes, as someone else is makingextensive changes"

This reverts commit 9b2b94c.

* update

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* Update CHANGELOGS.md

* Update README.md

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Author: JoeOster

AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression/README.md

@@ -1,5 +1,5 @@
 # `Intel Python daal4py Distributed Linear Regression Sample`
-This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the MPI library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the Intel® MPI Library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel® oneAPI AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---

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

@@ -44,7 +44,7 @@ You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi)
 
 ### Sourcing the oneAPI AI Analytics Toolkit environment variables
 
-By default, the Intel AI Analytics toolkit is installed in the `/opt/intel/oneapi` folder. The toolkit may be loaded by sourcing the `setvars.sh` script on a Linux shell. Notice the flag `--ccl-configuration=cpu_icc`. By default, the `ccl-configuration` is set to `cpu_gpu_dpcpp`. However, since we are distributing our TensorFlow workload on multiple CPU nodes, we are configuring the Horovod installation to use CPUs. 
+By default, the Intel® AI Analytics toolkit is installed in the `/opt/intel/oneapi` folder. The toolkit may be loaded by sourcing the `setvars.sh` script on a Linux shell. Notice the flag `--ccl-configuration=cpu_icc`. By default, the `ccl-configuration` is set to `cpu_gpu_dpcpp`. However, since we are distributing our TensorFlow workload on multiple CPU nodes, we are configuring the Horovod installation to use CPUs. 
 
 ```
 source /opt/intel/oneapi/setvars.sh --ccl-configuration=cpu_icc

AI-and-Analytics/Features-and-Functionality/IntelTensorFlow_ModelZoo_Inference_with_FP32_Int8/README.md

@@ -22,7 +22,7 @@ If you are running this sample on the DevCloud, skip the Pre-requirements and go
 
 ## Pre-requirements (Local or Remote Host Installation)
 
-TensorFlow* is ready for use once you finish the Intel AI Analytics Toolkit installation and have run the post installation script.
+TensorFlow* is ready for use once you finish the Intel® AI Analytics Toolkit installation and have run the post installation script.
 
 You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation and the Toolkit [Intel® oneAPI AI Analytics Toolkit Get Started Guide for Linux](https://software.intel.com/en-us/get-started-with-intel-oneapi-linux-get-started-with-the-intel-ai-analytics-toolkit) for post-installation steps and scripts.
 

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

@@ -5,8 +5,8 @@ TensorFlow* is a widely-used machine learning framework in the deep learning are
 |:---                               |:---
 | OS                                | Linux* Ubuntu* 18.04 
 | Hardware                          | Intel® Xeon® Scalable processor family or newer
-| Software                          | Intel® oneAPI AI Analytics Toolkit
-| What you will learn               | How to get started to use Intel optimization for TensorFlow*
+| Software                          | Intel® AI Analytics Toolkit
+| What you will learn               | How to get started to use Intel Optimization for TensorFlow*
 | Time to complete                  | 10 minutes
 
 ## Purpose

CHANGELOGS.md

@@ -26,8 +26,7 @@ Intel® oneAPI provides two runtime profiling tools to help you analyze your DPC
 
 1. The **Intel® FPGA Dynamic Profiler for DPC++** is a profiling tool used to collect fine-grained device side data during DPC++ kernel execution. When used within the Intel® VTune™ Profiler, some host side performance data is also collected. However, note that the VTune Profiler is not designed to collect detailed system level host-side data. 
 
-2. The **Intercept Layer for OpenCL™** is a profiling tool used to obtain detailed system-level information. 
-
+2. The **Intercept Layer for OpenCL™ Applications™** is a profiling tool used to obtain detailed system-level information. 
 This tutorial introduces the Intel® FPGA Dynamic Profiler for DPC++. To learn more about the Intercept Layer, refer to the FPGA tutorial "[Using the OpenCL Intercept Layer to Profile Designs running on the FPGA](https://github.com/oneapi-src/oneAPI-samples/blob/master/DirectProgramming/DPC%2B%2BFPGA/Tutorials/Tools/system_profiling)".
 
 #### The Intel® FPGA Dynamic Profiler for DPC++

CODESAMPLESLIST.md

@@ -10,8 +10,8 @@ It illustrates two OpenMP* directives to help speed up the code.
 | Optimized for                     | Description
 |:---                               |:---
 | OS                                | macOS* with Xcode* installed 
-| Software                          | Intel® oneAPI Intel Fortran Compiler
-| What you will learn               | How to build and run a Fortran OpenMP application using Intel Fortran compiler
+| Software                          | Intel® Fortran Compiler
+| What you will learn               | How to build and run a Fortran OpenMP application using Intel Fortran Compiler
 | Time to complete                  | 10 minutes
 
 ## Purpose

DirectProgramming/DPC++FPGA/Tutorials/Tools/dynamic_profiler/README.md

@@ -7,14 +7,14 @@ serial version and the version that was compiled with the auto-vectorizer.
 | Optimized for                     | Description
 |:---                               |:---
 | OS                                | macOS* with Xcode* installed 
-| Hardware							   | Intel-based Mac*
-| Software                          | Intel® oneAPI Intel Fortran Compiler
-| What you will learn               | Vectorization using Intel Fortran compiler
+| Hardware							            | Intel®-based Mac*
+| Software                          | Intel® Fortran Compiler
+| What you will learn               | Vectorization using Intel Fortran Compiler
 | Time to complete                  | 15 minutes
 
 
 ## Purpose
-The Intel® Compiler has an auto-vectorizer that detects operations in the application 
+The Intel® Fortran Compiler has an auto-vectorizer that detects operations in the application 
 that can be done in parallel and converts sequential operations 
 to parallel operations by using the 
 Single Instruction Multiple Data (SIMD) instruction set.

DirectProgramming/Fortran/CombinationalLogic/openmp-primes/README.md

@@ -2,7 +2,7 @@
 *This sample and any necessary extra files/data needed to run it are already located in the [AI-and-Analytics](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics) folder of this repository. Please go to the [IntelPython_daal4py_DistributedKMeans](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans)  folder within the AI-and-Analytics folder to get everything you need to build and run this sample.*
 
 # `Intel Python daal4py Distributed K-Means` Sample 
-This sample code shows how to train and predict with a distributed k-means model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the MPI library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code shows how to train and predict with a distributed k-means model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the Intel® MPI Library installed, and it demonstrates how to use software products that can be found in the [Intel® oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel® AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -22,7 +22,11 @@ In this sample, you will run a distributed K-Means model with oneDAL daal4py lib
 This distributed K-means sample code is implemented for CPU using the Python language. The example assumes you have daal4py and scikit-learn installed inside a conda environment, similar to what is delivered with the installation of the Intel® Distribution for Python as part of the [oneAPI AI Analytics Toolkit powered by oneAPI](https://software.intel.com/en-us/oneapi/ai-kit). 
 
 ## Additional Requirements
+<<<<<<< HEAD
+You will need a working Intel® MPI library, which is included in the [Intel® oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
+=======
 You will need a working MPI library. We recommend to use Intel&reg; MPI, which is included in the [oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
+>>>>>>> parent of 5fa54cb (round 1 changes)
  
 ## License  
 Code samples are licensed under the MIT license. See

DirectProgramming/Fortran/DenseLinearAlgebra/vectorize-vecmatmult/README.md

@@ -1,7 +1,7 @@
 *This sample and any necessary extra files/data needed to run it are already located in the [AI-and-Analytics](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics) folder of this repository. Please go to the [IntelPython_daal4py_DistributedLinearRegression](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression)  folder within the AI-and-Analytics folder to get everything you need to build and run this sample.*
 
 # `Intel Python daal4py Distributed Linear Regression` Sample
-This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the MPI library installed, and it demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This sample code shows how to train and predict with a distributed linear regression model using the python API package daal4py for oneAPI Data Analytics Library. It assumes you have a working version of the Intel® MPI Library installed, and it demonstrates how to use software products that can be found in the [Intel® oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---
@@ -22,7 +22,11 @@ This distributed linear regression sample code is implemented for the CPU using
 
 
 ## Additional Requirements
+<<<<<<< HEAD
+You will need a working Intel® MPI Library, which is included in the [Intel® oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
+=======
 You will need a working MPI library. We recommend to use Intel(R) MPI, which is included in the [oneAPI HPC Toolkit](https://software.intel.com/en-us/oneapi/hpc-kit).
+>>>>>>> parent of 5fa54cb (round 1 changes)
   
 ## License  
 Code samples are licensed under the MIT license. See

Libraries/oneDAL/IntelPython_daal4py_Distributed_Kmeans/README.md

@@ -3,7 +3,7 @@
 
 # `Intel Python daal4py Getting Started` Sample
 
-This Getting Started sample code show how to do batch linear regression using the python API package daal4py from oneDAL. It demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or the [Intel AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
+This Getting Started sample code show how to do batch linear regression using the python API package daal4py from oneDAL. It demonstrates how to use software products that can be found in the [Intel oneAPI Data Analytics Library](https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onedal.html) or the [Intel® AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). 
 
 | Optimized for                     | Description
 | :---                              | :---

Libraries/oneDAL/IntelPython_daal4py_Distributed_LinearRegression/README.md

@@ -86,7 +86,7 @@ Example: If running a sample in the Intel DevCloud, remember that you must speci
 1. Setup oneAPI environment variables:
     > Windows: C:\Program Files(x86)\Intel\oneAPI\setvars.bat
     This will need to be run each time you open a new cmd window(non Persistent)
-    - Aternatively you can search for the oneAPI cmd prompt - startmenu> look for Intel oneAPI 202*> "Intel oneAPI command prompt for Intel 64 for Visual Studio 2017"  
+    - Aternatively you can search for the oneAPI cmd prompt - startmenu> look for `Intel oneAPI 202*`> "Intel oneAPI command prompt for Intel 64 for Visual Studio 2017"  
 
     On Windows: