update automation docs and tpp file (oneapi-src#881)

Signed-off-by: JoeOster <[email protected]>

.repo-tools/Docs_Automation/create_docs.py

@@ -7,7 +7,7 @@
 
 today = date.today()
 d = today.strftime("%B %d, %Y")
-currentVersion = "2022.1.0"
+currentVersion = "2022.2.0"# this should be moved to content.json, possibly add as cmd line argument
 fCodeSamplesLists = "CODESAMPLESLIST.md"
 fChangeLogs = "CHANGELOGS.md"
 freadme = "README.md"

.repo-tools/Docs_Automation/guids.json

@@ -1215,14 +1215,15 @@
     },
     "BDC6B80E-E764-409D-966B-662CF7EFB072": {
         "guid": "BDC6B80E-E764-409D-966B-662CF7EFB072",
-	       "ver": "2022.1.0",
-	       "name": "Intel oneAPI Rendering Toolkit ISPC Getting Started: 05_ispc_gsg"
+	    "ver": "2022.2.0",
+	    "name": "Intel oneAPI Rendering Toolkit ISPC Getting Started: 05_ispc_gsg",
+        "removed": "False"
     },
     "1F8590F3-FA2E-4246-92E4-C1848E9A768E": {
         "guid": "1F8590F3-FA2E-4246-92E4-C1848E9A768E",
         "name": "Jacobi Iterative",
         "notes": "-",
         "removed": "False",
-        "ver": "2021.1.Gold"
+        "ver": "2022.2.0"
     }
 }

CHANGELOGS.md

@@ -3,6 +3,8 @@
 This document shows the history of when a specific sample was introduced to the oneAPI ecosystem of Code Samples.
 | Version|Code Sample Name|description|
 |-----------------------|------------------|-------------------------|
+|2022.2.0|[Intel Implicit SPMD Program Compiler (Intel ISPC) Getting Started: 05_ispc_gsg](https://github.com/oneapi-src/oneAPI-samples/tree/master/RenderingToolkit/GettingStarted/05_ispc_gsg)|This introductory rendering toolkit sample demonstrates how to compile basic programs with Intel ISPC and the system C++ compiler. Use this sample to further explore developing accelerated applications with Intel Embree and Intel Open VKL.|
+|2022.2.0|[Jacobi Iterative](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++/DenseLinearAlgebra/jacobi_iterative)|Calculates the number of iterations needed to solve system of linear equations using Jacobi Iterative method|
 |2022.1.0|[AC Int](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/Features/ac_int)|An Intel® FPGA tutorial demonstrating how to use the Algorithmic C Integer (AC Int) |
 |2022.1.0|[Adaptive Noise Reduction](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/ReferenceDesigns/anr)|A highly optimized adaptive noise reduction (ANR) algorithm on an FPGA.|
 |2022.1.0|[Autorun kernels](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/DesignPatterns/autorun)|Intel® FPGA tutorial demonstrating autorun kernels|
@@ -38,12 +40,12 @@ This document shows the history of when a specific sample was introduced to the
 |2021.4.0|[Intel Open Image Denoise Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/RenderingToolkit/GettingStarted/04_oidn_gsg)|This introductory 'hello rendering toolkit' sample program demonstrates how to denoise a raytraced image with Intel Open Image Denoise|
 |2021.4.0|[Intel Open VKL Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/RenderingToolkit/GettingStarted/03_openvkl_gsg)|This introductory hello rendering toolkit sample program demonstrates how to sample into volumes with Intel Open VKL|
 |2021.4.0|[Intel OSPRay Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/RenderingToolkit/GettingStarted/01_ospray_gsg)|This introductory 'hello rendering toolkit' sample program demonstrates how to render triangle data with the pathtracer from Intel OSPRay|
-|2021.4.0|[Intel(R) Extension for Scikit-learn: SVC for Adult dataset](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelScikitLearn_Extensions_SVC_Adult)|Use Intel(R) Extension for Scikit-learn to accelerate the training and prediction with SVC algorithm on Adult dataset. Compare the performance of SVC algorithm optimized through Intel(R) Extension for Scikit-learn against original Scikit-learn.|
+|2021.4.0|[Intel(R) Extension for Scikit-learn: SVC for Adult dataset](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/Intel_Extension_For_SKLearn_Performance_SVC_Adult)|Use Intel(R) Extension for Scikit-learn to accelerate the training and prediction with SVC algorithm on Adult dataset. Compare the performance of SVC algorithm optimized through Intel(R) Extension for Scikit-learn against original Scikit-learn.|
 |2021.4.0|[Intel® Python Scikit-learn Extension Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/Intel_Extension_For_SKLearn_GettingStarted)|This sample illustrates how to do Image classification using SVM classifier from Python API package SKlearnex with the use of Intel® oneAPI Data Analytics Library (oneDAL).|
 |2021.4.0|[Merge Sort](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/ReferenceDesigns/merge_sort)|A Reference design demonstrating merge sort on an Intel® FPGA|
 |2021.4.0|[Private Copies](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/Features/private_copies)|An Intel® FPGA tutorial demonstrating how to use the private_copies attribute to trade off the resource use and the throughput of a DPC++ FPGA program|
 |2021.4.0|[Stall Enable](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/Features/stall_enable)|An Intel® FPGA tutorial demonstrating the use_stall_enable_clusters attribute|
-|2021.3.0|[Intel® Python XGBoost Performance](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance)|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 |
+|2021.3.0|[Intel® Python XGBoost Performance](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_Performance)|This sample code illustrates how to analyze the performance benefit from using Intel training optimizations upstreamed by Intel to latest XGBoost compared to un-optimized XGBoost 0.81 |
 |2021.3.0|[IO streaming with DPC++ IO pipes](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/DesignPatterns/io_streaming)|An FPGA tutorial describing how to stream data to and from DPC++ IO pipes.|
 |2021.3.0|[Jacobi](https://github.com/oneapi-src/oneAPI-samples/tree/master/Tools/ApplicationDebugger/jacobi)|A small Data Parallel C++ (DPC++) example which solves a harcoded linear system with Jacobi iteration. The sample includes two versions of the same program: with and without bugs.|
 |2021.3.0|[Loop Initiation Interval](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/Features/loop_initiation_interval)|An Intel® FPGA tutorial demonstrating the usage of the initiation_interval attribute|
@@ -101,10 +103,10 @@ This document shows the history of when a specific sample was introduced to the
 |2021.1.Gold|[IBM Device](https://github.com/oneapi-src/oneAPI-samples/tree/master/Tools/IoTConnectionTools/ibm-device)|This project shows how-to develop a device code using Watson IoT Platform iot-c device client library, connect and interact with Watson IoT Platform Service|
 |2021.1.Gold|[Intel® Neural Compressor Tensorflow Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/INC-Sample-for-Tensorflow)|This sample illustrates how to run Intel® Neural Compressor to quantize the FP32 model trained by Keras on Tensorflow to INT8 model to speed up the inference.|
 |2021.1.Gold|[Intel® Modin Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelModin_GettingStarted)|This sample illustrates how to use Modin accelerated Pandas functions and notes the performance gain when compared to standard Pandas functions|
-|2021.1.Gold|[Intel® Python Daal4py Distributed K-Means](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans)|This sample code illustrates how to train and predict with a distributed K-Means model with the Intel® Distribution of Python using the Python API package Daal4py for Intel® oneDAL|
-|2021.1.Gold|[Intel® Python Daal4py Distributed Linear Regression](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression)|This sample code illustrates how to train and predict with a Distributed Linear Regression model with the Intel® Distribution of Python using the Python API package Daal4py for Intel® oneDAL|
-|2021.1.Gold|[Intel® Python Daal4py Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelPython_daal4py_GettingStarted)|This sample illustrates how to do Batch Linear Regression using the Python API package Daal4py for Intel® oneDAL|
-|2021.1.Gold|[Intel® Python XGBoost Daal4py Prediction](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction)|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|
+|2021.1.Gold|[Intel® Python Daal4py Distributed K-Means](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans)|This sample code illustrates how to train and predict with a distributed K-Means model with the Intel® Distribution of Python using the Python API package Daal4py powered by Intel® oneDAL|
+|2021.1.Gold|[Intel® Python Daal4py Distributed Linear Regression](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedLinearRegression)|This sample code illustrates how to train and predict with a Distributed Linear Regression model with the Intel® Distribution of Python using the Python API package Daal4py powered by Intel® oneDAL|
+|2021.1.Gold|[Intel® Python Daal4py Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelPython_daal4py_GettingStarted)|This sample illustrates how to do Batch Linear Regression using the Python API package Daal4py powered by Intel® oneDAL|
+|2021.1.Gold|[Intel® Python XGBoost Daal4py Prediction](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Features-and-Functionality/IntelPython_XGBoost_daal4pyPrediction)|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|
 |2021.1.Gold|[Intel® Python XGBoost Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelPython_XGBoost_GettingStarted)|The sample illustrates how to setup and train an XGBoost model on datasets for prediction|
 |2021.1.Gold|[Intel® PyTorch Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelPyTorch_GettingStarted)|This sample illustrates how to train a PyTorch model and run inference with Intel® oneMKL and Intel® oneDNN|
 |2021.1.Gold|[Intel® Tensorflow Getting Started](https://github.com/oneapi-src/oneAPI-samples/tree/master/AI-and-Analytics/Getting-Started-Samples/IntelTensorFlow_GettingStarted)|This sample illustrates how to train a TensorFlow model and run inference with oneMKL and oneDNN.|
@@ -168,6 +170,6 @@ This document shows the history of when a specific sample was introduced to the
 |2021.1.Gold|[Vectorize VecMatMult](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/Fortran/DenseLinearAlgebra/vectorize-vecmatmult)|Fortran Tutorial - Using Auto Vectorization|
 |2021.1.Gold|[AWS Pub Sub](https://github.com/oneapi-src/oneAPI-samples/tree/master/Tools/IoTConnectionTools/aws-pub-sub)|This sample uses the Message Broker for AWS* IoT to send and receive messages through an MQTT connection|
 |2021.1.Gold|[Zero Copy Data Transfer](https://github.com/oneapi-src/oneAPI-samples/tree/master/DirectProgramming/DPC++FPGA/Tutorials/DesignPatterns/zero_copy_data_transfer)|An Intel® FPGA tutorial demonstrating zero-copy host memory using the SYCL restricted Unified Shared Memory (USM) model|
-Total Samples: 165
+Total Samples: 167
 
-Report Generated on:  March 04, 2022
+Report Generated on:  March 08, 2022