Patch 13

.github/workflows/github-pages.yml

@@ -0,0 +1,81 @@
+name: github-samples-app
+
+on:
+ push:
+  branches:
+  - master
+
+ workflow_dispatch: 
+
+#  schedule:
+#    - cron: '55 13 * * *'
+
+# Top-level default; empty/no permissions 
+permissions: {}  
+
+jobs:
+  pages:
+    name: Build GitHub Pages
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      # Above required for publishing to gh-pages; see auth on Ln 67-68
+
+    steps:
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.8"
+
+    - uses: actions/checkout@v3
+      name: Check out app/dev  # checks out app/dev in top-level dir
+      with:
+        ref: 'refs/heads/app/dev'
+
+    - uses: actions/checkout@v3
+      name: Check out master # checks out master in subdirectory
+      with:
+        ref: 'refs/heads/master'
+        path: master
+
+    - name: Build JSON DB
+      run: |
+        python3 -m pip install -r src/requirements.txt
+        echo master
+        python3 src/db.py master
+
+    - name: Remove JSON pre-prod  
+      run: |       
+        rm -rf src/docs/sample_db_pre.json
+
+    - name: Build Sphinx
+      run: |    
+        python3 -m sphinx -W -b html src/docs/ src/docs/_build/
+        echo $PWD
+        echo ${{ github.ref }}
+
+    - name: Add GPU-Occupancy-Calculator
+      env: 
+        GPU_OCC_CALC: src/docs/_build/Tools/GPU-Occupancy-Calculator/
+      run: |
+        mkdir -p ${GPU_OCC_CALC}
+        cp -v ${{ github.workspace }}/master/Tools/GPU-Occupancy-Calculator/index.html ${GPU_OCC_CALC}/index.html
+
+    - name: Push docs
+      if: ${{ github.ref == 'refs/heads/master' }} # only if this workflow is run from the master branch, push docs
+      env:
+        GITHUB_USER: ${{ github.actor }}
+        GITHUB_TOKEN: ${{ github.token }}
+        GITHUB_REPO: ${{ github.repository }} 
+      run: |
+        cd src/docs/_build/
+        touch .nojekyll       
+        git init
+        git remote add origin "https://${GITHUB_USER}:${GITHUB_TOKEN}@github.com/${GITHUB_REPO}"
+        git add -A
+        git status
+        git config --global user.name "GitHub Actions"
+        git config --global user.email "actions@github.com"
+        git commit -sm "$(date)"
+        git branch -M gh-pages
+        git push -u origin -f gh-pages

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

@@ -11,22 +11,22 @@ The `Census` sample code illustrates how to use Intel® Distribution of Modin* f
 ## Purpose
 This sample code demonstrates how to run the end-to-end census workload using the AI Toolkit without any external dependencies.
 
-Intel® Distribution of Modin* uses Ray to speed up your Pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Intel® Distribution of Modin* provides integration and compatibility with existing Pandas code. Intel® Extension for Scikit-learn* dynamically patches scikit-learn estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the underlying solver to get the solution faster.
+Intel® Distribution of Modin* uses HDK to speed up your Pandas notebooks, scripts, and libraries. Unlike other distributed DataFrame libraries, Intel® Distribution of Modin* provides integration and compatibility with existing Pandas code. Intel® Extension for Scikit-learn* dynamically patches scikit-learn estimators to use Intel® oneAPI Data Analytics Library (oneDAL) as the underlying solver to get the solution faster.
 
 ## Prerequisites
 
 | Optimized for                     | Description
 | :---                              | :---
 | OS                                | 64-bit Ubuntu* 18.04 or higher
 | Hardware                          | Intel Atom® processors <br> Intel® Core™ processor family <br> Intel® Xeon® processor family <br> Intel® Xeon® Scalable processor family
-| Software                          | Intel® AI Analytics Toolkit (AI Kit) (Python version 3.7, Intel® Distribution of Modin*) <br> Intel® Extension for Scikit-learn* <br> NumPy <br> Ray
+| Software                          | Intel® AI Analytics Toolkit (AI Kit) (Python version 3.8 or newer, Intel® Distribution of Modin*) <br> Intel® Extension for Scikit-learn* <br> NumPy
 
 The Intel® Distribution of Modin* and Intel® Extension for Scikit-learn* libraries are available together in [Intel® AI Analytics Toolkit (AI Kit)](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html).
 
 
 ## Key Implementation Details
 
-This end-to-end workload sample code is implemented for CPU using the Python language. Once you have installed AI Kit, the Conda environment is prepared with Python version 3.7 (or newer), Intel Distribution of Modin*, Ray, Intel® Extension for Scikit-Learn, and NumPy. 
+This end-to-end workload sample code is implemented for CPU using the Python language. Once you have installed AI Kit, the Conda environment is prepared with Python version 3.8 (or newer), Intel Distribution of Modin*, Intel® Extension for Scikit-Learn, and NumPy. 
 
 In this sample, you will use Intel® Distribution of Modin* to ingest and process U.S. census data from 1970 to 2010 in order to build a ridge regression-based model to find the relation between education and total income earned in the US.
 
@@ -74,23 +74,29 @@ To learn more about the extensions and how to configure the oneAPI environment,
 
 ### On Linux*
 
-1. Install the Intel® Distribution of Modin* python environment.
+1. Install the Intel® Distribution of Modin* python environment (Only python 3.8 - 3.10 are supported).
    ```
-   conda create -y -n intel-aikit-modin intel-aikit-modin -c intel
+   conda create -n modin-hdk python=3.x -y
    ```
 2. Activate the Conda environment.
    ```
-   conda activate intel-aikit-modin
+   conda activate modin-hdk
    ```
-3. Install Jupyter Notebook.
+3. Install modin-hdk, Intel® Extension for Scikit-learn* and related libraries.
    ```
-   conda install jupyter nb_conda_kernels
+   conda install modin-hdk -c conda-forge -y
+   pip install scikit-learn-intelex
+   pip install matplotlib
    ```
-4. Install OpenCensus.
+4. Install Jupyter Notebook
    ```
-   pip install opencensus
+   pip install jupyter ipykernel
    ```
-5. Change to the sample directory, and open Jupyter Notebook.
+5. Add kernel to Jupyter Notebook.
+   ```
+   python -m ipykernel install --user --name modin-hdk
+   ```
+6. Change to the sample directory, and open Jupyter Notebook.
    ```
    jupyter notebook
    ```
@@ -127,20 +133,17 @@ To learn more about the extensions and how to configure the oneAPI environment,
 2. Open a web browser, and navigate to https://devcloud.intel.com. Select **Work with oneAPI**.
 3. From Intel® DevCloud for oneAPI [Get Started](https://devcloud.intel.com/oneapi/get_started), locate the ***Connect with Jupyter* Lab*** section (near the bottom).
 4. Click **Sign in to Connect** button. (If you are already signed in, the link should say ***Launch JupyterLab****.)
-5. Once JupyterLab opens, select **no kernel**.
-6. You might need to [clone the samples](#clone-the-samples-in-intel®-devcloud) from GitHub. If the samples are already present, skip this step.
-7. Change to the sample directory.
-8. Open `census_modin.ipynb`.
-9. Click **Run** to run the cells.
-10. Alternatively, run the entire workbook by selecting **Restart kernel and re-run whole notebook**.
-
-#### Clone the Samples in Intel® DevCloud
-If the samples are not already present in your Intel® DevCloud account, download them.
-1. From JupyterLab, select **File** > **New** > **Terminal**.
-2. In the terminal, clone the samples from GitHub.
+5. Open a terminal from Launcher
+6. Follow [step 1-5](#on-linux) to create conda environment
+7. Clone the samples from GitHub. If the samples are already present, skip this step.
    ```
    git clone https://github.com/oneapi-src/oneAPI-samples.git
    ```
+8. Change to the sample directory.
+9. Open `census_modin.ipynb`.
+10. Select kernel "modin-hdk" 
+11. Click **Run** to run the cells.
+12. Alternatively, run the entire workbook by selecting **Restart kernel and re-run whole notebook**.
 
 ## Example Output
 
@@ -152,4 +155,4 @@ This is an example Cell Output for `census_modin.ipynb` run in Jupyter Notebook.
 
 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).
+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).

AI-and-Analytics/End-to-end-Workloads/Census/census_modin.ipynb

@@ -21,6 +21,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
@@ -29,7 +30,7 @@
    },
    "source": [
     "In this example we will be running an end-to-end machine learning workload with US census data from 1970 to 2010.\n",
-    "It uses Intel® Distribution of Modin with Ray as backend compute engine for ETL, and uses Ridge Regression algorithm from Intel scikit-learn-extension library to train and predict the co-relation between US total income and education levels."
+    "It uses Intel® Distribution of Modin with HDK (Heterogeneous Data Kernels) as backend compute engine for ETL, and uses Ridge Regression algorithm from Intel scikit-learn-extension library to train and predict the co-relation between US total income and education levels."
    ]
   },
   {
@@ -73,14 +74,15 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {
     "pycharm": {
      "name": "#%% md\n"
     }
    },
    "source": [
-    "Import Modin and set Ray as the compute engine. This engine uses analytical database OmniSciDB to obtain high single-node scalability for specific set of dataframe operations. "
+    "Import Modin and set HDK as the compute engine. This engine provides a set of components for federating analytic queries to an execution backend based on OmniSciDB to obtain high single-node scalability for specific set of dataframe operations. "
    ]
   },
   {
@@ -97,16 +99,7 @@
     "import modin.pandas as pd\n",
     "\n",
     "import modin.config as cfg\n",
-    "from packaging import version\n",
-    "import modin\n",
-    "\n",
-    "cfg.IsExperimental.put(\"True\")\n",
-    "cfg.Engine.put('native')\n",
-    "# Since modin 0.12.0 OmniSci engine activation process slightly changed\n",
-    "if version.parse(modin.__version__) <= version.parse('0.11.3'):\n",
-    "    cfg.Backend.put('omnisci')\n",
-    "else:\n",
-    "    cfg.StorageFormat.put('omnisci')\n"
+    "cfg.StorageFormat.put('hdk')\n"
    ]
   },
   {
@@ -288,13 +281,23 @@
     "mean MSE ± deviation: 0.032564569 ± 0.000041799\n",
     "mean COD ± deviation: 0.995367533 ± 0.000005869"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# release resources\n",
+    "%reset -f"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "modin-hdk",
    "language": "python",
-   "name": "python3"
+   "name": "modin-hdk"
   },
   "language_info": {
    "codemirror_mode": {
@@ -306,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.11"
+   "version": "3.9.16"
   }
  },
  "nbformat": 4,

AI-and-Analytics/End-to-end-Workloads/Census/sample.json

@@ -15,14 +15,16 @@
         "id": "Intel_Modin_E2E_py",
         "steps": [
           "set -e # Terminate the script on first error",
-          "source $(conda info --base)/etc/profile.d/conda.sh # Bypassing conda's disability to activate environments inside a bash script: https://github.com/conda/conda/issues/7980",
-          "conda create -y -n intel-aikit-modin intel-aikit-modin -c intel",
-          "conda activate intel-aikit-modin",
-          "conda install -y jupyter # Installing 'jupyter' for extended abilities to execute the notebook",
-          "pip install opencensus # Installing 'runipy' for extended abilities to execute the notebook",
+          "source /root/intel/oneapi/intelpython/bin/activate",
+          "conda create -n modin-hdk python=3.9 -y",
+          "conda activate modin-hdk",
+          "conda install modin-hdk -c conda-forge -y",
+          "pip install scikit-learn scikit-learn-intelex matplotlib jupyter ipykernel",
+          "python -m ipykernel install --name modin-hdk",
           "jupyter nbconvert --to notebook --execute census_modin.ipynb"
         ]
       }
     ]
-}
-}
+},
+"expertise": "Reference Designs and End to End"
+}

AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/Inference/audio_ground_truth_labels.csv

@@ -0,0 +1 @@
+Audio File,Language Code

AI-and-Analytics/End-to-end-Workloads/LanguageIdentification/Inference/clean.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+rm -R RIRS_NOISES
+rm -R tmp
+rm -R speechbrain
+rm -f rirs_noises.zip noise.csv reverb.csv vad_file.txt
+rm -f ./*.wav