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* Updating License file to no date in the title /* * Copyright (c) 2020 Intel Corporation * * This program and the accompanying materials are made available under the * terms of the The MIT License which is available at * https://opensource.org/licenses/MIT. * * SPDX-License-Identifier: MIT */ * Update README.md * Fix FPGA entries * Update README.md Updates per request of sranikonda * Update README.md * removing duplicate samples after transfering to dwarves folders * Update Makefile.win changing compiler name from "dpcpp-cl" to "dpcpp" * Update Makefile.win * Update Makefile.win.fpga * Update CMakeLists.txt * Update CMakeLists.txt * Update CMakeLists.txt * Update README.md * Update README.md * Update from Legal Approval of 10/05/2020 * Create README.md * Add files via upload * Update README.md minor modifications to content, purpose and key implementation details. * Update sample.json aligned description with readme * Update README.md reshuffled parts of the purpose and implementation details and abstracted a few key concepts into better summaries. * Update sample.json synched description with readme. * Update README.md * Fixing conflicts * ng conflicts * Create README.md * Create sample.json * Create sample.json * removing franmeworks folder * fixing a issue * Fixing an issue * Update hyperlink * update a hyperlin * fixing hyperlink * updatereadme.md files w/ grammer corrections * updatereadme.md files w/ grammer & spelling corrections * Audrey's edits to fpga_compile's README * Disambiguate "compile time" in fpga_compile README * updatereadme.md files w/ grammer & spelling corrections * updatereadme.md files w/ grammer corrections * updatereadme.md files w/ grammer corrections * updatereadme.md files w/ grammer corrections * updatereadme.md files w/ grammer corrections * updatereadme.md files w/ grammer corrections * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * Update README.md * updatereadme.md files w/ corrected window run commands * Update README.md * Remove license files from all samples, except root * Readme changes based pn Lincense file requirements * removing unused folders * Minor fixits to FPGA project template READMEs Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Fix some grammar-check-induced ambiguity in FPGA Reference Design READMEs Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Correct name of FPGA PAC D5005 in reference design README files. Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Fix errors introduced by grammar check in FPGA Design Pattern READMEs Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Fix errors introduced by Grammarly in FPGA Tools and GettingStarted code samples Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Fix auto-corrections in READMEs for FPGA Features code samples Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * repalcing license file * repalcing license file * correcting formatting * Update README.md * Update README.md * Update README.md * Fix Intel FPGA PAC D5005 name in FPGA REAMDEs Signed-off-by: Audrey Kertesz <audrey.kertesz@intel.com> * Update readme titles * Updateing readme body * Update ISO2DFD readme body * Updating hyperlinks Co-authored-by: akertesz <67655634+akertesz@users.noreply.github.com> Co-authored-by: tomlenth <tom.f.lenth@intel.com> Co-authored-by: Audrey Kertesz <audrey.kertesz@intel.com>
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‎AI-and-Analytics/End-to-end-Workloads/Census/License.txt‎

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

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# End-to-end machine learning workload: Census
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# End-to-end machine learning workload: `Census` Sample
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This sample code illustrates how to use Intel® Distribution of Modin for ETL operations and ridge regression algorithm from the Intel® oneAPI Data Analytics Library (oneDAL) accelerated scikit-learn library to build and run an end to end machine learning workload. Both Intel Distribution of Modin and oneDAL accelerated scikit-learn libraries are available together in [Intel AI Analytics Toolkit](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). This sample code demonstrates how to seamlessly run the end-to-end census workload using the toolkit, without any external dependencies.
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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 the total income earned in the US.
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Data transformation stage normalizes the income to the yearly inflation, balances the data such that each year has a similar number of data points, and extracts the features from the transformed dataset. The feature vectors are fed into the ridge regression model to predict the income of each sample.
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Dataset is from IPUMS USA, University of Minnesota , [www.ipums.org](https://ipums.org/) (Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek. IPUMS USA: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D010.V10.0)
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Dataset is from IPUMS USA, University of Minnesota, [www.ipums.org](https://ipums.org/) (Steven Ruggles, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and Matthew Sobek. IPUMS USA: Version 10.0 [dataset]. Minneapolis, MN: IPUMS, 2020. https://doi.org/10.18128/D010.V10.0)
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## Key Implementation Details
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This end-to-end workload sample code is implemented for CPU using the Python language. With the installation of Intel AI Analytics Toolkit, the conda environment is prepared with Python version 3.7, Intel® Distribution of Modin , Ray, Intel® oneAPI Data Analytics Library (oneDAL), Scikit-Learn, NumPy following which the sample code can be directly run using the underlying steps in this README.
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## License
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This code sample is licensed under MIT license
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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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)
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## Building Intel® Distribution of Modin and Intel® oneAPI Data Analytics Library (oneDAL) for CPU to build and run end-to-end workload
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Intel® Distribution of Modin and Intel® oneAPI Data Analytics Library (oneDAL) is ready for use once you finish the Intel AI Analytics Toolkit installation with the Conda Package Manager.
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You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi), and the Toolkit [Getting Started Guide for Linux](https://software.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top.html) for installation steps and scripts.
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### Activate conda environment With Root Access
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Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script and Intel® Distribution of Modin environment installation (https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-with-conda.html). 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 super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
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Please follow the Getting Started Guide steps (above) to set up your oneAPI environment with the `setvars.sh` script and [Intel® Distribution of Modin environment installation] (https://software.intel.com/content/www/us/en/develop/articles/installing-ai-kit-with-conda.html). 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 super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
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Activate the conda environment with the following command:
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### Install wget package
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Install wget package in order to retrieve the Census dataset using HTTPS
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Install wget package to retrieve the Census dataset using HTTPS
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```
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### Run as Python File
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Open notebook in Jupyter and download as python file (see image using "census modin" sample)
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Open notebook in Jupyter and download as python file (see the image using "census modin" sample)
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![Download as python file in the Jupyter Notebook](Running_Jupyter_notebook_as_Python.jpg "Download as python file in the Jupyter Notebook")
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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_Extensions_AutoMixedPrecision/README.md‎

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# `Intel Extension for PyTorch Getting Started` Sample
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Intel Extension for PyTorch is a Python package to extend official PyTorch. It is designed to make the Out-of-Box user experience of PyTorch CPU better while achieving good performance. The extension also will be the PR(Pull-Request) buffer for the Intel PyTorch framework dev team. The PR buffer will not only contain functions, but also optimization (for example, take advantage of Intel's new hardware features).
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Intel Extension for PyTorch is a Python package to extend the official PyTorch. It is designed to make the Out-of-Box user experience of PyTorch CPU better while achieving good performance. The extension also will be the PR(Pull-Request) buffer for the Intel PyTorch framework dev team. The PR buffer will contain functions and optimization (for example, take advantage of Intel's new hardware features).
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For comprehensive instructions regarding Intel Extension for PyTorch, go to https://github.com/intel/intel-extension-for-pytorch.
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For comprehensive instructions goto the github repo for [Intel Extension for PyTorch](https://github.com/intel/intel-extension-for-pytorch).
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## Purpose
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From this sample code, you will learn how to download, compile and get started with Intel Extension for PyTorch.
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You will learn how to download, compile, and get started with Intel Extension for PyTorch from this sample code.
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The code will be running on CPU.
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The code will be running on the CPU.
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## Key Implementation Details
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The code includes Intel Extension for PyTorch and Auto-mixed-precision.
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## License
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This code sample is licensed under MIT license.
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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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)
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## Building the `Intel Extension for PyTorch Getting Started` Sample
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### Running Samples In DevCloud
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‎AI-and-Analytics/Features-and-Functionality/IntelPyTorch_TorchCCL_Multinode_Training/README.md‎

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# `Intel Extension for PyTorch Getting Started` Sample
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# `Intel Extension for PyTorch Getting Started` Sample
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Intel® oneCCL (collective commnications library) is a library for efficient distributed deep learning training implementing such collectives like allreduce, allgather, alltoall. For more information on oneCCL, please refer to the oneCCL documentation.
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Intel® oneCCL (collective communications library) is a library for efficient distributed deep learning training that implements such collectives like allreduce, allgather, and alltoall. For more information on oneCCL, please refer to the oneCCL documentation.
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* [PyTorchand CCL](https://github.com/intel/torch-ccl)
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* [PyTorch](https://github.com/intel/optimized-models/tree/master/pytorch/distributed)
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The code will be running on the CPU.
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This code sample is licensed under MIT license.
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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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)
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## Building the `torch-ccl Getting Started` Sample
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‎AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans/License.txt‎

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‎AI-and-Analytics/Features-and-Functionality/IntelPython_daal4py_DistributedKMeans/README.md‎

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# Intel Python daal4py Distributed K-Means
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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 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).
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# `Intel Python daal4py Distributed K-Means` Samplw
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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).
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daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for either direct usage or integration into one's own framework.
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daal4py is a simplified API to Intel® DAAL that allows for fast usage of the framework suited for Data Scientists or Machine Learning users. Built to help provide an abstraction to Intel® DAAL for direct usage or integration into one's own framework.
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In this sample you will run a distributed K-Means model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
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In this sample, you will run a distributed K-Means model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file.
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## Key Implementation Details
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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(R) Distribution for Python as part of the [oneAPI AI Analytics Toolkit powered by oneAPI](https://software.intel.com/en-us/oneapi/ai-kit).
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## License
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This code sample is licensed under MIT license
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Code samples are licensed under the MIT license. See
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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)
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oneAPI Data Analytics Library is ready for use once you finish the Intel AI Analytics Toolkit installation and have run the post installation script.
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You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation, and the Toolkit [Getting 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.
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You can refer to the oneAPI [main page](https://software.intel.com/en-us/oneapi) for toolkit installation and the Toolkit [Getting 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.
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### Activate conda environment With Root Access
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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 super user. If you customized the installation folder, the `setvars.sh` file is in your custom folder.
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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.
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The output of the script will be saved in the included models and result directories.
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_Note: This code samples focuses on how to use daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on single local node. In order to launch on a cluster, you will need to create a host file on the master node among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
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_Note: This code samples focus on using daal4py to do distributed ML computations on chunks of data. The `mpirun` command above will only run on a single local node. To launch on a cluster, you will need to create a host file on the master node, among other steps. The **TensorFlow_Multinode_Training_with_Horovod** code sample explains this process well._
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##### Expected Printed Output (with similar numbers, printed 4 times):
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```

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