From cb9b3d34f1233aaa4a398d67217b64ddb51005e6 Mon Sep 17 00:00:00 2001 From: "Oberman, Rachel" Date: Thu, 20 Aug 2020 12:15:14 -0700 Subject: [PATCH 01/13] adding WIP daal4py sample for migration --- Libraries/oneDAL/License.txt | 20 ++ .../daal4py_Getting_Started/Jupyter_Run.jpg | Bin 0 -> 21870 bytes .../Jupyter_Save_Py.jpg | Bin 0 -> 34787 bytes .../daal4py_Getting_Started/License.txt | 20 ++ .../oneDAL/daal4py_Getting_Started/README.md | 148 ++++++++++ .../daal4py_GettingStarted.ipynb | 252 ++++++++++++++++++ .../daal4py_GettingStarted.py | 105 ++++++++ .../models/linear_regression_batch.sav | Bin 0 -> 16905 bytes .../linear_regression_batch_results.csv | 127 +++++++++ .../daal4py_Getting_Started/sample.json | 32 +++ 10 files changed, 704 insertions(+) create mode 100644 Libraries/oneDAL/License.txt create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Run.jpg create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Save_Py.jpg create mode 100644 Libraries/oneDAL/daal4py_Getting_Started/License.txt create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/README.md create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.ipynb create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.py create mode 100644 Libraries/oneDAL/daal4py_Getting_Started/models/linear_regression_batch.sav create mode 100644 Libraries/oneDAL/daal4py_Getting_Started/results/linear_regression_batch_results.csv create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/sample.json diff --git a/Libraries/oneDAL/License.txt b/Libraries/oneDAL/License.txt new file mode 100644 index 0000000000..619419dbbd --- /dev/null +++ b/Libraries/oneDAL/License.txt @@ -0,0 +1,20 @@ + +Copyright (c) 2020, Intel Corporation. All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Run.jpg b/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Run.jpg new file mode 100755 index 0000000000000000000000000000000000000000..4f54045950e68e5f99a90c513070114d9a1fe0ed GIT binary patch literal 21870 zcmeHt2UJu`m+m2F5d_IWg5;c`$yr2#BngtCTXJYX5St(6h)#S zP0q2&IW({R=X&oybMKlrcV^yOv({L>PM=+M_Nm(Y+f}>vsjAEGmovcCTWXqW00ssI zV1@nxm$PJqs(ug$0MOP3xB&pb2QZ&P0BkgcfzDX1(F1tsI~zKG6MX^nYv@1xAJmV8 zPmKPP`$7Hd@*gF?(k}#lA@B=e_n-Ea2RWg}^Ta zej)I`2mxV1VL@p@NohfGW+72&Q88&D3E;2F06-n^1nkj2SM=o!cmuWo7~ln%fjfXT zItNDoyP|VH0+h>HA`Y;(x4X1}fU6h3wVj)dJ-@A+i-4cCyMPeCpa1}p_j9+lb+Y$n zwy}4FxXN;D)i-l6L+oTZjK#DCwcVBN!4M69Py4(6cMNR(oouD-IOOF>2tj_*elG4V 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All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Getting_Started/README.md b/Libraries/oneDAL/daal4py_Getting_Started/README.md new file mode 100755 index 0000000000..d487e2feba --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/README.md @@ -0,0 +1,148 @@ +# daal4py Getting Started +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 AI Analytics Toolkit powered by oneAPI](https://software.intel.com/content/www/us/en/develop/tools/oneapi/ai-analytics-toolkit.html). + +| Optimized for | Description +| :--- | :--- +| OS | 64-bit Linux: Ubuntu 18.04 or higher, 64-bit Windows 10, macOS 10.14 or higher +| Hardware | Kaby Lake with GEN9 or newer +| Software | Intel oneAPI Deep Neural Network Library (oneDNN), Intel oneAPI DPC++ Compiler, Intel oneAPI Threading Building Blocks (oneTBB) +| What you will learn | basic oneDNN programming model for Intel CPU and GPU +| Time to complete | 15 minutes + +## What You Will Learn + +* How to create oneDNN memory objects. +* How to get data from application buffer into a oneDNN memory object. +* How tensor's logical dimensions and memory object formats relate. +* How to create oneDNN primitives. +* How to execute the primitives. +* +## Key implementation details +This Getting Started 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). + +## License +This code sample is licensed under MIT license + +## How to Build and Run + +oneAPI Data Analytics Library 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 [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. + +## 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: + +#### Linux +``` +source activate base +``` + +## Activate conda environment Without Root Access (Optional) + +By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: + +#### Linux +``` +conda create --name user_base --clone base +``` + +Then activate your conda environment with the following command: + +``` +source activate user_base +``` + +### Install _Jupyter Notebook_ + +Launch Jupyter Notebook in the directory housing the code example + +``` +conda install jupyter nb_conda_kernels +``` + +#### _View in Jupyter Notebook_ + +_Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ + +Launch Jupyter Notebook in the directory housing the code example + +``` +jupyter notebook +``` + +Open .pynb file and run cells in Jupyter Notebook using the "Run" button (see image) + +![Click the Run Button in the Jupyter Notebook](Jupyter_Run.jpg "Run Button on Jupyter Notebook") + +##### Expected Printed Output for Cells (with similar numbers): +``` +Here's our model: + + + NumberOfBetas: 14 + +NumberOfResponses: 1 + +InterceptFlag: False + +Beta: array( + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]], + dtype=float64, shape=(1, 14)) + +NumberOfFeatures: 13 + +Here is one of our loaded model's features: + + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]] +[CODE_SAMPLE_COMPLETED_SUCCESFULLY] +``` + + +#### _Run as Python File_ + +Open notebook in Jupyter and download as python file + +![Download as python file in the Jupyter Notebook](Jupyter_Save_Py.jpg "Download as python file in the Jupyter Notebook") + +Run the Program + +`python IntelPython_GettingStarted.py` + +The output files of the script will be saved in the included models and results directories. + +##### Expected Printed Output (with similar numbers): +``` +Here's our model: + + + NumberOfBetas: 14 + +NumberOfResponses: 1 + +InterceptFlag: False + +Beta: array( + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]], + dtype=float64, shape=(1, 14)) + +NumberOfFeatures: 13 + +Here is one of our loaded model's features: + + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]] +[CODE_SAMPLE_COMPLETED_SUCCESFULLY] +``` + diff --git a/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.ipynb b/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.ipynb new file mode 100755 index 0000000000..521b43af6b --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================\n", + "# Copyright © 2020 Intel Corporation\n", + "# \n", + "# SPDX-License-Identifier: MIT\n", + "# =============================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# IntelPython Getting Started Example for Shared Memory Systems" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing and Organizing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we will be predicting **prices of houses in Boston** based on the features of each house.\n", + "\n", + "Let's start by **importing** all necessary data and packages." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##### Linear regression example for shared memory systems #####\n", + "import daal4py as d4p\n", + "from sklearn.datasets import load_boston\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load** in the dataset and **organize** it as necessary to work with our model." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# loading in the data\n", + "data = load_boston()\n", + "\n", + "# organizing variables used in the model for prediction\n", + "X = data.data # house characteristics\n", + "y = data.target[np.newaxis].T # house price\n", + "\n", + "# splitting the data for training and testing, with a 25% test dataset size\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state =1693)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training and Saving the Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's **train our model** and look at the model's features!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# training the model for prediction\n", + "train_result = d4p.linear_regression_training().compute(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To **get training model information** and **save it to a file**:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here's our model:\n", + "\n", + "\n", + " NumberOfBetas: 14\n", + "\n", + "NumberOfResponses: 1\n", + "\n", + "InterceptFlag: False\n", + "\n", + "Beta: array(\n", + " [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03\n", + " 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03\n", + " -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01\n", + " 1.58423529e-02 -4.57542900e-01]],\n", + " dtype=float64, shape=(1, 14))\n", + "\n", + "NumberOfFeatures: 13 \n", + "\n" + ] + } + ], + "source": [ + "# retrieving and printing training model\n", + "model = train_result.model\n", + "print(\"Here's our model:\\n\\n\\n\", model , \"\\n\")\n", + "\n", + "model_filename = './models/linear_regression_batch.sav'\n", + "\n", + "# saving model to a file\n", + "pickle.dump(model, open(model_filename, \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load up the model** and look at one of the model's features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is one of our loaded model's features: \n", + "\n", + " [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03\n", + " 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03\n", + " -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01\n", + " 1.58423529e-02 -4.57542900e-01]]\n" + ] + } + ], + "source": [ + "# loading the training model from a file\n", + "loaded_model = pickle.load(open(model_filename, \"rb\"))\n", + "print(\"Here is one of our loaded model's features: \\n\\n\", loaded_model.Beta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making a Prediction and Saving the Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to **make a prediction!**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# now predicting the target feature(s) using the trained model\n", + "y_pred = d4p.linear_regression_prediction().compute(X_test, loaded_model).prediction " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **export the results to a CSV file**." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CODE_SAMPLE_COMPLETED_SUCCESFULLY]\n" + ] + } + ], + "source": [ + "np.savetxt(\"./results/linear_regression_batch_results.csv\", y_pred, delimiter = \",\")\n", + "print(\"[CODE_SAMPLE_COMPLETED_SUCCESFULLY]\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.py b/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.py new file mode 100755 index 0000000000..1719881fe8 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/daal4py_GettingStarted.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +''' +============================================================= +Copyright © 2020 Intel Corporation + +SPDX-License-Identifier: MIT +============================================================= +''' + +# # IntelPython Getting Started Example for Shared Memory Systems + +# ## Importing and Organizing Data + +# In this example we will be predicting **prices of houses in Boston** based on the features of each house. +# +# Let's start by **importing** all necessary data and packages. + +# In[2]: + + +##### Linear regression example for shared memory systems ##### +import daal4py as d4p +from sklearn.datasets import load_boston +from sklearn.model_selection import train_test_split +import pandas as pd +import numpy as np +import pickle + + +# Now let's **load** in the dataset and **organize** it as necessary to work with our model. + +# In[3]: + + +# loading in the data +data = load_boston() + +# organizing variables used in the model for prediction +X = data.data # house characteristics +y = data.target[np.newaxis].T # house price + +# splitting the data for training and testing, with a 25% test dataset size +X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state =1693) + + +# ## Training and Saving the Model + +# Let's **train our model** and look at the model's features! + +# In[4]: + + +# training the model for prediction +train_result = d4p.linear_regression_training().compute(X_train, y_train) + + +# To **get training model information** and **save it to a file**: + +# In[5]: + + +# retrieving and printing training model +model = train_result.model +print("Here's our model:\n\n\n", model , "\n") + +model_filename = './models/linear_regression_batch.sav' + +# saving model to a file +pickle.dump(model, open(model_filename, "wb")) + + +# Now let's **load up the model** and look at one of the model's features. + +# In[6]: + + +# loading the training model from a file +loaded_model = pickle.load(open(model_filename, "rb")) +print("Here is one of our loaded model's features: \n\n", loaded_model.Beta) + + +# ## Making a Prediction and Saving the Results + +# Time to **make a prediction!** + +# In[7]: + + +# now predicting the target feature(s) using the trained model +y_pred = d4p.linear_regression_prediction().compute(X_test, loaded_model).prediction + + +# Now let's **export the results to a CSV file**. + +# In[8]: + + +np.savetxt("./results/linear_regression_batch_results.csv", y_pred, delimiter = ",") 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a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json new file mode 100755 index 0000000000..b310f7b2c1 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -0,0 +1,32 @@ +{ + "guid": "2E6A2E22-035F-493B-B471-DFD8CF8F8256", + "name": "daal4py Getting Started", + "categories": ["Toolkit/Intel® AI Analytics Toolkit/oneDAL"], + "description": "This Getting Started sample code shows how to do batch linear regression using the python API package daal4py for oneDAL", + "builder": ["cli"], + "languages": [{"python":{}}], + "dependencies": ["oneDAL", "pickle"] + "os":["linux"], + "targetDevice": ["CPU"], + "ciTests": { + "linux": [ + { + "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], + "id": "d4p_GS_py", + "steps": [ + "cd oneapi-toolkit/oneDAL/daal4py_Getting_Started", + "python daal4py_GettingStarted.py", "runipy daal4py_GettingStarted.ipynb" + + ] + }, + { + "id": "d4p_GS_ipynb", + "steps": [ + "cd oneapi-toolkit/oneDAL/daal4py_Getting_Started", + "runipy daal4py_GettingStarted.ipynb" + + ] + } + ] +} +} From a5023840e3d1ac3b626305d59bcbef0cce9db1cc Mon Sep 17 00:00:00 2001 From: "Oberman, Rachel" Date: Thu, 20 Aug 2020 13:34:13 -0700 Subject: [PATCH 02/13] more modifications and adding distributed samples for migration --- .../daal4py_Distributed_Kmeans/License.txt | 20 + .../daal4py_Distributed_Kmeans/README.md | 113 ++++ .../daal4py_Distributed_Kmeans.py | 125 ++++ .../daal4py_Distributed_Kmeans_1.csv | 601 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_2.csv | 601 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_3.csv | 601 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_4.csv | 601 ++++++++++++++++++ .../daal4py_Distributed_Kmeans/sample.json | 22 + .../License.txt | 20 + .../oneDAL/daal4py_Getting_Started/README.md | 21 +- .../daal4py_Getting_Started/sample.json | 6 +- 11 files changed, 2715 insertions(+), 16 deletions(-) create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.py create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_1.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_2.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_3.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_4.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt b/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt new file mode 100755 index 0000000000..619419dbbd --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt @@ -0,0 +1,20 @@ + +Copyright (c) 2020, Intel Corporation. All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md new file mode 100755 index 0000000000..b443ecf050 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md @@ -0,0 +1,113 @@ +# daal4py Distributed Kmeans 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 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 +| :--- | :--- +| OS | 64-bit Linux: Ubuntu 18.04 or higher, 64-bit Windows 10, macOS 10.14 or higher +| Hardware | Intel Atom® Processors; Intel® Core™ Processor Family; Intel® Xeon® Processor Family; Intel® Xeon® Scalable Performance Processor Family +| Software | oneDAL Software Library, Python version 2.7 or >= 3.6, conda-build version >= 3, C++ compiler with C++11 support, Pickle, Pandas, NumPy +| What you will learn | distributed oneDAL K-Means programming model for Intel CPU +| Time to complete | 5 minutes + +## What You Will Learn + +* How to run a distributed K-Means model with oneDAL daal4py library memory objects. +* How to train a model and save the information to a file + +## Key implementation details +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). + +## Additional Requirements +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). + +## License +This code sample is licensed under MIT license + +## How to Build and Run + +oneAPI Data Analytics Library 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 [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. + +## 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: + +#### Linux +``` +source activate base +``` + +## Activate conda environment Without Root Access (Optional) + +By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: + +#### Linux +``` +conda create --name user_base --clone base +``` + +Then activate your conda environment with the following command: + +``` +source activate user_base +``` + +## Install Jupyter Notebook +``` +conda install jupyter nb_conda_kernels +``` + + +#### _View in Jupyter Notebook_ + +_Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ + +Launch Jupyter Notebook in the directory housing the code example + +``` +jupyter notebook +``` + +#### _Run as Python File_ + +When using daal4py for distributed memory systems, the command needed to execute the program should be executed in a bash shell. To execute this example, run the following command, where the number **4** is chosen as an example and means that it will run on **4 processes**: + +Run the Program + +`mpirun -n 4 python ./IntelPython_daal4py_Distributed_Kmeans.py` + +The output of the script will be saved in the included models and results directories. + +_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._ + +##### Expected Printed Output (with similar numbers, multiplied by 4): +``` + +Here's our model: + + + NumberOfBetas: 14 + +NumberOfResponses: 1 + +InterceptFlag: False + +Beta: array( + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]], + dtype=float64, shape=(1, 14)) + +NumberOfFeatures: 13 + +Here is one of our loaded model's features: + + [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 + 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 + -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 + 1.58423529e-02 -4.57542900e-01]] +``` + + diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.py b/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.py new file mode 100755 index 0000000000..611abc988c --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.py @@ -0,0 +1,125 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +''' +============================================================= +Copyright © 2020 Intel Corporation + +SPDX-License-Identifier: MIT +============================================================= +''' + +# # Daal4py K-Means Clustering Example for Distributed Memory Systems [SPMD mode] + +# ## IMPORTANT NOTICE + +# When using daal4py for distributed memory systems, the command needed to execute the program should be **executed +# in a bash shell**. In order to run this example, please download it as a .py file then run the following command (**the number 4 means that it will run on 4 processes**): + +# mpirun -n 4 python ./daal4py_Distributed_Kmeans.py + +# ## Importing and Organizing Data + +# In this example we will be using K-Means clustering to **initialize centroids** and then **use them to cluster the synthetic dataset.** +# +# Let's start by **importing** all necessary data and packages. + +# In[2]: + + +##### daal4py K-Means Clustering example for Distributed Memory Systems [SPMD Mode] ##### +import daal4py as d4p +import pickle +import pandas as pd +import numpy as np + + +# Now let's **load** in the dataset and **organize** it as necessary to work with our model. For distributed, every file has a unique ID. +# +# We will also **initialize the distribution engine**. + +# In[3]: + + +d4p.daalinit() #initializes the distribution engine + +# organizing variables used in the model for prediction +# each process gets its own data +infile = "./data/distributed_data/daal4py_Distributed_Kmeans_" + str(d4p.my_procid()+1) + ".csv" + +# read data +X = pd.read_csv(infile) + + +# ## Computing and Saving Initial Centroids + +# Time to **initialize our centroids!** + +# In[4]: + + +# computing inital centroids +init_result = d4p.kmeans_init(nClusters = 3, method = "plusPlusDense").compute(X) + + +# To **get initial centroid information and save it** to a file: + +# In[5]: + + +# retrieving and printing inital centroids +centroids = init_result.centroids +print("Here our centroids:\n\n\n", centroids, "\n") + +centroids_filename = './models/kmeans_clustering_initcentroids_'+ str(d4p.my_procid()+1) + '.csv' + +# saving centroids to a file +pickle.dump(centroids, open(centroids_filename, "wb")) + + +# Now let's **load up the centroids** and look at them. + +# In[6]: + + +# loading the initial centroids from a file +loaded_centroids = pickle.load(open(centroids_filename, "rb")) +print("Here is our centroids loaded from file:\n\n",loaded_centroids) + + +# # Assign The Data to Clusters and Save The Results + +# Let's **assign the data** to clusters. + +# In[7]: + + +# compute the clusters/centroids +kmeans_result = d4p.kmeans(nClusters = 3, maxIterations = 5, assignFlag = True).compute(X, init_result.centroids) + + +# To **get Kmeans result objects** (assignments, centroids, goalFunction [deprecated], nIterations, and objectiveFunction): + +# In[8]: + + +# retrieving and printing cluster assignments +assignments = kmeans_result.assignments +print("Here is our cluster assignments for first 5 datapoints: \n\n", assignments[:5]) + + +# Now let's **export the cluster assignments** to a **CSV file**. We will also **stop the distribution engine.** + +# In[9]: + + +# now export the results to a CSV file +results_filename = "./results/daal4py_Distributed_Kmeans_results_" + str(d4p.my_procid()+1) + ".csv" +np.savetxt(results_filename, assignments, delimiter=",") + +d4p.daalfini() # stops the distribution engine +print('[CODE_SAMPLE_COMPLETED_SUCCESFULLY]') + diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_1.csv b/Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_1.csv new file mode 100755 index 0000000000..7f45cc383f --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/data/distributed_data/daal4py_Distributed_Kmeans_1.csv @@ -0,0 +1,601 @@ +,0,1 +0,1.8723965807238774,9.423076509081708 +1,2.3039829051048324,9.018580186635331 +2,1.026073451114729,8.077031073995432 +3,-8.541605432789838,-8.00341797317489 +4,-2.369401173189912,-6.975984734090902 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/dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json @@ -0,0 +1,22 @@ +{ + "guid": "B69FAC86-88BF-41BD-B4E0-ACDF753ED3CE", + "name": "daal4py Distributed K-means", + "categories": ["Toolkit/Intel® AI Analytics Toolkit/oneDAL"], + "description": "This sample code shows how to train and predict with a distributed k-means model with the Intel Distribution of Python using the python API package daal4py for oneDAL", + "builder": ["cli"], + "languages": [{"python":{}}], + "dependencies": ["oneDAL", "pickle"], + "os":["linux"], + "targetDevice": ["CPU"], + "ciTests": { + "linux": [ + { + "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], + "id": "d4p_KM_Dist", + "steps": [ + "mpirun -n 4 python ./daal4py_Distributed_LinearRegression.py" + ] + } + ] +} +} diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt new file mode 100755 index 0000000000..619419dbbd --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt @@ -0,0 +1,20 @@ + +Copyright (c) 2020, Intel Corporation. All rights reserved. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Getting_Started/README.md b/Libraries/oneDAL/daal4py_Getting_Started/README.md index d487e2feba..ebbd6f871f 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/README.md +++ b/Libraries/oneDAL/daal4py_Getting_Started/README.md @@ -1,22 +1,19 @@ # daal4py Getting Started -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 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 | :--- | :--- | OS | 64-bit Linux: Ubuntu 18.04 or higher, 64-bit Windows 10, macOS 10.14 or higher -| Hardware | Kaby Lake with GEN9 or newer -| Software | Intel oneAPI Deep Neural Network Library (oneDNN), Intel oneAPI DPC++ Compiler, Intel oneAPI Threading Building Blocks (oneTBB) -| What you will learn | basic oneDNN programming model for Intel CPU and GPU -| Time to complete | 15 minutes +| Hardware | Intel Atom® Processors; Intel® Core™ Processor Family; Intel® Xeon® Processor Family; Intel® Xeon® Scalable Performance Processor Family +| Software | oneDAL Software Library, Python version 2.7 or >= 3.6, conda-build version >= 3, C++ compiler with C++11 support, Pickle, Pandas, NumPy +| What you will learn | basic oneDAL programming model for Intel CPU +| Time to complete | 5 minutes ## What You Will Learn -* How to create oneDNN memory objects. -* How to get data from application buffer into a oneDNN memory object. -* How tensor's logical dimensions and memory object formats relate. -* How to create oneDNN primitives. -* How to execute the primitives. -* +* How to run a batch linear regression model with oneDAL daal4py library memory objects. +* How to train a model and save the information to a file + ## Key implementation details This Getting Started 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). @@ -40,7 +37,7 @@ source activate base ## Activate conda environment Without Root Access (Optional) -By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: +By default, the Intel AI Analytics Toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: #### Linux ``` diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index b310f7b2c1..4fc3c7c78e 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -5,7 +5,7 @@ "description": "This Getting Started sample code shows how to do batch linear regression using the python API package daal4py for oneDAL", "builder": ["cli"], "languages": [{"python":{}}], - "dependencies": ["oneDAL", "pickle"] + "dependencies": ["oneDAL", "pickle"], "os":["linux"], "targetDevice": ["CPU"], "ciTests": { @@ -14,15 +14,13 @@ "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], "id": "d4p_GS_py", "steps": [ - "cd oneapi-toolkit/oneDAL/daal4py_Getting_Started", - "python daal4py_GettingStarted.py", "runipy daal4py_GettingStarted.ipynb" + "python daal4py_GettingStarted.py" ] }, { "id": "d4p_GS_ipynb", "steps": [ - "cd oneapi-toolkit/oneDAL/daal4py_Getting_Started", "runipy daal4py_GettingStarted.ipynb" ] From b483b19466368c70dd4f5eb7c8d9fdb0322dc47c Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 13:55:46 -0700 Subject: [PATCH 03/13] more migration additions for LR and Kmeans --- .../daal4py_Distributed_Kmeans/README.md | 36 +- .../daal4py_Distributed_Kmeans.ipynb | 254 ++++++++ .../kmeans_clustering_initcentroids_1.csv | Bin 0 -> 229 bytes .../kmeans_clustering_initcentroids_2.csv | Bin 0 -> 229 bytes .../kmeans_clustering_initcentroids_3.csv | Bin 0 -> 229 bytes .../kmeans_clustering_initcentroids_4.csv | Bin 0 -> 229 bytes .../daal4py_Distributed_Kmeans_results_1.csv | 600 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_results_2.csv | 600 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_results_3.csv | 600 ++++++++++++++++++ .../daal4py_Distributed_Kmeans_results_4.csv | 600 ++++++++++++++++++ .../daal4py_Distributed_Kmeans/sample.json | 2 +- .../README.md | 116 ++++ ...daal4py_Distributed_LinearRegression.ipynb | 285 +++++++++ .../daal4py_Distributed_LinearRegression.py | 122 ++++ .../linear_regression_test.csv | 128 ++++ .../linear_regression_train_1.csv | 96 +++ .../linear_regression_train_2.csv | 96 +++ .../linear_regression_train_3.csv | 96 +++ .../linear_regression_train_4.csv | 95 +++ ...daal4py_Distributed_LinearRegression_1.sav | Bin 0 -> 18057 bytes ...daal4py_Distributed_LinearRegression_2.sav | Bin 0 -> 18057 bytes ...daal4py_Distributed_LinearRegression_3.sav | Bin 0 -> 18057 bytes ...daal4py_Distributed_LinearRegression_4.sav | Bin 0 -> 18057 bytes ..._Distributed_LinearRegression_results1.csv | 127 ++++ ..._Distributed_LinearRegression_results2.csv | 127 ++++ ..._Distributed_LinearRegression_results3.csv | 127 ++++ ..._Distributed_LinearRegression_results4.csv | 127 ++++ .../sample.json | 22 + 28 files changed, 4236 insertions(+), 20 deletions(-) create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.ipynb create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/models/kmeans_clustering_initcentroids_1.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/models/kmeans_clustering_initcentroids_2.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/models/kmeans_clustering_initcentroids_3.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/models/kmeans_clustering_initcentroids_4.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/results/daal4py_Distributed_Kmeans_results_1.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/results/daal4py_Distributed_Kmeans_results_2.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/results/daal4py_Distributed_Kmeans_results_3.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_Kmeans/results/daal4py_Distributed_Kmeans_results_4.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.ipynb create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.py create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_test.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_train_1.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_train_2.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_train_3.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_train_4.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/daal4py_Distributed_LinearRegression_1.sav create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/daal4py_Distributed_LinearRegression_2.sav create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/daal4py_Distributed_LinearRegression_3.sav create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/daal4py_Distributed_LinearRegression_4.sav create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/daal4py_Distributed_LinearRegression_results1.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/daal4py_Distributed_LinearRegression_results2.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/daal4py_Distributed_LinearRegression_results3.csv create mode 100644 Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/daal4py_Distributed_LinearRegression_results4.csv create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md index b443ecf050..82d6468fec 100755 --- a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md @@ -75,39 +75,37 @@ When using daal4py for distributed memory systems, the command needed to execute Run the Program -`mpirun -n 4 python ./IntelPython_daal4py_Distributed_Kmeans.py` +`mpirun -n 4 python ./daal4py_Distributed_Kmeans.py` The output of the script will be saved in the included models and results directories. _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._ -##### Expected Printed Output (with similar numbers, multiplied by 4): +##### Expected Printed Output (with similar numbers, printed 4 times): ``` -Here's our model: +Here our centroids: - NumberOfBetas: 14 -NumberOfResponses: 1 + [[ 5.46000000e+02 -3.26170648e+00 -6.15922494e+00] + [ 1.80000000e+01 -1.00432059e+01 -8.38198798e+00] + [ 4.10000000e+02 3.78330964e-01 8.29073839e+00]] -InterceptFlag: False +Here is our centroids loaded from file: -Beta: array( - [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 - 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 - -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 - 1.58423529e-02 -4.57542900e-01]], - dtype=float64, shape=(1, 14)) + [[ 5.46000000e+02 -3.26170648e+00 -6.15922494e+00] + [ 1.80000000e+01 -1.00432059e+01 -8.38198798e+00] + [ 4.10000000e+02 3.78330964e-01 8.29073839e+00]] +Here is our cluster assignments for first 5 datapoints: -NumberOfFeatures: 13 + [[1] + [1] + [1] + [1] + [1]] +[CODE_SAMPLE_COMPLETED_SUCCESFULLY] -Here is one of our loaded model's features: - - [[ 0.00000000e+00 -1.05416344e-01 5.25259886e-02 4.26844883e-03 - 2.76607367e+00 -2.82517989e+00 5.49968304e+00 3.48833264e-03 - -8.73247684e-01 1.74005447e-01 -8.38917510e-03 -3.28044397e-01 - 1.58423529e-02 -4.57542900e-01]] ``` diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.ipynb b/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.ipynb new file mode 100755 index 0000000000..8d245508dc --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/daal4py_Distributed_Kmeans.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================\n", + "# Copyright © 2020 Intel Corporation\n", + "# \n", + "# SPDX-License-Identifier: MIT\n", + "# =============================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Daal4py K-Means Clustering Example for Distributed Memory Systems [SPMD mode]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IMPORTANT NOTICE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using daal4py for distributed memory systems, the command needed to execute the program should be **executed \n", + "in a bash shell**. In order to run this example, please download it as a .py file then run the following command (**the number 4 means that it will run on 4 processes**):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mpirun -n 4 python ./daal4py_Distributed_Kmeans.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing and Organizing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we will be using K-Means clustering to **initialize centroids** and then **use them to cluster the synthetic dataset.**\n", + "\n", + "Let's start by **importing** all necessary data and packages." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##### daal4py K-Means Clustering example for Distributed Memory Systems [SPMD Mode] #####\n", + "import daal4py as d4p\n", + "import pickle\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load** in the dataset and **organize** it as necessary to work with our model. For distributed, every file has a unique ID.\n", + "\n", + "We will also **initialize the distribution engine**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "d4p.daalinit() #initializes the distribution engine\n", + "\n", + "# organizing variables used in the model for prediction\n", + "# each process gets its own data\n", + "infile = \"./data/distributed_data/daal4py_Distributed_Kmeans_\" + str(d4p.my_procid()+1) + \".csv\"\n", + "\n", + "# read data\n", + "X = pd.read_csv(infile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Computing and Saving Initial Centroids" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to **initialize our centroids!**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# computing inital centroids\n", + "init_result = d4p.kmeans_init(nClusters = 3, method = \"plusPlusDense\").compute(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To **get initial centroid information and save it** to a file:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hers is our centroids:\n", + "\n", + "\n", + " [[ 5.46000000e+02 -4.95417384e-01 8.83354904e+00]\n", + " [ 1.80000000e+01 -4.12886224e+00 -7.35426095e+00]\n", + " [ 4.11000000e+02 -3.27940151e+00 -6.22280477e+00]] \n", + "\n" + ] + } + ], + "source": [ + "# retrieving and printing inital centroids\n", + "centroids = init_result.centroids\n", + "print(\"Here's our centroids:\\n\\n\\n\", centroids, \"\\n\")\n", + "\n", + "centroids_filename = './models/kmeans_clustering_initcentroids_'+ str(d4p.my_procid()+1) + '.csv'\n", + "\n", + "# saving centroids to a file\n", + "pickle.dump(centroids, open(centroids_filename, \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load up the centroids** and look at them." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is our centroids loaded from file:\n", + "\n", + " [[ 5.46000000e+02 -4.95417384e-01 8.83354904e+00]\n", + " [ 1.80000000e+01 -4.12886224e+00 -7.35426095e+00]\n", + " [ 4.11000000e+02 -3.27940151e+00 -6.22280477e+00]]\n" + ] + } + ], + "source": [ + "# loading the initial centroids from a file\n", + "loaded_centroids = pickle.load(open(centroids_filename, \"rb\"))\n", + "print(\"Here is our centroids loaded from file:\\n\\n\",loaded_centroids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assign The Data to Clusters and Save The Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's **assign the data** to clusters." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# compute the clusters/centroids\n", + "kmeans_result = d4p.kmeans(nClusters = 3, maxIterations = 5, assignFlag = True).compute(X, init_result.centroids)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To **get Kmeans result objects** (assignments, centroids, goalFunction [deprecated], nIterations, and objectiveFunction):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# retrieving and printing cluster assignments\n", + "assignments = kmeans_result.assignments\n", + "print(\"Here is our cluster assignments for first 5 datapoints: \\n\\n\", assignments[:5])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": 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/dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md @@ -0,0 +1,116 @@ +# Intel Python daal4py Distributed LinearRegression +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 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 +| :--- | :--- +| OS | 64-bit Linux: Ubuntu 18.04 or higher, 64-bit Windows 10, macOS 10.14 or higher +| Hardware | Intel Atom® Processors; Intel® Core™ Processor Family; Intel® Xeon® Processor Family; Intel® Xeon® Scalable Performance Processor Family +| Software | oneDAL Software Library, Python version 2.7 or >= 3.6, conda-build version >= 3, C++ compiler with C++11 support, Pickle, Pandas, NumPy +| What you will learn | distributed oneDAL Linear Regression programming model for Intel CPU +| Time to complete | 5 minutes + +## What You Will Learn + +* How to run a distributed Linear model with oneDAL daal4py library memory objects. +* How to train a model and save the information to a file + +## Key implementation details +This distributed linear regression 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](https://software.intel.com/en-us/oneapi/ai-kit). + + +## Additional Requirements +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). + +## License +This code sample is licensed under MIT license + +## How to Build and Run + +oneAPI Data Analytics Library 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 [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. + + +## 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: + +#### Linux +``` +source activate base +``` + +## Activate conda environment Without Root Access (Optional) + +By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: + +#### Linux +``` +conda create --name user_base --clone base +``` + +Then activate your conda environment with the following command: + +``` +source activate user_base +``` + +## Install Jupyter Notebook +``` +conda install jupyter nb_conda_kernels +``` + +#### _View in Jupyter Notebook_ + +_Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ + +Launch Jupyter Notebook in the directory housing the code example + +``` +jupyter notebook +``` + +#### _Run as Python File_ + +When using daal4py for distributed memory systems, the command needed to execute the program should be executed in a bash shell. To execute this example, run the following command, where the number **4** is chosen as an example and means that it will run on **4 processes**: + +Run the Program + +`mpirun -n 4 python ./IntelPython_daal4py_Distributed_LinearRegression.py` + +The output of the script will be saved in the included models and results directories. + +_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._ + +##### Expected Printed Output (with similar numbers, printed 4 times): +``` + + +Here's our model: + + + NumberOfBetas: 15 + +NumberOfResponses: 1 + +InterceptFlag: False + +Beta: array( + [[ 0.00000000e+00 -3.20923431e-03 -1.06404233e-01 5.46052700e-02 + 2.86834741e-03 2.75997053e+00 -2.54371297e+00 5.52421949e+00 + 6.67604639e-04 -9.01293646e-01 1.96091421e-01 -7.50083536e-03 + -3.11567377e-01 1.58333298e-02 -4.62941338e-01]], + dtype=float64, shape=(1, 15)) + +NumberOfFeatures: 14 + +Here is one of our loaded model's features: + + [[ 0.00000000e+00 -3.20923431e-03 -1.06404233e-01 5.46052700e-02 + 2.86834741e-03 2.75997053e+00 -2.54371297e+00 5.52421949e+00 + 6.67604639e-04 -9.01293646e-01 1.96091421e-01 -7.50083536e-03 + -3.11567377e-01 1.58333298e-02 -4.62941338e-01]] +[CODE_SAMPLE_COMPLETED_SUCCESFULLY] + +``` + diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.ipynb b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.ipynb new file mode 100755 index 0000000000..ea51a22f11 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.ipynb @@ -0,0 +1,285 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# =============================================================\n", + "# Copyright © 2020 Intel Corporation\n", + "# \n", + "# SPDX-License-Identifier: MIT\n", + "# =============================================================" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Daal4py Linear Regression Example for Distributed Memory Systems [SPMD mode]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IMPORTANT NOTICE\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When using daal4py for distributed memory systems, the command needed to execute the program should be **executed \n", + "in a bash shell**. In order to run this example, please download it as a .py file then run the following command (**the number 4 means that it will run on 4 processes**):" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "mpirun -n 4 python ./daal4py_Distributed_LinearRegression.py" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Importing and Organizing Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this example we will be predicting **prices of houses in Boston** based on the features of each house.\n", + "\n", + "Let's start by **importing** all necessary data and packages." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "##### daal4py linear regression example for distributed memory systems [SPMD mode] #####\n", + "import daal4py as d4p\n", + "from sklearn.datasets import load_boston\n", + "from sklearn.model_selection import train_test_split\n", + "import pandas as pd\n", + "import numpy as np\n", + "import pickle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load** in the dataset and **organize** it as necessary to work with our model. For distributed, every file has a unique ID.\n", + "\n", + "We will also **initialize the distribution engine**." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "d4p.daalinit() #initializes the distribution engine\n", + "\n", + "# organizing variables used in the model for prediction\n", + "# each process gets its own data\n", + "infile = \"./data/distributed_data/linear_regression_train_\" + str(d4p.my_procid()+1) + \".csv\"\n", + "\n", + "# read data\n", + "indep_data = pd.read_csv(infile).drop([\"target\"], axis=1) # house characteristics\n", + "dep_data = pd.read_csv(infile)[\"target\"] # house price" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training and Saving the Model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to **train our model** and look at the model's features! " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# training the model for prediction\n", + "train_result = d4p.linear_regression_training(distributed=True).compute(indep_data, dep_data)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To **get training model information** and **save it to a file**:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here's our model:\n", + "\n", + "\n", + " NumberOfBetas: 15\n", + "\n", + "NumberOfResponses: 1\n", + "\n", + "InterceptFlag: False\n", + "\n", + "Beta: array(\n", + " [[ 0.00000000e+00 -1.68027665e-04 -7.40435666e-02 3.72706786e-02\n", + " -1.32246207e-01 5.24821226e+00 -2.09646770e+00 6.15919748e+00\n", + " -1.17193612e-03 -8.86515999e-01 2.23344092e-02 -1.09556173e-03\n", + " -4.40967972e-01 1.12216533e-02 -4.74953243e-01]],\n", + " dtype=float64, shape=(1, 15))\n", + "\n", + "NumberOfFeatures: 14 \n", + "\n" + ] + } + ], + "source": [ + "# retrieving and printing training model\n", + "model = train_result.model\n", + "print(\"Here's our model:\\n\\n\\n\",model , \"\\n\")\n", + "\n", + "model_filename = './models/daal4py_Distributed_LinearRegression_' + str(d4p.my_procid()+1) + '.sav'\n", + "\n", + "# saving model to a file\n", + "pickle.dump(model, open(model_filename, \"wb\"))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **load up the model** and look at one of the model's features." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Here is one of our loaded model's features: \n", + "\n", + " [[ 0.00000000e+00 -1.68027665e-04 -7.40435666e-02 3.72706786e-02\n", + " -1.32246207e-01 5.24821226e+00 -2.09646770e+00 6.15919748e+00\n", + " -1.17193612e-03 -8.86515999e-01 2.23344092e-02 -1.09556173e-03\n", + " -4.40967972e-01 1.12216533e-02 -4.74953243e-01]]\n" + ] + } + ], + "source": [ + "# loading the training model from a file\n", + "loaded_model = pickle.load(open(model_filename, \"rb\"))\n", + "print(\"Here is one of our loaded model's features: \\n\\n\",loaded_model.Beta)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Making a Prediction and Saving the Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Time to **make a prediction!**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# read test data\n", + "test_data = pd.read_csv(\"./data/distributed_data/linear_regression_test.csv\").drop([\"target\"], axis=1)\n", + "\n", + "# now predict using the model from the training above\n", + "predict_result = d4p.linear_regression_prediction().compute(test_data, train_result.model).prediction" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's **export the results to a CSV file**. We will also **stop the distribution engine.**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[CODE_SAMPLE_COMPLETED_SUCCESFULLY]\n" + ] + } + ], + "source": [ + "# now export the results to a CSV file\n", + "results_filename = \"./results/daal4py_Distributed_LinearRegression_results\" + str(d4p.my_procid()+1) + \".csv\"\n", + "np.savetxt(results_filename, predict_result, delimiter = \",\")\n", + "\n", + "d4p.daalfini() # stops the distribution engine\n", + "print(\"[CODE_SAMPLE_COMPLETED_SUCCESFULLY]\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.py b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.py new file mode 100755 index 0000000000..820afbafa3 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/daal4py_Distributed_LinearRegression.py @@ -0,0 +1,122 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +''' +============================================================= +Copyright © 2020 Intel Corporation + +SPDX-License-Identifier: MIT +============================================================= +''' + +# # Daal4py Linear Regression Example for Distributed Memory Systems [SPMD mode] + +# ## IMPORTANT NOTICE +# + +# When using daal4py for distributed memory systems, the command needed to execute the program should be **executed +# in a bash shell**. In order to run this example, please download it as a .py file then run the following command (**the number 4 means that it will run on 4 processes**): + +# mpirun -n 4 python ./daal4py_Distributed_LinearRegression.py + +# ## Importing and Organizing Data + +# In this example we will be predicting **prices of houses in Boston** based on the features of each house. +# +# Let's start by **importing** all necessary data and packages. + +# In[2]: + + +##### daal4py linear regression example for distributed memory systems [SPMD mode] ##### +import daal4py as d4p +from sklearn.datasets import load_boston +from sklearn.model_selection import train_test_split +import pandas as pd +import numpy as np +import pickle + + +# Now let's **load** in the dataset and **organize** it as necessary to work with our model. For distributed, every file has a unique ID. +# +# We will also **initialize the distribution engine**. + +# In[3]: + + +d4p.daalinit() #initializes the distribution engine + +# organizing variables used in the model for prediction +# each process gets its own data +infile = "./data/distributed_data/linear_regression_train_" + str(d4p.my_procid()+1) + ".csv" + +# read data +indep_data = pd.read_csv(infile).drop(["target"], axis=1) # house characteristics +dep_data = pd.read_csv(infile)["target"] # house price + + +# ## Training and Saving the Model + +# Time to **train our model** and look at the model's features! + +# In[4]: + + +# training the model for prediction +train_result = d4p.linear_regression_training(distributed=True).compute(indep_data, dep_data) + + +# To **get training model information** and **save it to a file**: + +# In[5]: + + +# retrieving and printing training model +model = train_result.model +print("Here's our model:\n\n\n",model , "\n") + +model_filename = './models/daal4py_Distributed_LinearRegression_' + str(d4p.my_procid()+1) + '.sav' + +# saving model to a file +pickle.dump(model, open(model_filename, "wb")) + + +# Now let's **load up the model** and look at one of the model's features. + +# In[6]: + + +# loading the training model from a file +loaded_model = pickle.load(open(model_filename, "rb")) +print("Here is one of our loaded model's features: \n\n",loaded_model.Beta) + + +# ## Making a Prediction and Saving the Results + +# Time to **make a prediction!** + +# In[9]: + + +# read test data +test_data = pd.read_csv("./data/distributed_data/linear_regression_test.csv").drop(["target"], axis=1) + +# now predict using the model from the training above +predict_result = d4p.linear_regression_prediction().compute(test_data, train_result.model).prediction + + +# Now let's **export the results to a CSV file**. We will also **stop the distribution engine.** + +# In[10]: + + +# now export the results to a CSV file +results_filename = "./results/daal4py_Distributed_LinearRegression_results" + str(d4p.my_procid()+1) + ".csv" +np.savetxt(results_filename, predict_result, delimiter = ",") + +d4p.daalfini() # stops the distribution engine +print('[CODE_SAMPLE_COMPLETED_SUCCESFULLY]') + diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_test.csv b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_test.csv new file mode 100755 index 0000000000..3f06e11969 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/data/distributed_data/linear_regression_test.csv @@ -0,0 +1,128 @@ +,0,1,2,3,4,5,6,7,8,9,10,11,12,target +357,3.8497,0.0,18.1,1.0,0.77,6.395,91.0,2.5052,24.0,666.0,20.2,391.34,13.27,21.7 +236,0.52058,0.0,6.2,1.0,0.507,6.631,76.5,4.148,8.0,307.0,17.4,388.45,9.54,25.1 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-1.728322998652769371e+01 -1.769327334093282289e+01 -2.411174782838210007e+01 -2.523203701160021240e+01 -2.381302607305574170e+01 -2.133585306620044619e+01 -7.368353711514660453e+00 -2.272029468409390773e+01 -2.225105377865174816e+01 -2.092966855769869028e+01 -2.434825569088754804e+01 -3.170781019565941961e+01 -2.787049888173390144e+01 -2.587565015350248032e+01 --6.508184377396273312e+00 -4.399661916663892214e+01 -2.510490801440884923e+01 -5.391303151074379407e+00 -2.713954851503553911e+01 -2.098536156792856389e+01 -4.375721163826251825e+01 -1.382053536156590923e+01 -3.257217135908798156e+01 -3.427608029539869250e+01 -2.525394364188065666e+01 -1.182649206883593784e+01 -1.894255626960199024e+01 -1.471588579924654638e+01 -2.849635813222538872e+01 -3.223176706209355302e+01 -2.717711126163437640e+01 -2.164168210862941777e+01 -3.509184737487318984e+01 -3.345800232333562718e+01 -2.094772626950917527e+01 -2.118159840018571316e+01 -2.176802506051361163e+01 -2.013490009255475854e+01 -2.305593769127698422e+01 -1.488420339421873884e+01 -2.092391102680546311e+01 -1.576622974571369618e+01 -2.608209571894601098e+01 -1.907221598092810311e+01 -1.996658346379753013e+01 -2.381420539764293309e+01 -2.709548000300753046e+01 -1.770895350099947407e+01 -2.078070745143142517e+01 -2.497325034482068062e+01 From f917ca2e502891548a6e7d0e14d53a68a6df1f13 Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 14:09:35 -0700 Subject: [PATCH 05/13] more edits --- .../models/store_models_in_this_folder.txt | 0 .../results/store_results_in_this_folder.txt | 0 .../models/store_models_in_this_folder.txt | 0 .../results/store_results_in_this_folder.txt | 0 .../models/store_models_in_this_folder.txt | 0 .../results/store_results_in_this_folder.txt | 0 6 files changed, 0 insertions(+), 0 deletions(-) create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/models/store_models_in_this_folder.txt create mode 100755 Libraries/oneDAL/daal4py_Distributed_Kmeans/results/store_results_in_this_folder.txt create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/store_models_in_this_folder.txt create mode 100755 Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/store_results_in_this_folder.txt create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/models/store_models_in_this_folder.txt create mode 100755 Libraries/oneDAL/daal4py_Getting_Started/results/store_results_in_this_folder.txt diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/models/store_models_in_this_folder.txt b/Libraries/oneDAL/daal4py_Distributed_Kmeans/models/store_models_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/results/store_results_in_this_folder.txt b/Libraries/oneDAL/daal4py_Distributed_Kmeans/results/store_results_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/store_models_in_this_folder.txt b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/models/store_models_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/store_results_in_this_folder.txt b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/results/store_results_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 diff --git a/Libraries/oneDAL/daal4py_Getting_Started/models/store_models_in_this_folder.txt b/Libraries/oneDAL/daal4py_Getting_Started/models/store_models_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 diff --git a/Libraries/oneDAL/daal4py_Getting_Started/results/store_results_in_this_folder.txt b/Libraries/oneDAL/daal4py_Getting_Started/results/store_results_in_this_folder.txt new file mode 100755 index 0000000000..e69de29bb2 From 929c54bc1589863e73fb59d22b5f5230b6d78385 Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 14:18:09 -0700 Subject: [PATCH 06/13] fixing inconsistencies and some corrections --- Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json | 2 +- Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md | 2 +- .../oneDAL/daal4py_Distributed_LinearRegression/sample.json | 2 +- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json b/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json index d19415046b..ca263dac01 100755 --- a/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json @@ -5,7 +5,7 @@ "description": "This sample code shows how to train and predict with a distributed k-means model with the Intel Distribution of Python using the python API package daal4py for oneDAL", "builder": ["cli"], "languages": [{"python":{}}], - "dependencies": ["oneDAL", "pickle"], + "dependencies": ["oneDAL"], "os":["linux"], "targetDevice": ["CPU"], "ciTests": { diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md index c70218028c..9b117574b5 100755 --- a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md @@ -76,7 +76,7 @@ When using daal4py for distributed memory systems, the command needed to execute Run the Program -`mpirun -n 4 python ./IntelPython_daal4py_Distributed_LinearRegression.py` +`mpirun -n 4 python ./daal4py_Distributed_LinearRegression.py` The output of the script will be saved in the included models and results directories. diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json index 5b9698e116..999b8a7180 100755 --- a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json @@ -5,7 +5,7 @@ "description": "This sample code shows how to train and predict with a distributed linear regression model with the Intel Distribution of Python using the python API package daal4py for oneDAL", "builder": ["cli"], "languages": [{"python":{}}], - "dependencies": ["oneDAL", "pickle"], + "dependencies": ["oneDAL"], "os":["linux"], "targetDevice": ["CPU"], "ciTests": { diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index 4fc3c7c78e..19a420f632 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -5,7 +5,7 @@ "description": "This Getting Started sample code shows how to do batch linear regression using the python API package daal4py for oneDAL", "builder": ["cli"], "languages": [{"python":{}}], - "dependencies": ["oneDAL", "pickle"], + "dependencies": ["oneDAL"], "os":["linux"], "targetDevice": ["CPU"], "ciTests": { From 052ffa9708357ef1811fd3fef78cf055f93dbb97 Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 15:03:51 -0700 Subject: [PATCH 07/13] fixing properties --- Libraries/oneDAL/License.txt | 22 ++++------------ .../daal4py_Distributed_Kmeans/License.txt | 22 ++++------------ .../daal4py_Distributed_Kmeans/README.md | 7 ++--- .../License.txt | 22 ++++------------ .../README.md | 7 ++--- .../daal4py_Getting_Started/License.txt | 22 ++++------------ .../oneDAL/daal4py_Getting_Started/README.md | 26 +++++++++++-------- 7 files changed, 43 insertions(+), 85 deletions(-) diff --git a/Libraries/oneDAL/License.txt b/Libraries/oneDAL/License.txt index 619419dbbd..a3ab05efce 100644 --- a/Libraries/oneDAL/License.txt +++ b/Libraries/oneDAL/License.txt @@ -1,20 +1,8 @@ - -Copyright (c) 2020, Intel Corporation. All rights reserved. +Copyright Intel Corporation -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +© 2020 GitHub, Inc. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt b/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt index 619419dbbd..a3ab05efce 100755 --- a/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt @@ -1,20 +1,8 @@ - -Copyright (c) 2020, Intel Corporation. All rights reserved. +Copyright Intel Corporation -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +© 2020 GitHub, Inc. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md index 82d6468fec..89675ab543 100755 --- a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md @@ -9,10 +9,11 @@ This sample code shows how to train and predict with a distributed k-means model | What you will learn | distributed oneDAL K-Means programming model for Intel CPU | Time to complete | 5 minutes -## What You Will Learn +## Purpose -* How to run a distributed K-Means model with oneDAL daal4py library memory objects. -* How to train a model and save the information to a file +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. + +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. ## Key implementation details 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). diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt index 619419dbbd..a3ab05efce 100755 --- a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt @@ -1,20 +1,8 @@ - -Copyright (c) 2020, Intel Corporation. All rights reserved. +Copyright Intel Corporation -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +© 2020 GitHub, Inc. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md index 9b117574b5..c5ba67c805 100755 --- a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md @@ -9,10 +9,11 @@ This sample code shows how to train and predict with a distributed linear regres | What you will learn | distributed oneDAL Linear Regression programming model for Intel CPU | Time to complete | 5 minutes -## What You Will Learn +## Purpose -* How to run a distributed Linear model with oneDAL daal4py library memory objects. -* How to train a model and save the information to a file +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. + +In this sample you will run a distributed Linear Regression model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file. ## Key implementation details This distributed linear regression 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](https://software.intel.com/en-us/oneapi/ai-kit). diff --git a/Libraries/oneDAL/daal4py_Getting_Started/License.txt b/Libraries/oneDAL/daal4py_Getting_Started/License.txt index 619419dbbd..a3ab05efce 100644 --- a/Libraries/oneDAL/daal4py_Getting_Started/License.txt +++ b/Libraries/oneDAL/daal4py_Getting_Started/License.txt @@ -1,20 +1,8 @@ - -Copyright (c) 2020, Intel Corporation. All rights reserved. +Copyright Intel Corporation -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE -AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER -LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, -OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. +© 2020 GitHub, Inc. \ No newline at end of file diff --git a/Libraries/oneDAL/daal4py_Getting_Started/README.md b/Libraries/oneDAL/daal4py_Getting_Started/README.md index ebbd6f871f..0c5b25c4be 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/README.md +++ b/Libraries/oneDAL/daal4py_Getting_Started/README.md @@ -9,37 +9,38 @@ This Getting Started sample code show how to do batch linear regression using th | What you will learn | basic oneDAL programming model for Intel CPU | Time to complete | 5 minutes -## What You Will Learn +## Purpose -* How to run a batch linear regression model with oneDAL daal4py library memory objects. -* How to train a model and save the information to a file +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. + +In this sample you will run a batch Linear Regression model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file. -## Key implementation details +## Key Implementation Details This Getting Started 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). ## License This code sample is licensed under MIT license -## How to Build and Run +## Building daal4py for CPU oneAPI Data Analytics Library 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 [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. -## Activate conda environment With Root Access +### 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: -#### Linux +#### On a Linux* System ``` source activate base ``` -## Activate conda environment Without Root Access (Optional) +### Activate conda environment Without Root Access (Optional) By default, the Intel AI Analytics Toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: -#### Linux +#### On a Linux* System ``` conda create --name user_base --clone base ``` @@ -50,7 +51,7 @@ Then activate your conda environment with the following command: source activate user_base ``` -### Install _Jupyter Notebook_ +### _Install Jupyter Notebook_ Launch Jupyter Notebook in the directory housing the code example @@ -67,6 +68,9 @@ Launch Jupyter Notebook in the directory housing the code example ``` jupyter notebook ``` +## Running the Sample + +### Running the Sample as a Jupyter Notebook Open .pynb file and run cells in Jupyter Notebook using the "Run" button (see image) @@ -102,7 +106,7 @@ Here is one of our loaded model's features: ``` -#### _Run as Python File_ +### Running the Sample as a Python File Open notebook in Jupyter and download as python file From a6c6591753e375552a1ac991a2814d01bb9c787d Mon Sep 17 00:00:00 2001 From: "Oberman, Rachel" Date: Thu, 20 Aug 2020 16:11:08 -0700 Subject: [PATCH 08/13] modifying READMEs --- .../daal4py_Distributed_Kmeans/README.md | 20 ++++++++--------- .../README.md | 22 ++++++++++--------- .../oneDAL/daal4py_Getting_Started/README.md | 4 ++-- 3 files changed, 24 insertions(+), 22 deletions(-) diff --git a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md index 89675ab543..208d4a7dfe 100755 --- a/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md @@ -1,4 +1,4 @@ -# daal4py Distributed Kmeans Sample +# daal4py Distributed K-Means 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). | Optimized for | Description @@ -15,7 +15,7 @@ daal4py is a simplified API to Intel® DAAL that allows for fast usage of the fr 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. -## Key implementation details +## Key Implementation Details 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). ## Additional Requirements @@ -24,26 +24,26 @@ You will need a working MPI library. We recommend to use Intel(R) MPI, which is ## License This code sample is licensed under MIT license -## How to Build and Run +## Building daal4py for CPU oneAPI Data Analytics Library 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 [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. -## Activate conda environment With Root Access +### 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: -#### Linux +#### On a Linux* System ``` source activate base ``` -## Activate conda environment Without Root Access (Optional) +### Activate conda environment Without Root Access (Optional) By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: -#### Linux +#### On a Linux* System ``` conda create --name user_base --clone base ``` @@ -54,13 +54,13 @@ Then activate your conda environment with the following command: source activate user_base ``` -## Install Jupyter Notebook +### Install Jupyter Notebook ``` conda install jupyter nb_conda_kernels ``` -#### _View in Jupyter Notebook_ +#### View in Jupyter Notebook _Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ @@ -70,7 +70,7 @@ Launch Jupyter Notebook in the directory housing the code example jupyter notebook ``` -#### _Run as Python File_ +### Running the Sample as a Python File When using daal4py for distributed memory systems, the command needed to execute the program should be executed in a bash shell. To execute this example, run the following command, where the number **4** is chosen as an example and means that it will run on **4 processes**: diff --git a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md index c5ba67c805..19a0633325 100755 --- a/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md @@ -1,4 +1,4 @@ -# Intel Python daal4py Distributed LinearRegression +# daal4py Distributed Linear Regression 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 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 @@ -15,7 +15,7 @@ daal4py is a simplified API to Intel® DAAL that allows for fast usage of the fr In this sample you will run a distributed Linear Regression model with oneDAL daal4py library memory objects. You will also learn how to train a model and save the information to a file. -## Key implementation details +## Key Implementation Details This distributed linear regression 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](https://software.intel.com/en-us/oneapi/ai-kit). @@ -25,27 +25,27 @@ You will need a working MPI library. We recommend to use Intel(R) MPI, which is ## License This code sample is licensed under MIT license -## How to Build and Run +## Building daal4py for CPU oneAPI Data Analytics Library 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 [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. -## Activate conda environment With Root Access +### 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 `~/intel/inteloneapi`. Intel Python environment will be activte by default. However, if you activated another environment, you can return with the following command: -#### Linux +#### On a Linux* System ``` source activate base ``` -## Activate conda environment Without Root Access (Optional) +### Activate conda environment Without Root Access (Optional) By default, the Intel AI Analytics toolkit is installed in the inteloneapi folder, which requires root privileges to manage it. If you would like to bypass using root access to manage your conda environment, then you can clone your desired conda environment using the following command: -#### Linux +#### On a Linux* System ``` conda create --name user_base --clone base ``` @@ -56,12 +56,12 @@ Then activate your conda environment with the following command: source activate user_base ``` -## Install Jupyter Notebook +### Install Jupyter Notebook ``` conda install jupyter nb_conda_kernels ``` -#### _View in Jupyter Notebook_ +#### View in Jupyter Notebook _Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ @@ -71,7 +71,9 @@ Launch Jupyter Notebook in the directory housing the code example jupyter notebook ``` -#### _Run as Python File_ +## Running the Sample + +### Running the Sample as a Python File When using daal4py for distributed memory systems, the command needed to execute the program should be executed in a bash shell. To execute this example, run the following command, where the number **4** is chosen as an example and means that it will run on **4 processes**: diff --git a/Libraries/oneDAL/daal4py_Getting_Started/README.md b/Libraries/oneDAL/daal4py_Getting_Started/README.md index 0c5b25c4be..8267be8bfb 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/README.md +++ b/Libraries/oneDAL/daal4py_Getting_Started/README.md @@ -51,7 +51,7 @@ Then activate your conda environment with the following command: source activate user_base ``` -### _Install Jupyter Notebook_ +### Install Jupyter Notebook Launch Jupyter Notebook in the directory housing the code example @@ -59,7 +59,7 @@ Launch Jupyter Notebook in the directory housing the code example conda install jupyter nb_conda_kernels ``` -#### _View in Jupyter Notebook_ +#### View in Jupyter Notebook _Note: This distributed execution cannot be launched from the jupyter notebook version, but you can still view inside the notebook to follow the included write-up and description._ From 57f4228159eb72991e0d62eecd7771c39fe094ab Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 16:51:31 -0700 Subject: [PATCH 09/13] modifying for ci --- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index 19a420f632..f1d6fcd58e 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -21,7 +21,7 @@ { "id": "d4p_GS_ipynb", "steps": [ - "runipy daal4py_GettingStarted.ipynb" + "python daal4py_GettingStarted.ipynb" ] } From a30e5a44646a6cb381872bad07b82279d2747e9a Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 17:05:53 -0700 Subject: [PATCH 10/13] modifying for ci again --- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index f1d6fcd58e..e8f0b743b8 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -19,10 +19,10 @@ ] }, { - "id": "d4p_GS_ipynb", + "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], + "id": "d4p_GS_ipynb", "steps": [ "python daal4py_GettingStarted.ipynb" - ] } ] From 970acdb2c6fae6927566ca30a53ce51d97695afe Mon Sep 17 00:00:00 2001 From: "rachel.oberman" Date: Thu, 20 Aug 2020 17:09:29 -0700 Subject: [PATCH 11/13] fixing sample.json again --- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index e8f0b743b8..fd38a716a3 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -22,7 +22,7 @@ "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], "id": "d4p_GS_ipynb", "steps": [ - "python daal4py_GettingStarted.ipynb" + "runipy daal4py_GettingStarted.ipynb" ] } ] From 727c6b8e3de7acc6906fad054ec523d8dbcbd199 Mon Sep 17 00:00:00 2001 From: "Oberman, Rachel" Date: Fri, 21 Aug 2020 06:14:38 -0700 Subject: [PATCH 12/13] more mods for ci --- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index fd38a716a3..e8f0b743b8 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -22,7 +22,7 @@ "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], "id": "d4p_GS_ipynb", "steps": [ - "runipy daal4py_GettingStarted.ipynb" + "python daal4py_GettingStarted.ipynb" ] } ] From 5d391b561ee92c7d7ec39e055bc22241da730476 Mon Sep 17 00:00:00 2001 From: "Oberman, Rachel" Date: Fri, 21 Aug 2020 13:01:22 -0700 Subject: [PATCH 13/13] ci edit --- Libraries/oneDAL/daal4py_Getting_Started/sample.json | 10 +--------- 1 file changed, 1 insertion(+), 9 deletions(-) diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json index e8f0b743b8..2a255231ce 100755 --- a/Libraries/oneDAL/daal4py_Getting_Started/sample.json +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -14,15 +14,7 @@ "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], "id": "d4p_GS_py", "steps": [ - "python daal4py_GettingStarted.py" - - ] - }, - { - "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], - "id": "d4p_GS_ipynb", - "steps": [ - "python daal4py_GettingStarted.ipynb" + "python daal4py_GettingStarted.py" ] } ]