diff --git a/Libraries/oneDAL/License.txt b/Libraries/oneDAL/License.txt new file mode 100644 index 0000000000..a3ab05efce --- /dev/null +++ b/Libraries/oneDAL/License.txt @@ -0,0 +1,8 @@ +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: + +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. +© 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 new file mode 100755 index 0000000000..a3ab05efce --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/License.txt @@ -0,0 +1,8 @@ +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: + +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. +© 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 new file mode 100755 index 0000000000..208d4a7dfe --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_Kmeans/README.md @@ -0,0 +1,112 @@ +# 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 +| :--- | :--- +| 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 + +## Purpose + +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). + +## 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 + +## 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 + +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: + +#### On a Linux* System +``` +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: + +#### On a Linux* System +``` +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 +``` + +### 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**: + +Run the Program + +`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, printed 4 times): +``` + + +Here our centroids: + + + [[ 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 centroids loaded from file: + + [[ 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: + + [[1] + [1] + [1] + [1] + [1]] +[CODE_SAMPLE_COMPLETED_SUCCESFULLY] + +``` + + 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": "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_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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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_Kmeans/sample.json b/Libraries/oneDAL/daal4py_Distributed_Kmeans/sample.json new file mode 100755 index 0000000000..ca263dac01 --- /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"], + "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_Kmeans.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..a3ab05efce --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/License.txt @@ -0,0 +1,8 @@ +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: + +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. +© 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 new file mode 100755 index 0000000000..19a0633325 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/README.md @@ -0,0 +1,119 @@ +# 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 +| :--- | :--- +| 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 + +## Purpose + +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). + + +## 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 + +## 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 + +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: + +#### On a Linux* System +``` +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: + +#### On a Linux* System +``` +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 +``` + +## 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**: + +Run the Program + +`mpirun -n 4 python ./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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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_Distributed_LinearRegression/sample.json b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json new file mode 100755 index 0000000000..999b8a7180 --- /dev/null +++ b/Libraries/oneDAL/daal4py_Distributed_LinearRegression/sample.json @@ -0,0 +1,22 @@ +{ + "guid": "ED2952EA-04CB-4353-9FE6-80E0F7DCA098", + "name": "daal4py Distributed Linear Regression", + "categories": ["Toolkit/Intel® AI Analytics Toolkit/oneDAL"], + "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"], + "os":["linux"], + "targetDevice": ["CPU"], + "ciTests": { + "linux": [ + { + "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], + "id": "d4p_Linear_Regression_Dist", + "steps": [ + "mpirun -n 4 python ./daal4py_Distributed_LinearRegression.py" + ] + } + ] +} +} 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 0000000000..4f54045950 Binary files /dev/null and b/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Run.jpg differ diff --git a/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Save_Py.jpg b/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Save_Py.jpg new file mode 100755 index 0000000000..f4248cddbb Binary files /dev/null and b/Libraries/oneDAL/daal4py_Getting_Started/Jupyter_Save_Py.jpg differ diff --git a/Libraries/oneDAL/daal4py_Getting_Started/License.txt b/Libraries/oneDAL/daal4py_Getting_Started/License.txt new file mode 100644 index 0000000000..a3ab05efce --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/License.txt @@ -0,0 +1,8 @@ +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: + +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. +© 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 new file mode 100755 index 0000000000..8267be8bfb --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/README.md @@ -0,0 +1,149 @@ +# 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 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 | 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 + +## Purpose + +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 +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 + +## 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 + +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: + +#### On a Linux* System +``` +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: + +#### On a Linux* System +``` +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 +``` +## 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) + +![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] +``` + + +### Running the Sample as a 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 = ",") +print("[CODE_SAMPLE_COMPLETED_SUCCESFULLY]") + 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 diff --git a/Libraries/oneDAL/daal4py_Getting_Started/sample.json b/Libraries/oneDAL/daal4py_Getting_Started/sample.json new file mode 100755 index 0000000000..2a255231ce --- /dev/null +++ b/Libraries/oneDAL/daal4py_Getting_Started/sample.json @@ -0,0 +1,22 @@ +{ + "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"], + "os":["linux"], + "targetDevice": ["CPU"], + "ciTests": { + "linux": [ + { + "env": ["source /opt/intel/oneapi/setvars.sh --force", "source activate base"], + "id": "d4p_GS_py", + "steps": [ + "python daal4py_GettingStarted.py" + ] + } + ] +} +}