oneAPI Data Analytics Library (oneDAL) is a powerful machine learning library that helps speed up big data analysis. oneDAL solvers are also used in Intel Distribution for Python for scikit-learn optimization.
oneAPI Data Analytics Library is an extension of Intel® Data Analytics Acceleration Library (Intel® DAAL).
oneDAL is part of oneAPI. The current branch implements version 1.1 of oneAPI Specification.
oneDAL uses all capabilities of Intel® hardware, which allows you to get a significant performance boost for the classic machine learning algorithms.
We provide highly optimized algorithmic building blocks for all stages of data analytics: preprocessing, transformation, analysis, modeling, validation, and decision making.
oneDAL also provides Data Parallel C++ (DPC++) API extensions to the traditional C++ interfaces.
The size of the data is growing exponentially as does the need for high-performance and scalable frameworks to analyze all this data and benefit from it. Besides superior performance on a single node, oneDAL also provides distributed computation mode that shows excellent results for strong and weak scaling:
|oneDAL K-Means fit, strong scaling result||oneDAL K-Means fit, weak scaling results|
Technical details: FPType: float32; HW: Intel Xeon Processor E5-2698 v3 @2.3GHz, 2 sockets, 16 cores per socket; SW: Intel® DAAL (2019.3), MPI4Py (3.0.0), Intel® Distribution Of Python (IDP) 3.6.8; Details available in the article https://arxiv.org/abs/1909.11822
Refer to our examples and documentation for more information about our API.
oneDAL has a Python API that is provided as a standalone Python library called daal4py.
The example below shows how daal4py can be used to calculate K-Means clusters:
import numpy as np import pandas as pd import daal4py as d4p data = pd.read_csv("local_kmeans_data.csv", dtype = np.float32) init_alg = d4p.kmeans_init(nClusters = 10, fptype = "float", method = "randomDense") centroids = init_alg.compute(data).centroids alg = d4p.kmeans(nClusters = 10, maxIterations = 50, fptype = "float", accuracyThreshold = 0, assignFlag = False) result = alg.compute(data, centroids)
Data scientists often require different tools for analysis of regular and big data. daal4py offers various processing models, which makes it easy to enable distributed multi-node mode.
import numpy as np import pandas as pd import daal4py as d4p d4p.daalinit() # <-- Initialize SPMD mode data = pd.read_csv("local_kmeans_data.csv", dtype = np.float32) init_alg = d4p.kmeans_init(nClusters = 10, fptype = "float", method = "randomDense", distributed = True) # <-- change model to distributed centroids = init_alg.compute(data).centroids alg = d4p.kmeans(nClusters = 10, maxIterations = 50, fptype = "float", accuracyThreshold = 0, assignFlag = False, distributed = True) # <-- change model to distributed result = alg.compute(data, centroids)
For more details browse daal4py documentation.
You can speed up Scikit-learn using Intel(R) Extension for Scikit-learn*.
Intel(R) Extension for Scikit-learn* speeds up scikit-learn beyond by providing drop-in patching. Acceleration is achieved through the use of the oneAPI Data Analytics Library that allows for fast usage of the framework suited for Data Scientists or Machine Learning users.
|Technical details: HW: c5.24xlarge AWS EC2 Instance using an Intel Xeon Platinum 8275CL with 2 sockets and 24 cores per socket; SW: scikit-learn version 0.24.2, scikit-learn-intelex version 2021.2.3, Python 3.8|
Intel(R) Extension for Scikit-learn* provides an option to replace some scikit-learn methods by oneDAL solvers, which makes it possible to get a performance gain without any code changes. You can patch the stock scikit-learn by using the following command-line flag:
python -m sklearnex my_application.py
Patches can also be enabled programmatically:
from sklearn.svm import SVC from sklearn.datasets import load_digits from time import time svm_sklearn = SVC(kernel="rbf", gamma="scale", C=0.5) digits = load_digits() X, y = digits.data, digits.target start = time() svm_sklearn = svm_sklearn.fit(X, y) end = time() print(end - start) # output: 0.141261... print(svm_sklearn.score(X, y)) # output: 0.9905397885364496 from sklearnex import patch_sklearn patch_sklearn() # <-- apply patch from sklearn.svm import SVC svm_sklearnex = SVC(kernel="rbf", gamma="scale", C=0.5) start = time() svm_sklearnex = svm_sklearnex.fit(X, y) end = time() print(end - start) # output: 0.032536... print(svm_sklearnex.score(X, y)) # output: 0.9905397885364496
For more details browse Intel(R) Extension for Scikit-learn* documentation.
oneDAL provides Scala and Java interfaces that match Apache Spark MlLib API and use oneDAL solvers under the hood. This implementation allows you to get a 3-18X increase in performance compared to the default Apache Spark MLlib.
Technical details: FPType: double; HW: 7 x m5.2xlarge AWS instances; SW: Intel DAAL 2020 Gold, Apache Spark 2.4.4, emr-5.27.0; Spark config num executors 12, executor cores 8, executor memory 19GB, task cpus 8
Check the samples tab for more details.
You can download the specific version of oneDAL or install from sources.
Beside C++ and Python API, oneDAL also provides APIs for DPC++ and Java:
Refer to GitHub Wiki to browse the full list of oneDAL and daal4py resources.
Ask questions and engage in discussions with oneDAL developers, contributers, and other users through the following channels:
You may reach out to project maintainers privately at email@example.com.
To report a vulnerability, refer to Intel vulnerability reporting policy.
Report issues and make feature requests using GitHub Issues.
We welcome community contributions, so check our contributing guidelines to learn more.
Use GitHub Wiki to provide feedback about oneDAL.
Samples are examples of how oneDAL can be used in different applications:
Technical preview features are introduced to gain early feedback from developers. A technical preview feature is subject to change in the future releases. Using a technical preview feature in a production code base is therefore strongly discouraged.
In C++ APIs, technical preview features are located in
oneapi::dal::preview namespaces. In Java APIs, technical preview features are located in packages that have the
com.intel.daal.preview name prefix.
The preview features list:
undirected_adjacency_vector_graph), where vertex indices can only be of type int32
directed_adjacency_vector_graph), where vertex indices can only be of type int32, edge weights can be of type int32 or double
oneAPI Data Analytics Library is an extension of Intel® Data Analytics Acceleration Library (Intel® DAAL).
This repository contains branches corresponding to both oneAPI and classical versions of the library. We encourage you to use oneDAL located under the
|Intel® DAAL||2020 Update 3||rls/daal-2020-u3-rls|
Source Code: https://github.com/oneapi-src/oneDAL
License: Apache-2.0 License
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
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The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
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What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.
To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.
The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:
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For Big Data Analytics, the challenges faced by businesses are unique and so will be the solution required to help access the full potential of Big Data.
Let’s take a look at the Top Big Data Analytics Challenges faced by Businesses and their Solutions.
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Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.
If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?
Let’s take a look at the most important things you need to know.
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