Intel® oneAPI Data Analytics Library
Deploy High-Performance Data Science on CPUs and GPUs
Maximum Calculation Performance
- Provides the right tools to build compute-intense applications that run fast on Intel® architecture
- Optimized for CPUs and GPUs
High-Speed Algorithms
- Includes algorithms for analysis functions, math functions, and training and library prediction functions for C++
- Used to optimize algorithms from popular machine learning Python* libraries, such as XGBoost (made available as part of AI Tools from Intel)
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What You Can Do
- Analyze large datasets with available compute resources.
- Make better predictions faster.
- Optimize data ingestion and algorithmic compute simultaneously.
- Support offline, streaming, and distributed usage models.
Intel® oneAPI Data Analytics Library (oneDAL) is available as part of the Intel® oneAPI Base Toolkit.
Download as Part of the Toolkit
oneDAL is included as part of the Intel oneAPI Base Toolkit, which is a core set of tools and libraries for developing high-performance, data-centric applications across diverse architectures.
Download the Stand-Alone Version
A stand-alone download of oneDAL is available. You can download binaries from Intel or choose your preferred repository.
Develop in the Cloud
Build and optimize oneAPI multiarchitecture applications using the latest Intel-optimized oneAPI and AI tools, and test your workloads across Intel® CPUs and GPUs. No hardware installations, software downloads, or configuration necessary.
Help oneDAL Evolve
oneDAL is part of the open oneAPI industry standards initiative. We welcome you to participate.
Features
Performance and Portability
- To ensure maximum calculation speed, each function is highly tuned to the instruction set, vector width, core count, and memory architecture of each target CPU or GPU.
- See performance benefits for a wide range of applications—from IoT gateways to back-end servers.
- Work in the language you are most familiar with and get maximum performance in your application with integrated Python, SYCL*, and C++ support.
In-Depth Algorithm Support
Supported Algorithms:
- Apriori for Association Rules Mining
- Correlation and Variance-Covariance Matrices
- Decision Forest for Classification and Regression
- Expectation-Maximization Using a Gaussian Mixture Model (EM-GMM)
- Gradient Boosted Trees (GBT) for Classification and Regression
- Alternating Least Squares (ALS) for Collaborative Filtering
- Multinomial Naïve Bayes Classifier
- Multiclass Classification Using a One-Against-One Strategy
- Limited-Memory BFGS (L-BFGS) Optimization Solver
- Logistic Regression with L1 and L2 regularization support
- Limited-Memory BFGS (L-BFGS) Optimization Solver
- Linear Regression
Supported CPU & GPU Algorithms via SYCL Interfaces:
- K-Means Clustering
- K-Nearest Neighbor (KNN)
- Support Vector Machines (SVM) with Linear and Radial Basis Function (RBF) Kernels
- Principal Components Analysis (PCA)
- Density-based Special Clustering of Applications with Noise (DBSCAN)
- Random Forest
Benchmarks
This library addresses all stages of the data analytics pipeline: preprocessing, transformation, analysis, modeling, validation, and decision-making.
Documentation & Code Samples
Documentation
Code Samples
Get Started
Intel® Extension for Scikit-learn* for Python
Learn how to use machine learning algorithms that are available with scikit-learn*.
Learn how to train a model and save the information to a file using daal4py—a Python API for oneDAL.
Distributed daal4py CPU Computations
Distributed Linear Regression Training and Prediction
Use this sample to train and predict with a distributed linear regression model.
Distributed K-means Training and Prediction
See how to train and predict with a distributed K-means model.
Master oneAPI C++ and SYCL* Interfaces
Use this set of examples to learn about oneAPI interfaces without SYCL support.
DPC++ (a oneAPI Implementation of SYCL) Examples
Use this set of examples to learn about oneAPI interfaces with SYCL support.
How to work with code samples:
Training
Machine Learning Workflow Optimization
Optimize End-to-End Data Science and Machine Learning Acceleration [01:09:50]
Steps to Optimize End-to-End Machine Learning Workflows [57:42]
XGBoost Performance
XGBoost with Intel® Optimizations Webinar [52:22]
Improve the Performance of XGBoost and LightGBM Inference with oneDAL
Efficient Data Analysis with scikit-learn
Save Time and Money with Intel® Extension for Scikit-learn*
Specifications
Processors:
- Intel® Core™ processors
- Intel® Xeon® processors
GPUs:
- Intel® Processor Graphics Gen9 and above
- Xe Architecture
Operating systems:
- Linux
- Windows
- macOS*
For more information, see the system requirements.
Compilers:
- Intel® oneAPI DPC++/C++ Compiler
- Intel® C++ Compiler
- GNU Compiler Collection (GCC)* on Linux
- Microsoft Visual C++ Compiler* on Windows
- Clang on macOS
Languages:
- SYCL
Note Must have Intel oneAPI Base Toolkit installed
- C++
- Python
Get Help
Your success is our success. Access these support resources when you need assistance.
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