{"categories":[{"label":"Libraries","url":"https://skillfed.io/packages/category/software-development-libraries/8"}],"enrichment":{"capability":"oneDAL is a C++ and DPC++ library that implements accelerated machine learning routines for tabular data (linear regression, K-means clustering, random forests, etc.) for CPUs, GPUs, and distributed setups, with a Python interface powered by tbb.","skillfed_tags":["accelerated-ml","distributed-computing","gpu-support"],"use_cases":["Accelerate scikit-learn pipelines by installing the Extension for Scikit-learn to call oneDAL routines transparently.","Perform distributed K-means clustering and other algorithms across multi-node setups using oneDAL's MPI support.","Speed up linear regression, random forest, and other tabular ML tasks on modern CPUs via SIMD and cache optimization.","Leverage GPU acceleration for ML workloads on systems with SYCL-compatible GPUs using oneDAL's DPC++ interfaces.","Integrate oneDAL C++ routines into Python data science applications for performance-critical tabular data processing."],"what_it_does":"oneDAL is Intel's oneAPI Data Analytics Library packaged for Python, providing optimized implementations of common machine learning algorithms (regression, clustering, forests) that run on CPUs, GPUs, and distributed clusters. It accelerates computation by leveraging SIMD instructions and cache optimization on CPUs, and SYCL/oneMKL on GPUs. The library is part of the UXL Foundation and is typically used either directly via its C++ interfaces or indirectly through the Extension for Scikit-learn, which patches scikit-learn to call oneDAL behind the scenes.\n\nThe Python package depends on tbb (Intel Threading Building Blocks) for parallelization and is distributed as platform-specific wheels for x86_64 Linux (manylinux_2_28) and Windows. Installation has medium friction due to these binary constraints. The library is actively maintained (last release 2026-06-10, repository last commit 2026-08-14) and is classified as Production/Stable, making it suitable for production workloads where tabular ML performance is critical.","worth_installing":"Yes, with conditions. oneDAL is actively maintained, production-stable, and offers genuine performance benefits for tabular machine learning on CPUs and GPUs. However, the unclear Intel Simplified Software License requires verification before commercial use, and installation is limited to x86_64 Windows or manylinux_2_28 Linux. Most users will benefit more from the Extension for Scikit-learn (which uses oneDAL transparently) than from direct use of this package."},"id":"daal","links":{"html":"https://skillfed.io/packages/daal","md":"https://skillfed.io/packages/daal.md","pypi":"https://pypi.org/project/daal/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-10","license_spdx":null,"license_treatment":"unclear","name":"daal","python_support":"unspecified","summary":"Intel\u00ae oneAPI Data Analytics Library"},"popularity":{"monthly_downloads":170425,"position":10396,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2026.1.0"}