daal4py
daal4py is a Convenient Python API to the Intel® oneAPI Data Analytics Library (oneDAL)
Decision gist · record as of 2026-08-14
Yes, if you need accelerated implementations of specific algorithms (SVM, linear models, K-means) and run on Linux or Windows x86_64. Medium install friction and a stable but slowly-evolving codebase are acceptable tradeoffs for the performance gains. Not recommended if you rely on scikit-learn patching—use Intel Extension for Scikit-learn instead. No known vulnerabilities.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Linux (manylinux1) or Windows x86_64; macOS and other architectures not supported via PyPI wheels.
- Requires Python >=3.7.
- Medium install friction due to platform-specific wheels (manylinux1 and Windows x86_64 only).
License · maintenance · safety
Apache v2.0 (permissive) — Apache v2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
last release 2024-09-17 (696 days) · last repo commit 2026-08-12 · 1,355 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 132,226 downloads/mo, #11,561 on PyPI
Alternatives
Verify before relying
pip install daal4py
import daal4py as d4p
from daal4py.sklearn.svm import SVC
# Use optimized SVM classifier
clf = SVC(kernel='rbf')
clf.fit(X_train, y_train)- Whether GPU acceleration is available in current PyPI wheels or only through conda channels
- Performance gains over standard scikit-learn for specific algorithm classes
- Current status of scikit-learn patching after deprecation and move to separate package
What it is and what it does
daal4py wraps Intel's oneAPI Data Analytics Library (oneDAL) to provide optimized implementations of machine learning algorithms accessible through a Python API. It targets data scientists and ML practitioners who want to accelerate computationally intensive operations like SVM, linear models, and K-means clustering without rewriting their code. The package depends on daal (the underlying C++ library) and numpy, and is distributed as pre-built wheels for Python 3.9–3.12 on Linux (manylinux1) and Windows (x86_64).
Historically, daal4py offered scikit-learn patching to transparently accelerate scikit-learn calls, but that functionality has been deprecated and moved to a separate package (Intel Extension for Scikit-learn). Current users should treat daal4py as a direct API to oneDAL algorithms rather than a drop-in scikit-learn accelerator.
Use it for
- Accelerate SVM training and inference on large datasets where CPU optimization is a bottleneck.
- Speed up linear regression, logistic regression, and ridge regression on high-dimensional data.
- Optimize K-means clustering performance for exploratory data analysis on large point clouds.
- Integrate oneDAL algorithms directly into custom ML pipelines that don't rely on scikit-learn.
- Benchmark performance of Intel-optimized algorithms against standard implementations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need accelerated implementations of specific algorithms (SVM, linear models, K-means) and run on Linux or Windows x86_64.
Medium install friction and a stable but slowly-evolving codebase are acceptable tradeoffs for the performance gains. Not recommended if you rely on scikit-learn patching—use Intel Extension for Scikit-learn instead. No known vulnerabilities.
Install
daal4py on PyPI
Before you install
Medium install friction due to platform-specific wheels (manylinux1 and Windows x86_64 only). Active maintenance with recent commits; last release 696 days ago suggests a stable but not rapidly evolving codebase.
Requires Linux (manylinux1) or Windows x86_64; macOS and other architectures not supported via PyPI wheels. Requires Python >=3.7.
License in practice
Apache v2.0 permissive license allows commercial and private use with minimal restrictions; suitable for most projects.
Quickstart
pip install daal4py
import daal4py as d4p
from daal4py.sklearn.svm import SVC
# Use optimized SVM classifier
clf = SVC(kernel='rbf')
clf.fit(X_train, y_train)Verify before relying
- Whether GPU acceleration is available in current PyPI wheels or only through conda channels
- Performance gains over standard scikit-learn for specific algorithm classes
- Current status of scikit-learn patching after deprecation and move to separate package
Package facts
| License | Apache v2.0 permissive |
| Python support | Supports the current Python release >=3.7 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 2 packagesdaalnumpy |
| Maintenance | Actively maintained 696 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 132,226 / month, #11,561 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableEnvironment :: ConsoleIntended Audience :: DevelopersIntended Audience :: Other AudienceIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3Programming Language :: Python :: 3.6Programming Language :: Python :: 3.7Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Topic :: Scientific/EngineeringTopic :: Software DevelopmentTopic :: System |
Evidence: daal4py-2024.7.0-py310-none-manylinux1_x86_64.whl; daal4py-2024.7.0-py310-none-win_amd64.whl; daal4py-2024.7.0-py311-none-manylinux1_x86_64.whl; daal4py-2024.7.0-py311-none-win_amd64.whl; daal4py-2024.7.0-py312-none-manylinux1_x86_64.whl; daal4py-2024.7.0-py312-none-win_amd64.whl; daal4py-2024.7.0-py39-none-manylinux1_x86_64.whl; daal4py-2024.7.0-py39-none-win_amd64.whl
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See also daal · tensorflow-intel · scikit-learn-intelex · mkl · onemkl-sycl-blas · onemkl-sycl-lapack · onemkl-sycl-sparse · onemkl-license · mkl-static · instructure-dap-client