xgboost

XGBoost Python Package

xgboost Permissive license Apache-2.0 Active ★ 28,650 v3.4.0 released 2026-08-04

Install

xgboost on PyPI

pip

pip install xgboost

uv

uv add xgboost

poetry

poetry add xgboost

Package facts

License	Apache-2.0 (permissive)
Python support	supports the current Python release (>=3.12)
Install friction	medium — platform-specific wheel
Runtime dependencies	3 — numpy, scipy, nvidia-nccl-cu13
Maintenance	actively maintained — 9 days since the last release
Last repo commit	2026-08-13
First released	2015-08-20
Popularity	one of the top 1,000 most-downloaded packages on PyPI (30-day window, as of 2026-08-13)
Known vulnerabilities	none known (OSV.dev, checked 2026-08-13)

Evidence: xgboost-3.4.0-py3-none-macosx_10_15_x86_64.whl; xgboost-3.4.0-py3-none-macosx_12_0_arm64.whl; xgboost-3.4.0-py3-none-manylinux_2_28_aarch64.whl; xgboost-3.4.0-py3-none-manylinux_2_28_x86_64.whl; xgboost-3.4.0-py3-none-win_amd64.whl; xgboost-3.4.0-py3-none-win_arm64.whl

Development Status :: 5 - Production/StableOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Typing :: Typed

About xgboost

from the package's own PyPI description — quoted content, verbatim

<img src="https://xgboost.ai/images/logo/xgboost-logo-trimmed.png" width=200/> eXtreme Gradient Boosting

XGBoost-CI (image) Documentation Status (image) GitHub license (image) CRAN Status Badge (image) PyPI version (image) Conda version (image) Optuna (image) Twitter (image) OpenSSF Scorecard (image) [![Open In...

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AI interpretation — verify before relying

AI-generated interpretation of the package facts above; every digit, version, license, or vulnerability id it cites is grounded in the facts already shown on this page

XGBoost is a distributed gradient boosting library that trains tree-based machine learning models for classification, regression, and ranking tasks with high efficiency across single and distributed computing environments.

Medium install friction due to compiled C++ components with platform-specific wheels. Requires numpy, scipy, and nvidia-nccl-cu13. Actively maintained with last commit 2026-08-13 and release 9 days old.

Apache-2.0 permissive license allows commercial use, modification, and distribution with minimal restrictions—suitable for proprietary projects provided you include the license notice.

Usage

pip install xgboost
import xgboost as xgb
model = xgb.XGBClassifier()
model.fit(X_train, y_train)

Requires Python >=3.12; GPU support via nvidia-nccl-cu13 requires NVIDIA CUDA 13 runtime; compiled wheels are platform-specific.

Verdict: XGBoost 3.4.0 is a production-stable, actively maintained gradient boosting framework with no known vulnerabilities and permissive Apache-2.0 licensing. Medium install friction is typical for compiled ML libraries; GPU acceleration is optional via nvidia-nccl-cu13.

Needs verification

Whether GPU support via nvidia-nccl-cu13 is required or optional for typical use cases.
Performance characteristics and memory requirements for large-scale datasets.
Actual compatibility with Kubernetes, Hadoop, Spark versions mentioned in description.

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