xgboost.readthedocs.io · ML Frameworks
Gradient boosted decision tree library for tabular data, with a scikit learn style Python API and CUDA-accelerated training and prediction.
XGBoost is one of 20 ML framework tools on Axiomi. See all ML framework tools or how we check facts.
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The best ML frameworks for Python in 2026, ranked by job: PyTorch, scikit-learn, JAX, Keras 3, XGBoost, LightGBM, TensorFlow, MLX and more, with versions.
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LightGBMlightgbm.readthedocs.ioMicrosoft's tree based gradient boosting framework, designed for fast training and low memory use, with parallel, distributed and GPU learning.ML FrameworksOpen source★ 18.9k
scikit-learnscikit-learn.orgPython's standard library for classical machine learning: one fit/predict interface for linear models, trees, clustering, preprocessing, cross validation and metrics.ML FrameworksOpen source★ 68k
vLLMvllm.aiHigh throughput open source inference and serving engine built around PagedAttention and continuous batching, with an OpenAI compatible server that scales from one GPU to clusters.ML FrameworksOpen source★ 95.7k
PyTorchpytorch.orgThe dominant deep learning framework in research, with eager execution, torch.compile and a huge ecosystem.ML FrameworksOpen source★ 105.1k
Keraskeras.ioHigh level deep learning API that runs on JAX, TensorFlow or PyTorch backends.ML FrameworksOpen source★ 64.4k
SGLangsglang.ioOpen source (Apache 2.0) server for language, vision and diffusion models. RadixAttention reuses KV cache across prompts that share a prefix; v0.5.22 is current as of October 2026.ML FrameworksOpen source★ 38.3k