> ML_LIBRARIES_CATALOG_v1.0

Qualified ML Libraries

Every library is independently qualified with primary citations, supported version ranges, hardware accelerator separation, and real-world failure patterns.

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Showing 18 of 161 Qualified Libraries & Tools (Page 1 of 9)Accelerator Mappings & Failure Modes

numerical-data-foundations

2.1.1BSD-3-Clause

NumPy

NumPy Developers / NumFOCUS

The fundamental package for scientific computing with Python.

Training:

CPU

Inference:

CPUWASM

#tabular#time-series

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numerical-data-foundations

1.14.1BSD-3-Clause

SciPy

SciPy Community / NumFOCUS

Fundamental algorithms for scientific computing in Python.

Training:

CPU

Inference:

CPUWASM

#tabular#time-series

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numerical-data-foundations

2.2.3BSD-3-Clause

pandas

pandas Community / NumFOCUS

Flexible and powerful data analysis and manipulation library for Python.

Training:

CPU

Inference:

CPUWASM

#tabular#time-series

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numerical-data-foundations

1.8.2MIT

Polars

Polars / Open Source

Blazingly fast DataFrames powered by a multi-threaded Rust query engine.

Training:

CPU

Inference:

CPUWASM

#tabular#time-series

Docs Deep Dive

numerical-data-foundations

17.0.0Apache-2.0

Apache Arrow

Apache Software Foundation

Universal columnar in-memory data format and zero-copy transport layer.

Training:

CPUCUDA

Inference:

CPUCUDAWASM

#tabular

Docs Deep Dive

distributed-computation

2024.9.0BSD-3-Clause

Dask

Dask Community / NumFOCUS

Flexible library for parallel computing and distributed scaling in Python.

Training:

CPUCUDA

Inference:

CPUCUDA

#tabular#time-series

Docs Deep Dive

distributed-computation

2.37.0Apache-2.0

Ray

Anyscale / UC Berkeley RISELab

Unified framework for scaling AI and Python workloads from training to serving.

Training:

CPUCUDAROCMTPU

Inference:

CPUCUDAROCM

#tabular#text#image#multimodal

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distributed-computation

3.5.3Apache-2.0

Apache Spark MLlib

Apache Software Foundation

Scalable machine learning library for distributed big data clusters.

Training:

CPUCUDA

Inference:

CPUCUDA

#tabular

Docs Deep Dive

numerical-data-foundations

24.08.0Apache-2.0

RAPIDS cuDF

NVIDIA / RAPIDS

GPU DataFrame library for loading, joining, and manipulating tabular data on NVIDIA GPUs.

Training:

CPUCUDA

Inference:

CPUCUDA

#tabular

Docs Deep Dive

Classical ML

24.08.0Apache-2.0

RAPIDS cuML

NVIDIA / RAPIDS

GPU-accelerated suite of machine learning algorithms matching the scikit-learn API.

Training:

CUDA

Inference:

CUDA

#tabular

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numerical-data-foundations

1.1.1MIT

DuckDB

DuckDB Foundation / DuckDB Labs

In-process SQL OLAP database management system for machine learning feature engineering.

Training:

CPU

Inference:

CPUWASM

#tabular

Docs Deep Dive

Classical ML

1.5.2BSD-3-Clause

scikit-learn

scikit-learn Consortium / Inria

Simple and efficient tools for predictive data analysis.

Training:

CPU

Inference:

CPU

#tabular#text

Docs Deep Dive

Classical ML

2.1.1Apache-2.0

XGBoost

DMLC (Distributed Machine Learning Community)

Scalable, Portable and Distributed Gradient Boosting Library.

Training:

CPUCUDAROCM

Inference:

CPUCUDAROCM

#tabular

Docs Deep Dive

Classical ML

4.5.0MIT

LightGBM

Microsoft

A fast, distributed, high performance gradient boosting framework.

Training:

CPUCUDA

Inference:

CPUCUDA

#tabular

Docs Deep Dive

Classical ML

1.2.25Apache-2.0

CatBoost

Yandex / Open Source

Fast, scalable, high performance gradient boosting on decision trees with native categorical handling.

Training:

CPUCUDAROCM

Inference:

CPUCUDAROCM

#tabular#text

Docs Deep Dive

Classical ML

0.12.4MIT

imbalanced-learn

scikit-learn-contrib

Toolkit for classification with severely imbalanced datasets.

Training:

CPU

Inference:

CPU

#tabular

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Classical ML

0.21.2BSD-3-Clause

River

River Community / Open Source

Online machine learning in Python for streaming and concept drift.

Training:

CPU

Inference:

CPU

#tabular#time-series

Docs Deep Dive

Classical ML

9.10.0BSD-3-Clause

Vowpal Wabbit

Microsoft Research / DMLC

Ultra-fast machine learning system for contextual bandits and massive streaming data.

Training:

CPU

Inference:

CPU

#tabular#text

Docs Deep Dive