The Best Katib Alternatives in 2026

AutoKeras automates development of deep learning models through a machine learning library.

AutoGL provides automated graph learning through a machine learning framework and toolkit.

Archai supports neural architecture search with fast, reproducible, and modular research tools.

autoai automates regression and classification modeling in Python through model search and hyperparameter tuning.

KerasTuner searches hyperparameters to optimize machine learning models built with Keras.

FEDOT automates machine learning model development through its modeling framework.

Guild AI automates and optimizes machine learning experiments while tracking each run.

PyTorch Geometric Temporal provides models and tools for spatiotemporal signal processing with neural learning.

PyTorch Lightning extension that accelerates and enhances foundation model experimentation with flexible fine-tuning schedules.

Learnergy is a Python library for energy-based machine learning models.

Enables single machine or distributed training and evaluation of deep learning models.

Detecto supports building and training computer vision models with the PyTorch machine learning framework.

ML_Forgex trains, evaluates, and predicts with machine learning models on cleaned datasets.

Auto_ViML builds multiple machine learning models with a single line of code.

Lightwood builds and customizes machine learning pipelines using a declarative JSON-AI syntax.

m2cgen converts machine learning models into native code for multiple programming languages without runtime dependencies.

EvalML builds, optimizes, and evaluates machine learning pipelines with automated data checks and objective functions.

numpy-ml collects machine learning algorithms in NumPy, presented as an intentionally readable Python package.

Karate Club offers algorithms for unsupervised learning on graph data in Python.

This metric-learn repository contains Python implementations of metric learning algorithms.

metric-learn provides Python implementations of metric learning algorithms.

ML-Ensemble provides Python tools for high-performance ensemble learning with scikit-learn.

DeepSpeed optimizes distributed training and inference for deep learning models.

This Scikit-learn extension accelerates compatible applications through optimized implementations.