The Best Jax Alternatives in 2026

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

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

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

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

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.

BayesO provides a Python package for Bayesian optimization algorithms and related modeling tasks.

Opytimizer provides Python implementations of metaheuristic optimization algorithms for search problems.

pomegranate provides flexible probabilistic modeling tools and algorithms for Python users.

OpenMetricLearning provides metric learning pipelines, models, and a model zoo.

Hugging Face Accelerate launches and trains PyTorch models across devices and distributed configurations.

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

TensorFlow Model Optimization provides Keras and TensorFlow tools for model quantization and pruning.

Octo provides a transformer-based policy for robots, with code to train and fine-tune it.

FEDOT automates machine learning model development through its modeling framework.

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

LightlyTrain supports pretraining, fine-tuning, and distillation for computer vision models.

P-tuning v2 is a deep prompt tuning strategy designed as an alternative to full model fine-tuning.

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

Katib automates machine learning on Kubernetes, including hyperparameter tuning and neural architecture search.

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

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

Scikit-Opt provides genetic algorithms, particle swarm optimization, simulated annealing, ant colony optimization, and other search algorithms.