The Best ParaMonte Alternatives in 2026

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

Use Learnergy, a Python library for training and working with energy-based machine learning models.

FEDOT automates machine learning model development through its modeling framework.

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.

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

hyperopt-sklearn uses hyperparameter optimization to select machine learning algorithms for scikit-learn.

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.

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

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

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

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

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

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

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.

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

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.

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