The Best Determined Alternatives in 2026

PyTorch Lightning helps pretrain and fine-tune AI models across one or many GPUs with minimal code changes.

doddle-model is an in-memory machine learning library for Scala, built on Breeze with a scikit-learn-like API using typeclasses.

AutoGluon provides tools for automated machine learning and model development.

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

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

metric-learn provides Python implementations of metric learning algorithms.

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

Knet provides a deep learning framework developed at Koç University for building models.

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

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

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

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

DeepSpeed optimizes distributed training and inference for deep learning models.

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

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

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

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

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

This repository implements DreamBooth subject fine-tuning for Stable Diffusion using textual inversion.

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

FEDOT automates machine learning model development through its modeling framework.

This repository implements random forest models in Common Lisp for classification tasks.