The Best PyTorch Lightning Alternatives in 2026

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.

Determined manages distributed model training, hyperparameter tuning, experiment tracking, and resources.

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

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

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

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

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

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

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

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

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

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

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

FEDOT automates machine learning model development through its modeling framework.

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

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

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

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

DeepSpeed optimizes distributed training and inference for deep learning models.

Bender helps developers build neural networks for iOS using TensorFlow models and Metal.

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

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

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