The Best KotlinDL Alternatives in 2026

FEDOT automates machine learning model development through its modeling framework.

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

neural-fortran provides a parallel framework for deep learning in Fortran.

AutoGluon provides tools for automated machine learning and model development.

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

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

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

DeepSpeed optimizes distributed training and inference for deep learning models.

Deeplearning4j provides tools for training and deploying deep learning models in the JVM.

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

DI-engine provides an OpenDILab framework for reinforcement learning projects.

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

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

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

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

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.

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.

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

PEFT provides parameter-efficient fine-tuning methods for adapting pretrained machine learning models.

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

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

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