The Best N2D2 Alternatives in 2026

Ludwig provides a low code framework for building custom LLMs, neural networks, and other AI models.

Deep learning in Rust, with shape checked tensors and neural networks.

Build neural networks and support machine learning research with Sonnet, a library based on TensorFlow.

Caffe provides an open framework for building and running deep learning models.

Rockpool supports training spiking neural networks with PyTorch and JAX and deploying them to neuromorphic hardware.

Sinabs supports fast training of spiking neural networks with PyTorch and inference on neuromorphic hardware.

Tensors and Dynamic neural networks in Python with strong GPU acceleration.

PyTorch Ignite provides a high level library for flexibly training and evaluating neural networks.

Catniff provides JavaScript developers with a Torch-like deep learning framework.

DJL is a Java framework for running deep learning models across different engines.

PaddlePaddle provides a deep learning framework for training models across devices and platforms.

Burn provides a flexible, efficient, and portable tensor library for building deep learning applications.

The MegEngine framework supports deep learning and automatic differentiation, according to its repository description.

Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.

Build machine learning applications with Delta, an open-source framework implemented in Rust.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

DeepDetect provides a deep learning server and command-line interface for Torch and TensorRT.

List of awesome compiler projects and papers for tensor computation and deep learning.

Apache SINGA supports distributed deep learning through a system and library for model training.

Flower provides a federated AI framework for machine learning projects.

The ChefBoost Python framework supports decision trees such as ID3, C4.5, CART, CHAID, regression trees, and ensemble methods.

The Datumbox framework is written in Java for developing machine learning and statistical applications.

The linfa project provides a machine learning framework written in Rust.

PyToune provides a simplified framework and utilities for working with PyTorch.