The Best Sonnet Alternatives in 2026

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

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.

TensorFlow offers an open-source machine learning platform with tools, libraries, and community resources.

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

Use Aeromancy to build reproducible machine learning workflows and projects.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

N2D2 provides an open source CAD framework for deep neural network simulation and application building.

Flower provides a federated AI framework for machine learning projects.

FLAML provides a fast Python library for automated machine learning and tuning.

The fuku-ml project provides a machine learning library described as simple to use.

FlexML offers an easy to use and flexible automated machine learning library for Python.

The goro project provides a high level machine learning library written for Go.

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

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

Sagify describes its project as a way to work with large language models and machine learning more easily.

TPOT uses genetic programming to optimize machine learning pipelines in Python.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.

Cog packages machine learning models into containers to support deployment across environments.

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.

Hypernets offers a general automated machine learning framework for developing end to end AutoML toolkits in specific domains.