The Best Lucent 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.

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

A JavaScript Native PyTorch-aligned Machine Learning Framework, built from scratch on WebGPU.

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

Torchvision provides datasets, transforms, and models for computer vision with PyTorch.

The Feast repository describes an open source feature store for AI and machine learning projects.

The Hydrosphere repository describes an MLOps platform for machine learning operations.

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

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

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.

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

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

Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.

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

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.