The Best MegEngine Alternatives in 2026

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

FATE supports federated learning through an industrial grade framework.

MLX provides an array computing framework designed for Apple silicon devices.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

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

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

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

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

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

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

Cascade supports MLOps workflows for smaller projects without a full platform.

This repository contains machine learning code examples that use Comet.ml.

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

Maze provides an applied reinforcement learning framework for developing projects in Python.

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

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

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

ModelFox supports training, deployment, and monitoring for machine learning models.

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.

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

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