The Best linfa Alternatives in 2026

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

FATE supports federated learning through an industrial grade framework.

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

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.

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

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

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.

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

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

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

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

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

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

The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.

Flower provides a federated AI framework for machine learning projects.

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