The Best MLX Alternatives in 2026

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

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

FATE supports federated learning through an industrial grade framework.

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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.

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

The cONNXr project provides a pure C runtime for ONNX models intended for embedded devices.

PAI schedules resources and manages clusters for AI workloads across distributed computing environments.

Adaptive Reliability Layer decides when to retrain a model based on drift signals and delayed labels.

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

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

Semantic Cache Router presents a distributed semantic cache and stateful routing system.

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

PyTorch TNT supplies lightweight tools and utilities for training with PyTorch.

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

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