The Best deepjavalibrary/djl Alternatives in 2026

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

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

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

ModelDB tracks machine learning model versions, metadata, and experiments.

Flexible, high-performance serving system for machine learning models.

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

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

Apache SINGA supports distributed deep learning through a system and library for model training.

DeepDetect provides a deep learning server and command-line interface for Torch and TensorRT.

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

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

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

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

Caffe provides an open framework for building and running deep learning models.

QKeras provides quantization tools for deep learning with TensorFlow Keras.

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

FATE supports federated learning through an industrial grade framework.

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

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

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.

Model deployment for AI applications is supported by nndeploy, a framework described as easy to use and high performance.