The Best Oneflow Alternatives in 2026

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

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

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.

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

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

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

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

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.

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

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.

Ray provides an AI compute engine with a distributed runtime and libraries for machine learning workloads.

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

QKeras provides quantization tools for deep learning with TensorFlow Keras.