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Deeplearning4j vs MegEngine

Deeplearning4j

deeplearning4j.konduit.ai

MegEngine

megengine.org.cn

Side by side

FigureDeeplearning4jMegEngine
Price fromFreeFree
Free planYesYes
Free trial——
Open source——
WindowsYesYes
macOSYesYes
LinuxYesYes
iOS—Yes
Android—Yes
APIYes—
Self-hostedYesYes
Plans published11
Spec score · Deep Learning Software8.7 (#4)7.9 (#15)
PurposeEclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM.MegEngine is a fast, scalable deep learning framework with automatic differentiation.
SupportThe support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit.The project lists GitHub issues, a forum, QQ group and [email protected] for contact.

Plans

Deeplearning4j

  • Open-source Deeplearning4jFree

Deeplearning4j pricing →

MegEngine

  • MegEngineFree

MegEngine pricing →

In their own words

Deeplearning4j

Deeplearning4j is an open-source framework for building, training, and deploying neural networks on the JVM, using Java or Scala. It can compute on CPUs through OpenBLAS and oneDNN, or use CUDA for GPU acceleration. For distributed training, it supports Apache Spark across CPU or GPU clusters. The framework can import models from Keras, TensorFlow, and ONNX, while Python4J provides Python interoperability from Java through CPython embedding. Its wider ecosystem includes ND4J, SameDiff, DataVec, Keras Import, Python4J, OmniHub, and Arbiter. Documented deployment targets include JVM microservices, mobile devices, IoT, and Spark environments. The libraries are licensed under Apache License 2.0 and governed by the Eclipse Foundation. The quickstart recommends Maven for Java projects, with Ivy and Gradle also workable. Documentation covers version 1.0.0-M2.1. The free plan is listed at free. Support options include GitHub issues, community forums, Stack Overflow, and professional support from Konduit.

Visit deeplearning4j.konduit.ai

MegEngine

MegEngine is a free, open-source deep learning framework for developing and deploying models, with automatic differentiation. It uses one model for training and inference, including quantization and dynamic shapes. The project says enabling DTR can reduce GPU memory use to one-third of the original. Inference is supported across x86, Arm, CUDA, and ROCm; GPU use requires compatible device drivers. Python packages are listed for 64-bit Linux and Windows, macOS 10.14 or later, and Android 7 or later, though macOS and Android installations are CPU-only. The installation guide specifies Python 3.6–3.9. MegEngine Lite supplies C/C++, Rust, and Python runtimes for deployment. MgeConvert handles conversion between MegEngine and third-party model formats, MegFile provides Python file interfaces for S3, HTTP, and local files, and MegFlow is a streaming computation framework for AI applications. Tutorials address beginners and advanced developers. Support options include GitHub issues, a forum, a QQ group, and email.

Visit megengine.org.cn