Deeplearning4j vs MindSpore
Deeplearning4j
deeplearning4j.konduit.aiMindSpore
mindspore.cnSide by side
| Figure | Deeplearning4j | MindSpore |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.7 (#4) | 7.3 (#27) |
| Distributed training | Deeplearning4j supports distributed neural network training on CPU or GPU clusters using Apache Spark. | The official site says MindSpore provides parallel capabilities and APIs for configuring distributed training of foundation models. |
| Open source | The libraries are described as completely open source under the Apache 2.0 license and under Eclipse Foundation governance. | Huawei announced that MindSpore became open source on Gitee on March 28, 2020. |
| Purpose | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. | MindSpore is an AI computing framework designed for AI application development across device, edge, and cloud scenarios. |
| Support | The support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit. | The official site directs users to its forum for help and professional answers, and to AtomGit to submit issues. |
Plans
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.