Deeplearning4j vs PyTorch
Deeplearning4j
deeplearning4j.konduit.aiPyTorch
pytorch.orgSide by side
| Figure | Deeplearning4j | PyTorch |
|---|---|---|
| Price from | Free | — |
| Free plan | Yes | — |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| iOS | — | Yes |
| Android | — | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 0 |
| Spec score · Deep Learning Software | 8.7 (#4) | 7.3 (#29) |
| Distributed training | Deeplearning4j supports distributed neural network training on CPU or GPU clusters using Apache Spark. | PyTorch provides asynchronous collective operations and peer-to-peer communication through Python and C++ interfaces. |
| Ecosystem | Its ecosystem includes ND4J, SameDiff, DataVec, Keras Import, Python4J, OmniHub, and Arbiter. | The site identifies Captum, PyTorch Geometric, and skorch as ecosystem projects or tools. |
| Languages | It supports building, training, and deploying neural networks in Java and Scala. | PyTorch offers Python and C++ front ends, and the installer lists Python and C++/Java language choices. |
| Purpose | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. | PyTorch enables fast, flexible experimentation and efficient production through a user-friendly front end, distributed training, and an ecosystem of tools and libraries. |
| Support | The support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit. | The Foundation directs users with technical questions to the PyTorch discussion community. |
| What it does | Eclipse Deeplearning4j is an open-source deep learning framework for the JVM, for building, training, and deploying neural networks in Java and Scala. | PyTorch is an end-to-end machine learning framework for fast experimentation and production. |
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.