Caffe vs Deeplearning4j
Caffe
caffe.berkeleyvision.orgDeeplearning4j
deeplearning4j.konduit.aiSide by side
| Figure | Caffe | Deeplearning4j |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | — | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.1 (#9) | 8.7 (#4) |
| Audience | The project describes use across academic research, startup prototypes, and industrial applications. | The quickstart says DL4J targets professional Java developers familiar with production deployments, IDEs, and automated build tools. |
| Compute | Caffe supports CPU and GPU operation, with GPU mode requiring CUDA. | It provides native GPU acceleration via CUDA and CPU computation via OpenBLAS and oneDNN. |
| License | Caffe is released under the BSD 2-Clause license. | The Deeplearning4j project is licensed under Apache License 2.0. |
| Purpose | Caffe is a deep learning framework developed by Berkeley AI Research and community contributors. | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. |
| Support | The site directs usage and installation questions to the caffe-users group and bug reports to GitHub Issues. | The support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit. |
Plans
In their own words
Caffe
Caffe has no write-up on Specifiction yet; its figures are above.
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.