Deeplearning4j vs fastai
Deeplearning4j
deeplearning4j.konduit.aifastai
fast.aiSide by side
| Figure | Deeplearning4j | fastai |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | — | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | — |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · Deep Learning Software | 8.7 (#4) | 8.1 (#10) |
| Audience | The quickstart says DL4J targets professional Java developers familiar with production deployments, IDEs, and automated build tools. | The fast.ai site says it works to make deep learning easier to use and involve more people from all backgrounds through free courses, a software library, research, and community. |
| License | The Deeplearning4j project is licensed under Apache License 2.0. | The fastai GitHub repository identifies its license as Apache-2.0. |
| Purpose | Eclipse Deeplearning4j is an open-source, distributed deep learning framework for the JVM. | fastai simplifies training fast and accurate neural networks using modern best practices. |
| Support | The support page lists GitHub issues, community forums, Stack Overflow, and professional support from Konduit. | The fast.ai forums include a category for help installing and using the fastai library for users at any level. |
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.