Eclipse Deeplearning4J
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn... observed · 2026-08-28
Health v2 · maintenance only
77/100
- Activity 99
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4663
- days_rel: n/a
- days_push: 9
- n_releases_24m: 0
Adoption not part of the score
14246 stars · 3826 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
Eclipse Deeplearning4J is an open-source deep learning framework and ecosystem for the JVM, including the ND4J linear algebra library, the SameDiff automatic differentiation framework, DataVec ETL, and model import for Keras, TensorFlow, ONNX, and PyTorch. It supports CPU (AVX) and CUDA GPU acceleration, distributed training on Apache Spark, and Python interop via Python4J.
Use cases
- train neural networks in java
- import keras or tensorflow models into a jvm application
- run deep learning inference on the jvm without python
- distributed deep learning training on apache spark
- numpy-like linear algebra library for java
- deploy deep learning models in jvm microservices or on android
- retrain pytorch or onnx models from java
When to choose
- your production stack is JVM-based (Java, Scala, Kotlin, Clojure) and you want to train or run deep learning models without a Python runtime
- you need to import and retrain models built in Python frameworks and deploy them in JVM environments, Spark clusters, mobile, or IoT
- you need a high-performance N-dimensional array and linear algebra library for the JVM with CPU and CUDA GPU backends
When to avoid
- your workflow is Python-first and you have no JVM requirements - PyTorch or TensorFlow are more natural choices
- you need the very latest research models or eager execution features that mature Python frameworks get first
- you want a large community and ecosystem of pretrained models and tutorials comparable to mainstream frameworks
Facets
library · maturity active
deep-learning machine-learning etl gpu-computing llm-training math deep-learning machine-learning gpu-computing data-science jvm windows nd4j samediff datavec keras-import onnx tensorflow-import spark hadoop linear-algebra model-import python4j scala kotlin clojure natural-language-processing linux macos gpu docker android
8 sources
- readme: https://github.com/deeplearning4j/deeplearning4j · fetched 2026-08-28 · 94a7ded9c575
- homepage: http://deeplearning4j.konduit.ai · fetched 2026-08-29 · d3d434658ba3
- site_page: https://deeplearning4j.konduit.ai/nd4j/quickstart · fetched 2026-08-29 · 0762afc2aeb9
- site_page: https://deeplearning4j.konduit.ai/deeplearning4j/quickstart · fetched 2026-08-29 · 704448effbea
- site_page: https://deeplearning4j.konduit.ai/python4j/getting-started · fetched 2026-08-29 · 370e9c87de7d
- site_page: https://deeplearning4j.konduit.ai/v/en-1.0.0-m1.1 · fetched 2026-08-29 · f721903197be
- site_page: https://deeplearning4j.konduit.ai/v/en-1.0.0-beta7 · fetched 2026-08-29 · 2cd6ec17436d
- site_page: https://deeplearning4j.konduit.ai/v/en-1.0.0-beta6 · fetched 2026-08-29 · 6ff318fd9765
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| deeplearning4j/deeplearning4j | main | 77 |
| deeplearning4j/deeplearning4j-examples | examples | 74 |
For agents
markdown · JSON · MCP: product_card(name="deeplearning4j/deeplearning4j")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem