#112112th of 206 in ML frameworks and libraries
djl
Engine-agnostic Java framework for training and running deep learning models
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- Stars
- 4.9k
- License
- Apache-2.0
- Last commit
- Oct 2026
- Last release
- Oct 2026
- Language
- Java
4,768 on 22 Apr 20264,860 on 11 Nov 2026
From GitHub's daily star counts.
Checked on GitHub.
Overview
Deep Java Library (DJL) is a Java API for deep learning that covers both inference and training. It loads pre-trained models from a model zoo through a Criteria builder, and the underlying engine can be swapped without changing application code. The README says it picks CPU or GPU automatically based on the hardware.
Who it is for: Java developers adding deep learning inference or training to JVM apps
Strengths
- Native Java API that works like any regular library in an IDE and build
- Engine-agnostic: switch deep learning engines without rewriting application code
- Model zoo with Criteria-based loading, plus built-in training and datasets such as MNIST
- Automatic CPU/GPU selection based on detected hardware
Weaknesses
- Java-centric, while most ML tooling and examples are in Python
- README shows only classic deep learning examples, no LLM serving or agent features
What it needs
- GPU optional
- Docker
- Needs Java, Gradle (build from source)
What changed
From the release notes. No flag means the note does not say so, not that the upgrade is safe.
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