Train and run models with OneFlow, a scalable deep learning framework for distributed machine learning workloads.
Open source
24 alternativesdeepjavalibrary/djl
DJL is a Java framework for running deep learning models across different engines.
About deepjavalibrary/djl
Deep Java Library (DJL) is an open-source, high-level Java framework for deep learning that functions like a regular Java library. It supports building, training, and deploying models and integrating them into Java applications using standard IDEs. It is engine-agnostic and allows changing engines during a project, with automatic CPU or GPU selection based on hardware configuration. It provides a Criteria API for loading pre-trained models from a model zoo and built-in blocks with training configuration for model training.
Key features
- Switch deep learning engines at any point without preselecting one
- Automatically choose CPU or GPU based on hardware configuration
- Load pre-trained models from model zoo using Criteria API
- Build, train, and deploy models using standard Java IDEs
- Construct networks with built-in blocks such as Mlp
- Configure training with initializer, optimizer, and loss settings
Best for: Java developers who want to build, train, and deploy deep learning models within Java applications.
Good for
- Engine-agnostic design allows switching engines during a project
- Automatically selects CPU or GPU based on hardware configuration
- Functions like a regular Java library and supports standard IDEs
deepjavalibrary/djl at a glance
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Platforms
- Android
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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deepjavalibrary/djl FAQ
Do I need to choose a deep learning engine before starting a project?
No. DJL is engine-agnostic and allows switching engines at any point.
How do I build DJL from source?
Check out the code, then run ./gradlew build on Linux/macOS or gradlew build on Windows. Add -x test to skip unit tests.
How do I use a pre-trained model for inference?
Define a Criteria with application type and input/output types, load the model from the model zoo, then use a Predictor to run prediction.
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