Deep Java Library (DJL)
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About
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It allows Java developers to utilize their existing knowledge to easily start with machine learning and deep learning tasks. DJL simplifies the process of building, training, and deploying models while providing an ergonomic API interface that guides users in best practices. The library supports various backends including PyTorch, TensorFlow, MXNet, and ONNX, making it flexible for developers to switch between engines without hassle. DJL also supports GPU inference and multi-threading, enhancing performance for deep learning tasks. The library aims to reduce the friction in deploying models into production environments and provides a model zoo for model sharing.
Platform
Task
Features
• built-in model zoo for sharing and reuse
• easy model integration into java applications
• high-performance multi-threaded inference
• gpu support for training and inference
• supports multiple deep learning engines
• engine-agnostic framework
FAQs
Why Deep Java Library (DJL)?
Prioritizes the Java developer's experience, reduces friction for deploying to production, and connects model developers with consumers.
Which models can I run with DJL?
DJL supports PyTorch, TensorFlow, MXNet, ONNX, TensorRT, Python scripts, XGBoost, LightGBM, Sentencepiece, and fastText models.
Does DJL support inference on GPU?
Yes, DJL supports inference on GPU, automatically detecting and using it if available.
Does DJL support training on GPU?
Yes, it supports multi-GPU training and automatically detects GPUs.
Does DJL support distributed training?
No, DJL does not currently support distributed training.
How can I get help if I run into problems?
Check the troubleshooting document, discussions, or issues on the DJL community forum.
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