Aimed at Integrating Models with Java Applications, Deeplearning4j Offers a Stack of Components for Building JVM-Based AI Applications
Eclipse Deeplearning4j is an open-source, distributed deep learning library designed for the Java Virtual Machine (JVM). Written in Java, it is compatible with other JVM languages such as Scala, Clojure, and Kotlin. The core computations are handled by underlying components written in C, C++, and CUDA, while Keras serves as the Python API. Deeplearning4j is integrated with Hadoop and Apache Spark, enabling its use in business environments on distributed GPUs and CPUs.
Deeplearning4j encompasses a suite of projects aimed at addressing all aspects of a JVM-based deep learning application. This stack includes:
- Deeplearning4j: The high-level API for building deep learning models.
- ND4J: A general-purpose linear algebra library.
- SameDiff: A library for graph-based automatic differentiation.
- DataVec: A tool for Extract, Transform, Load (ETL) processes.
- Arbiter: A library for hyperparameter optimization.
- LibND4J: The underlying C++ library supporting all of the above components, which relies on standard libraries like OpenBLAS, OneDNN (MKL-DNN), cuDNN, and cuBLAS for CPU and GPU support.
The primary objective of Deeplearning4j is to offer a comprehensive set of tools for developing AI-integrated applications within the JVM ecosystem. Unlike some deep learning frameworks that focus solely on machine learning, Deeplearning4j aims to provide a robust infrastructure for a wide range of AI applications, supporting complex enterprise needs.
In terms of competition, Deeplearning4j stands alongside major deep learning frameworks such as TensorFlow and PyTorch. TensorFlow, known for its extensive production capabilities, supports Python, C++, and Java (experimental), and provides both graph-based and immediate mode (eager execution) training. Deeplearning4j currently supports only graph-based execution.
PyTorch, a leading framework in research, supports only immediate mode and offers interfaces for Python, C++, and Java. H2O Sparkling Water, another competitor, integrates the H2O distributed machine learning platform with Spark and provides interfaces for Java, Scala, Python, R, and H2O Flow notebooks.
For those seeking commercial support, Konduit offers services for Deeplearning4j, including support for many of the project’s developers, ensuring enterprise-level assistance and expertise.