What is Deep Java Library(DJL) - Deep Learning Toolkit for Java Developers
#deeplearning #deepjavalibrary #machinelearning #djl
Deep Java Library (DJL), is an open-source library created by Amazon to develop machine learning (ML) and deep learning (DL) models natively in Java while simplifying the use of deep learning frameworks.
DJL is designed to be easy to get started with and simple to use for Java developers.DJL provides a set of high-level APIs to train, test, and run inferences.
Java developers can develop their own models or utilize pre-trained models developed by data scientists in Python from their Java code.
DJL provides a convenient abstraction layer for using the most popular AI/ML frameworks such as Apache MXNet, PyTorch, and TensorFlow.
With DJL API, you are getting a uniform and consistent layer that can interact with all of these frameworks, allowing you to swap out the framework of your choice without any impact to the client code.
Deep Java Library (DJL) is an open-source library
Developers can build, train, and deploy machine learning (ML) and deep learning (DL) models using Java and their favorite IDE
DJL simplifies the use of deep learning (DL) frameworks and currently supports Apache MXNet, PyTorch, and TensorFlow
DJL can also be configured with different underlying engines (such as MXNet, PyTorch, or TensorFlow)
Use native Java concepts and abstract away complexity involved with Deep learning
The open-source nature of DJL should be mutually beneficial for the toolkit and its users
DJL is engine agnostic, which means developers can write code once and run it on any engine
Java developers should have an understanding of the ML lifecycle and common ML terms before attempting to use DJL but don't need to be an expert
To ensure the best performance, DJL also provides automatic CPU/GPU choice based on the hardware configuration.
DJL offers a repository of pre-trained models in our ModelZoo that simplifies implementation and streamlines model sharing across projects.
The library can be referenced from the following URL - @/djl
Quickstart - @ick_start.html