We are excited to announce the Deep Java Library (DJl), an open source library to develop, train and run Deep learning models in Java using intuitive, high-level APIs. In this post, we will show how to run a prediction with a pre-trained deep learning model in minutes.
We are excited to announce the Deep Java Library (DJL) , an open source library to develop, train and run deep learning models in Java using intuitive, high-level APIs. If you are a Java user interested in learning deep learning, DJL is a great way to start learning. If you’re a Java developer working with deep learning models, DJL will simplify the way you train and run predictions. In this post, we will show how to run a prediction with a pre-trained deep learning model in minutes.
Before we start coding, we want to share our motivation for building this library. In surveying the deep learning landscape, we found an abundance of resources for Python users. For instance, NumPy for data analysis; Matplotlib for visualizations; frameworks such as MXNet, PyTorch, TensorFlow, and many more. But there are very few resources for Java users, even though it is the most popular language in enterprise . We set out with the goal to provide millions of Java users open source tools to train and serve deep learning models in a language they are already familiar with.
DJL is built with native Java concepts on top of existing deep learning frameworks. It offers users access to the latest innovations in deep learning and the ability to work with cutting edge hardware. The simple APIs abstract away the complexity involved in developing deep learning models, making them easy to learn and easy to apply. With the bundled set of pre-trained models in model-zoo, users can immediately start integrating deep learning into their Java applications.
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