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.
filter_dramaImplement Object Detection with PyTorch in Java in 5 minutes
While there are a variety of options to serve PyTorch models in production, there are only a few options to deploy PyTorch models natively in Java. Previously, users could write a Java wrapper around the PyTorch C++ API or use the experimental PyTorch Java bindings. Amazon’s Deep Java Library (DJL) now offers the PyTorch and Java community a simpler option with its easy to use high-level APIs. By abstracting the complexity involved in ML and bundling tedious data processing routines, DJL simplifies running predictions with a PyTorch model down to a few lines of code. In our two-part blog series, we demonstrate how users can run inference with PyTorch. First with pre-trained PyTorch models, then with user-generated PyTorch models.
filter_dramaAdopting machine learning in your microservices with DJL (Deep Java Library) and Spring Boot
Many AWS customers—startups and large enterprises—are on a path to adopt machine learning and deep learning in their existing applications. The reasons for machine learning adoption are dictated by the pace of innovation in the industry, with business use cases ranging from customer service (including object detection from images and video streams, sentiment analysis) to fraud detection and collaboration. However, until recently, the adoption learning curve was steep and required development of internal technical expertise in new programming languages (e.g., Python) and frameworks, with cascading effect on the whole software development lifecycle, from coding to building, testing, and deployment. The approach outlined in this blog post enables enterprises to leverage existing talent and resources (frameworks, pipelines, and deployments) to integrate machine learning capabilities.
filter_dramaMachine Learning in Java With Amazon Deep Java Library
Today, many of these solutions are primarily developed in Python using open source and proprietary ML toolkits, each with their own APIs. Despite Java's popularity in enterprises, there aren’t any standards to develop machine learning applications in Java. JSR-381 was developed to address this gap by offering Java application developers a set of standard, flexible and Java-friendly APIs for Visual Recognition (VisRec) applications such as image classification and object detection. JSR-381 has several implementations that rely on machine learning platforms such as TensorFlow, MXNet and DeepNetts. One of these implementations is based on Deep Java Library (DJL), an open source library developed by Amazon to build machine learning in Java. DJL offers hooks to popular machine learning frameworks such as TensorFlow, MXNet, and PyTorch by bundling requisite image processing routines, making it a flexible and simple choice for JSR-381 users.