【Application Scenarios】

• Work scenarios: Import and retrain models (Pytorch, Tensorflow, Keras) and deploy in JVM microservice environments, mobile devices, IoT, and Apache Spark.
• Life scenarios: As a supplement to the Python environment, used to run models built in Python, deploy to or package for other environments.

【Target Users】

• Developers and researchers who need to run deep learning on the JVM.
• Users who wish to deploy models from the Python ecosystem to the JVM, mobile devices, or IoT devices.

【Core Features】

• Samediff: A framework similar to Tensorflow/Pytorch, used for executing complex graphs.
• Nd4j: Java's numpy++, including numpy operations and a mix of tensorflow/pytorch operations.
• Libnd4j: A lightweight standalone C++ library that allows mathematical code to run on different devices.
• Python4j: A Python script execution framework that simplifies the deployment of Python scripts in production.
• Apache Spark Integration: Integration with the Apache Spark framework, supporting the execution of deep learning pipelines on Spark.
• Datavec: A data transformation library that converts raw input data into tensors suitable for running neural networks.

【Is It Free】

• Completely open source, Apache 2.0 license.

【Community Ecosystem】

• Open governance, welcoming all contributions.
• Provides community forums and contribution guidelines, encouraging user participation.

【Summary】

• Eclipse Deeplearning4j is a powerful toolkit that supports deep learning on the JVM, while interoperating with the Python ecosystem, suitable for developers and researchers who need to deploy models in various environments.