deeplearning4j and JDLL
DL4J is a comprehensive deep learning framework that can train and deploy models, while JDLL is a lightweight Java library specifically designed to run pre-trained deep learning models, making them complements that could be used together in a workflow where DL4J trains models and JDLL deploys them in bioimage analysis contexts.
About deeplearning4j
deeplearning4j/deeplearning4j
Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learn...
SameDiff provides graph-based automatic differentiation with TensorFlow protobuf import, while ND4J's 500+ operations leverage LibND4J's optimized C++ backend with multi-platform support (AVX2/512 CPU, CUDA GPU, ARM). DataVec handles ETL across diverse formats (HDFS, Spark, images, video, audio, CSV), and the stack integrates Python4J for cpython execution—enabling end-to-end ML pipelines entirely on the JVM without leaving Java.
About JDLL
bioimage-io/JDLL
The Java library to run Deep Learning models
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