JavaCPP VS Deep Java Library (DJL)

JavaCPP and presets for working with JNI

In the meantime you might want to check out JavaCPP: https://github.com/bytedeco/javacpp

Maybe you can use JavaCPP?

Here is the code for JNI, which uses the prebuilt JavaCPP library to call the getpid function. We don't have to write all the manual C binding code and rituals as the JavaCPP library already does it.

Javacpp is the best ffi library of all https://github.com/bytedeco/javacpp

As for our approach, we maintain a library called javacpp: https://github.com/bytedeco/javacpp which proves a python wheel like experience where we distribute natively optimized c/c++ code (and even cuda accelerated code) as jar files on maven central. We also are able to develop with a python like experience by passing pointers around and other low level constructs directly allowing optimizations that you typically only get in c/c++.

https://github.com/bytedeco/javacpp

* it maps naturally and efficiently many common features afforded by the C++ language and often considered problematic, including overloaded operators, class and function templates, callbacks through function pointers, function objects (aka functors), virtual functions and member function pointers, nested struct definitions, variable length arguments, nested namespaces, large data structures containing arbitrary cycles, virtual and multiple inheritance, passing/returning by value/reference/string/vector, anonymous unions, bit fields, exceptions, destructors and shared or unique pointers (via either try-with-resources or garbage collection), and documentation comments*

It seems to have been picked up by Eclipse and there is also Oracle Labs' Tribuo and Deep Java Library. All seem active, but I don't know much about any of them. I agree it's probably best to follow the community and use a more popular tool like PyTorch.

Although it may be a bit more work, you can do both machine learning and AI in Java. If you are doing deep learning, you can use DeepJavaLibrary (I do work on this one at Amazon). If you are looking for other ML algorithms, I have seen Smile, Tribuo, or some around Spark.

Image preprocessing I know less about, but tokenization is something I've dealt with a bunch. There are a few options, either push the tokenizer into the ONNX model and use MS's ONNX Runtime extensions (we've used this when working with sentencepiece tokenizers), port the tokenizer entirely to Java (we did this for BERT), or use a sentencepiece or HF tokenizers wrapper directly (e.g. Amazon's DJL did this - HF, sentencepiece).

https://djl.ai/ seems very promising. I've played around with it quite a bit, not in real production though. It's a very well documented (https://d2l.djl.ai/) and active project, with Amazon working on it.

Well, we've been using Tribuo in production for many years now. The ONNX Runtime Java API that I maintain in MS's ONNX Runtime project has also seen a bunch of uptake in companies, and Amazon have been building DJL for several years too.

Here is link number 1 - Previous text "DJL"

Regarding Tensorflow: As far as I understand, it is accessible through DJL, which has a Clojure wrapper (work in progress): clj-djl. (But I haven't tried it.)

To give a contrasting perspective, I think the Java ecosystem is much better suited for many data science tasks, and has a growing and well-maintained set of libraries for general purpose machine learning. I won't list them all, but TF-Java, DJL et al. have implementations of many modern architectures and there are a number of excellent libraries (CoreNLP, Lucene et al.) for working with text.