Deeplearning4j
JavaCPP
Deeplearning4j | JavaCPP | |
---|---|---|
13 | 8 | |
13,427 | 4,380 | |
0.3% | 0.7% | |
5.8 | 6.8 | |
6 days ago | 29 days ago | |
Java | Java | |
Apache License 2.0 | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Deeplearning4j
- Deeplearning4j Suite Overview
- Java for ML?
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Best way to combine Python and Java?
Have you considered migrating off of Python to just using JVM ML libraries then? I hear good things about Deeplearning4j, but there's quite a few.
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Anybody here using Java for machine learning?
I've gone to the linux workflow as directed in the docs and reconstructed the maven command line:
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Data Science Competition
DL4J
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Java Matrix Benchmark is Updated! See how linear algebra libraries compare for speed
Hey folks, just letting you know we see this thread and I appreciate you guys running these benchmarks. I'm not seeing any of your posts on our forums. I think I saw a notification from our examples but we do not actually monitor that. Please use: https://community.konduit.ai/ or at least the main repo dl4j issues: https://github.com/eclipse/deeplearning4j/issues and you'll get a lot more visibility. Thanks!
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Does Java has similar project like this one in C#? (ml, data)
Also, the website is now redirected to: https://deeplearning4j.konduit.ai/
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If it gets better w age, will java become compatible for machine learning and data science?
On top of this several popular projects have been built. This includes tensorflow-java and our project eclipse deeplearning4j: https://github.com/eclipse/deeplearning4j
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Matrices multiplication benchmark: Apache math vs colt vs ejml vs la4j vs nd4j
Nd4j is actively developed. The latest commit was 6 hours ago. Nd4j is part of deeplearning4j which is now owned by eclipse (but the main contributors are from a company) https://github.com/eclipse/deeplearning4j/tree/master/nd4j
JavaCPP
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Any library you would like to recommend to others as it helps you a lot? For me, mapstruct is one of them. Hopefully I would hear some other nice libraries I never try.
JavaCPP and presets for working with JNI
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JDK 19 released
In the meantime you might want to check out JavaCPP: https://github.com/bytedeco/javacpp
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How can I use K/N with C++?
Maybe you can use JavaCPP?
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Does Java 18 finally have a better alternative to JNI?
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.
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JEP 419: Foreign Function and Memory API
Javacpp is the best ffi library of all https://github.com/bytedeco/javacpp
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If it gets better w age, will java become compatible for machine learning and data science?
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++.
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CXX - Safe interop between Rust and 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*
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An article on how to use C++ for cross-platform development
I did not try myself, but for JNI maybe this could make lives easier? https://github.com/bytedeco/javacpp
What are some alternatives?
Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java
JNA - Java Native Access
Weka
SWIG - SWIG is a software development tool that connects programs written in C and C++ with a variety of high-level programming languages.
tensorflow - An Open Source Machine Learning Framework for Everyone
JNR - Java Abstracted Foreign Function Layer
Smile - Statistical Machine Intelligence & Learning Engine
Cython - The most widely used Python to C compiler
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
cppimport - Import C++ files directly from Python!
Apache Mahout - Mirror of Apache Mahout
djinni