Pytorch
Deep Java Library (DJL)


Pytorch | Deep Java Library (DJL) | |
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381 | 13 | |
86,719 | 4,289 | |
1.5% | 1.5% | |
10.0 | 9.6 | |
1 day ago | 9 days ago | |
Python | Java | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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.
Pytorch
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Must-Know 2025 Developer’s Roadmap and Key Programming Trends
Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python, try projects that combine data with everyday problems. For example, build a simple recommendation system using Pandas and scikit-learn.
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Decorator JITs: Python as a DSL
Basically this style of code - https://github.com/pytorch-labs/attention-gym/pull/84/files - has issues like this - https://github.com/pytorch/pytorch/pull/137452 https://github.com/pytorch/pytorch/issues/144511 https://github.com/pytorch/pytorch/issues/145869
For some higher level context, see https://pytorch.org/blog/flexattention/
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Building an AI-powered Financial Data Analyzer with NodeJS, Python, SvelteKit, and TailwindCSS - Part 0
The AI Service will be built using aiohttp (asynchronous Python web server) and integrates PyTorch, Hugging Face Transformers, numpy, pandas, and scikit-learn for financial data analysis.
- PyTorch 2.6.0 Release
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Responsible Innovation: Open Source Best Practices for Sustainable AI
Open source frameworks like PyTorch are already enabling Machine Learning breakthroughs because they’re living communities where great things happen through:
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Golang Vs. Python Performance: Which Programming Language Is Better?
- Data Science and AI: TensorFlow, PyTorch and scikit-learn are only a few of the standard Python libraries. - Web Development: development of web-based applications is made simple by frameworks such as Flask as well as Django. - Prototyping: Python's ease of use lets you quickly iterate and testing concepts.
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How to resolve the dlopen problem with Nvidia and PyTorch or Tensorflow inside a virtual env
By chance, Tensorflow or PyTorch can work with pip packages from Nvidia.
- Making VLLM work on WSL2
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2025’s Must-Know Tech Stacks
PyTorch
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Experiments with Byte Matrix Multiplication
> It's quite common in machine learning operations to multiply a matrix of unsigned byte by a matrix of signed byte. Don't ask me why, but that's the case.
Overflow is the reason. Intel's vpmaddubsw takes int8_t and uint8_t to give you results in int16_t. If both are unsigned 255 * 255 = 65025 will be out of range for int16_t so likely the instruction is designed to take int8_t and uint8_t. The overflow (or rather saturation with this instruction) can still occur because it sums to adjacent multiplication. See my comment in PyTorch. https://github.com/pytorch/pytorch/blob/a37db5ae3978010e1bb7...
Deep Java Library (DJL)
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Is deeplearning4j a good choice?
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.
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Just want to vent a bit
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.
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Best way to combine Python and Java?
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).
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Anybody here using Java for machine learning?
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.
- Good document classification library in Java
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2021-09 - Plans & Hopes for Clojure Data Science
Here is link number 1 - Previous text "DJL"
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[D] Java vs Python for Machine learning
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.
- Does Java has similar project like this one in C#? (ml, data)
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If it gets better w age, will java become compatible for machine learning and data science?
I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.
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Machine learning on JVM
AWS Deep Learning more deep learning.
What are some alternatives?
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
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...
tinygrad - You like pytorch? You like micrograd? You love tinygrad! ❤️
Tribuo - Tribuo - A Java machine learning library
tensorflow - An Open Source Machine Learning Framework for Everyone
Apache Flink - Apache Flink
Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Flux.jl - Relax! Flux is the ML library that doesn't make you tensor
H2O - Sparkling Water provides H2O functionality inside Spark cluster
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit
CoreNLP - CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.

