SynapseML
Deep Java Library (DJL)
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| SynapseML | Deep Java Library (DJL) | |
|---|---|---|
| 18 | 13 | |
| 4,967 | 3,841 | |
| 0.6% | 2.2% | |
| 8.9 | 9.5 | |
| 7 days ago | 4 days ago | |
| Scala | Java | |
| MIT 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.
SynapseML
- FLaNK Stack Weekly for 12 September 2023
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Microsoft announces new tool for applying ChatGPT and GPT-4 at massive scales
Release Notes: https://github.com/microsoft/SynapseML/releases/tag/v0.11.0
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Data science in Scala
b) There are libraries around e.g. Microsoft SynapseML, LinkedIn Photon ML
- [N] Microsoft Announces New Integrations with OpenAI and MLFlow
- [N] Microsoft Releases new Integrations with OpenAI and MLflow as part of SynapseML
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[P] Microsoft releases SynapseML v0.9.5 with support for speech synthesis, anomaly detection, and geospatial analytics on large-scale data
Link to Release Notes: https://github.com/microsoft/SynapseML/releases/tag/v0.9.5
- Microsoft releases SynapseML v0.9.5 for distributed geospatial analytics, speech synthesis, and anomaly detection in PySpark.
- [P] SynapseML v0.9.5 announces support for geospatial analytics, speech synthesis, and anomaly detection on large-scale datasets
- Microsoft releases SynapseML v0.9.5 with support for speech synthesis, anomaly detection, and geospatial analytics on Apache Spark
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?
mmlspark - Simple and Distributed Machine Learning [Moved to: https://github.com/microsoft/SynapseML]
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 learning using automatic differentiation.
isolation-forest - A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
Tensorflow_scala - TensorFlow API for the Scala Programming Language
mediapipe - Cross-platform, customizable ML solutions for live and streaming media.
deequ - Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.
Tribuo - Tribuo - A Java machine learning library
Breeze - Breeze is a numerical processing library for Scala.
CoreNLP - CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
azure-kusto-spark - Apache Spark Connector for Azure Kusto
Apache Flink - Apache Flink