library-of-alexandria VS Deep Java Library (DJL)

After a quick Google search, I figured out that only less than 1% of ancient texts survived to the modern day. This unfortunate fact was my inspiration to start working on an ambitious web crawling and archival project, called the Library of Alexandria.

Here is a link to the latest releases: https://github.com/bottomless-archive-project/library-of-alexandria/releases

What do you mean by storage system? Just curious because I'm working on a similar project.

If anybody is interested about the project mentioned in the interview, it's available here: https://github.com/bottomless-archive-project/library-of-alexandria

I'm collecting documents. Working on an app suite called Library of Alexandria. Got 91 million docs atm (mostly PDFs) and it's only going up. All of that fits on around 100 TB with gzip compression.

I'm working on an OSS called Library of Alexandria. It is an application that is built to collect, archive, and make searchable various (mostly PDF) documents. I have a little bit more than 90 million documents archived. My next step is to somehow label/classify them.

90 TB of PDFs. I'm working on the Library of Alexandria project. Just a fun little library, nothing more. 😅😅😅

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.

Here is link number 1 - Previous text "DJL"

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.

I think DJL also use use it for their tutorials - https://docs.djl.ai/jupyter/tutorial/01_create_your_first_network.html.

AWS Deep Learning more deep learning.