NeoGPT VS deeplake

One of the most interesting projects I came across this month was NeoGPT. It's a GPT based application that is being built to converse with documents and videos. While still in its infancy, the project has outlined a cool roadmap and has a very active base of contributors continuously expanding on its functionality. The project appeals to my desire to learn how to work with AI and neural networks. It is also at a development stage that it is not outside of the reach of my comprehension. Icing on the cake being it's Py based, which is my sharpest tool at the moment. I see it as a decent project to stay tapped into and grow my skills as the application develops.

I think Activeloop(YC) is too: https://github.com/activeloopai/deeplake/

This repository contains two Python scripts that demonstrate how to create a chatbot using Streamlit, OpenAI GPT-3.5-turbo, and Activeloop's Deep Lake. The chatbot searches a dataset stored in Deep Lake to find relevant information and generates responses based on the user's input.

Deep Lake GitHub

As the world is increasingly generating vast amounts of financial data, the need for advanced tools to analyze and make sense of it has never been greater. This is where LangChain and Deep Lake come in, offering a powerful combination of technology to help build a question-answering tool based on financial data. After participating in a LangChain hackathon last week, I created a way to use Deep Lake, the data lake for deep learning (a package my team and I are building) with LangChain. I decided to put together a guide of sorts on how you can approach building your own question-answering tools with LangChain and Deep Lake as the data store.

Re: HF - we know them and admire their work (primarily, until very recently, focused on NLP, while we focus mostly on CV). As mentioned in the post, a large part of Deep Lake, including the Python-based dataloader and dataset format, is open source as well - https://github.com/activeloopai/deeplake.

Likewise, we curate a list of large open source datasets here -> https://datasets.activeloop.ai/docs/ml/, but our main thing isn't aggregating datasets (focus for HF datasets), but rather providing people with a way to manage their data efficiently. That being said, all of the 125+ public datasets we have are available in seconds with one line of code. :)

We haven't benchmarked against HF datasets in a while, but Deep Lake's dataloader is much, much faster in third-party benchmarks (see this https://arxiv.org/pdf/2209.13705 and here for an older version, that was much slower than what we have now, see this: https://pasteboard.co/la3DmCUR2iFb.png). HF under the hood uses Git-LFS (to the best of my knowledge) and is not opinionated on formats, so LAION just dumps Parquet files on their storage.

While your setup would work for a few TBs, scaling to PB would be tricky including maintaining your own infrastructure. And yep, as you said NAS/NFS would neither be able to handle the scale (especially writes with 1k workers). I am also slightly curious about your use of mmap files with image/video compressed data (as zero-copy won’t happen) unless you decompress inside the GPU ;), but would love to learn more from you! Re: pricing thanks for the feedback, storage is one component and customly priced for PB-scale workloads.

Deep Lake is fresh off the "press", so we would really appreciate your feedback here or in our community, a star on GitHub. If you're interested to learn more, you can read the Deep Lake academic paper or the whitepaper (that talks more about our vision!).

Continue reading | heck out the paper and github

GIthub: https://github.com/activeloopai/deeplake