tensorstore VS deeplake

shhh...don't tell them about Tensorflow, or tf.Tensor, or theano.tensor, or paddle.to_tensor or torch.Tensor or torch.Tensor or mindspore.Tensor or tensorstore or ... But in all seriousness your pitchforks might be a little late to the party here. Rightly or wrongly (large) segments of the machine learning community have adopted terminology to describe things (for your sake, I hope you have not seen what they did with the term 'deconvolution'). This is not a new phenomena either and has been happening for at least a decade and almost certainly longer. However, there are other libraries that use the array terminology np.array, mx.nd.array, chainer, jax.numpy.array so it is definitely not unanimous. Whether you think this is an egregious mis-representation of the nomenclature or not is irrelevant now as it has a pretty established use within the community. Language is an evolving untamed beast - don't get angry, get building!

There are tons of other ways to store vector data, one was just recently released - https://github.com/google/tensorstore

Continue reading | Github | Google Full Blog

Today we are introducing TensorStore, an open-source C++ and Python software library designed for storage and manipulation of n-dimensional data that:

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