Activeloop Hub VS labelflow

Hey u/platoTheSloth, as u/gopietz mentioned (thanks a lot for the shout-out!!!), you can share them with the general public through uploading to Activeloop Platform (for researchers, we offer special terms, but even as a general public member you get up to 300GBs of free storage!). Thanks to our open source dataset format for AI, Hub, anyone can load the dataset in under 3seconds with one line of code, and stream it while training in PyTorch/TensorFlow.

u/Remote_Cancel_7977 we just launched 100+ computer vision datasets via Activeloop Hub yesterday on r/ML (#1 post for the day!). Note: we do not intend to compete with HuggingFace (we're building the database for AI). Accessing computer vision datasets via Hub is much faster than via HuggingFace though, according to some third-party benchmarks. :)

u/gopietz good question. htype="class_label" will work, but querying doesn't support multi-dimensional labels yet. Would you mind opening an issue requesting that feature?

Hi HN,

In machine learning, we are faced with tensor-based computations (that's the language that ML models think in). I've recently discovered a project that helps you make it much easier to set up and conduct machine learning projects, and enables you to create and store datasets in deep learning-native format.

Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as TensorFlow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai). The real benefit for me is that, I can stream my datasets without the need to store them on my machine (my datasets can be up to 10GB+ big, but it works just as well with 100GB+ datasets like ImageNet (https://docs.activeloop.ai/datasets/imagenet-dataset), for instance).

Hub allows us to store images, audio, video data in a way that can be accessed at lightning speed. The data can be stored on GCS/S3 buckets, local storage, or on Activeloop cloud. The data can directly be used in the training TensorFlow/ PyTorch models so that you don't need to set up data pipelines. The package also comes with data version control, dataset search queries, and distributed workloads.

For me, personally the simplicity of the API stands out, for instance:

Loading datasets in seconds

  import hub ds = hub.load("hub://activeloop/cifar10-train")

Hub by Activeloop (https://github.com/activeloopai/Hub) is an open-source Python package that arranges data in Numpy-like arrays. It integrates smoothly with deep learning frameworks such as Tensorflow and PyTorch for faster GPU processing and training. In addition, one can update the data stored in the cloud, create machine learning pipelines using Hub API and interact with datasets (e.g. visualize) in Activeloop platform (https://app.activeloop.ai/3)

Consider this

Please take a look at our open-source dataset format https://github.com/activeloopai/hub and a tutorial on htypes https://docs.activeloop.ai/how-hub-works/visualization-and-htype

I'm Davit from Activeloop (activeloop.ai).

Hub.

What solution have you used that you like as a data scientist when working with large datasets? Any standard python API to access the data? Other solution? If anyone has used https://github.com/activeloopai/Hub or other similar API I'd be interested to hear your experience working with it!

It is launch day for us at LabelFlow, the open platform for image labeling, would be great to get your feedback on this major update for us.

We are hosting image datasets on our platform and until recently the stored datasets were relatively small (several hundreds of images, few GB) so we only offered the possibility to export zip files containing images and labels in the COCO or YOLO format. As the average size of the datasets is growing, it's not convenient anymore to export a zip.

SWC in NextJS is still in canary with experimental settings, but it took me 3 lines of code yesterday to make it work on a fairly large app ( https://labelflow.ai ). Hot reload times instantly went from 10s to 1s. Twitter discussion here https://twitter.com/vlecrubier/status/1448371633673187329?s=...

Overall I’m pretty bullish on Rust tooling and integration within the JS/ Wasm ecosystem !

Check labelflow.ai. It's free, the code is published, web UI is super simple and the images do not need to be uploaded on remote servers so you get started in no time. For classification you would press the 1 key if image has hotdog else right key to go to the next image. Not gonna lie, you're going to need a bit of time for 10k images but definitely doable alone on a simple use case like that. To be fully transparent, I work there! Classification features are still in beta they will be released in 2 weeks. Happy labeling!

I’ve used storybook for 4 years in teams of 1-15 devs and I’d say it’s a must have for any serious react app with 3+ full time developers. It has its rough edges sure but the ROI is 10x nonetheless in my experiences.

Advantages

- Testing components in isolation forces some good practices and allows to keep the codebase in check by encouraging good practices (limited coupling of unrelated parts of the codebase

- It’s super productive because it is both a form of unit tests, useful during development of UX in « TDD mode », and a very good documentation of your UI components. It greatly reduces the effort needed for both these aspects.

- For DX, the hot reload is generally faster in storybook than in the App (except if you use vite/snowpack in your app, so far..) because reloading a single component is faster than reloading the whole app and its state. In a large CRA our hot reload could sometimes take up 1min in complex cases, while storybook was taking 3s.

- Coupled with Chromatic (their hosted platform) and its GitHub integration it makes QA and visual regression testing a joy, 10x faster than alternatives, I really recommend that.

- It allows to share/iterate easily your ongoing developments with non-tech people in your organisation at early stage. A very good bridge between Figma and the final UI. A good support during Daily meetings about UI, just shared the deployed story url to ask for feedback.

Drawbacks

- It has its own Webpack config. So if you have a custom Webpack config in your app (don’t do that anyway, unless absolutely necessary) then be prepared to duplicate the customizations in your storybook config

- Global React Contexts needs to be duplicated in your storybook config and, if necessary, configured for individual stories. For example if your signup button changes based on an Auth status stored in a global context, then you will have to use Story.parameters to customize the content of the Auth context.

- We had a couple instances where storybook was the limiting factor for us to embrace some new/fancy tech, like yarn v2 or service worker. However maybe that’s a good litmus test: things that storybook support are state of the art JS and generally safe to use. Things that storybook does not support out of the box will cause you problems with other tools anyway: if it’s not storybook, some other tool like Cypress, Jest, Next, or some browsers will cause you trouble with your “shiny new tech”

- It can be slow to startup. We had a storybook with 300+ complex stories and it took 5min to startup and 10min to build in the CI

- It had some API changes/ migration pains a couple years back. However I think the new API is very good and will last a long time so this is behind.

Overall I definitely advocate to use storybook, especially with Chromatic, the ROI is 10x. If you find yourself limited by it in 2021 despite configuring it, maybe question your own tech stack.

Don’t try to implement your own storybook copycat (we had a colleague develop an alternative https://github.com/remorses/vitro , but i think it was not worth the effort)

If you want to see a state of the art repo in NextJS that uses storybook extensively with some customizations, check https://github.com/Labelflow/labelflow/

As a matter of fact, LabelFlow uses a service worker exactly to avoid sending your data to a server (your data is stored in the local service worker instead). The code of this service worker is there: https://github.com/labelflow/labelflow/blob/main/typescript/web/src/worker/index.ts . You won't find any privacy-defeating stuff in there. It's super simple.

What was then just a landing page is now a product that you can try for free with no login required, the code is also publicly available on GitHub. (https://github.com/Labelflow/labelflow/).

4 months ago we announced Labelflow (https://www.labelflow.ai/), the open image annotation and dataset cleaning platform.

What was then just a landing page is now a product that you can try for free with no login required, the code is also publicly available on GitHub. (https://github.com/Labelflow/labelflow/).

In this first version, we are releasing your most wanted features: a straightforward online image annotation tool. For privacy concerns, your images are never uploaded to our server! You can create bounding boxes, polygons, export labels to COCO format and we added plenty of keyboard shortcuts for productivity!

We’re excited to hear your feedback, tell us what features would make your life easier (https://labelflow.canny.io/feature-requests) and upvote what you would like us to build. Stay tuned, It’s just the beginning of a long story.