tangram VS label-studio

Head over to https://www.tangram.dev and give it a try!

In this post, we will go over how we optimized our Gradient Boosted Decision Tree library. This is based on a talk that we gave at RustConf 2021: Writing the Fastest Gradient Boosted Decision Tree Library in Rust. The code is available on GitHub.

You can check out the Tangram Ruby Gem. We built it using Ruby FFI and the source is available on our GitHub repo.

We are using Rust for our web application and website at Tangram. You can view the source here: https://github.com/tangramdotdev/tangram/tree/main/crates/www. The website is at https://www.tangram.dev. We decided to write our own web framework because we needed server rendering and we wanted to use the builder pattern in creating components. Here is an example component for our Logo to get a sense of what this looks like: https://github.com/tangramdotdev/tangram/blob/main/crates/www/ui/logo.rs

Check out https://github.com/tangramdotdev/tangram.

Tangram | Senior Rust Programmer | Remote | https://www.tangram.dev

Tangram is an all in one machine learning framework designed for programmers. With Tangram, developers can train models and make predictions on the command line or with libraries for languages including Elixir, Go, JS, Python, Ruby, and Rust, and learn about their models and monitor them in production from a web application. To learn more about what the product does, watch the demo on the homepage at https://www.tangram.dev or check it out on GitHub at https://www.github.com/tangramdotdev/tangram.

We are looking to grow our engineering team with senior Rust programmers. We are currently based in Boston, MA but are looking to build a remote team. At Tangram, you'll get to work on everything from our core machine learning algorithms to writing front-end code in Rust! We are looking for developers with experience in Rust and familiarity with or willingness to learn machine learning concepts. If this sounds exciting, email me (Isabella, cofounder) at [email protected].

We chose to use Rust instead of TypeScript for the front end of https://github.com/tangramdotdev/tangram.

This allows us to:

* Share code with our server written in Rust.

I'm working on machine learning in Rust at Tangram. We currently only provide an implementation of linear models and gradient boosted decision trees but will move into exposing training of deep models in the future. You can check out Tangram here: https://github.com/tangramdotdev/tangram. You may also be interested in checking out Linfa https://github.com/rust-ml/linfa. If you're interested in the future of machine learning in Rust, check out Luca Palmieri's blog post: https://www.lpalmieri.com/posts/2019-12-01-taking-ml-to-production-with-rust-a-25x-speedup/

If instead you have a cohort on hand — -i.e., you do not want to send your data to a third party for any reason, or perhaps you have energetic undergrads — -then you could alternatively consider local, open-source annotation such as CVAT and Label Studio. Finally, nowadays, you might instead work with Large Multimodal Models to have them annotate your data; more on this awkward angle later.

14. LabelStudio by Human Signal | Github | tutorial

For instance, the COCO Annotator is a web-based image annotation tool tailored for the COCO dataset format, allowing collaborative labeling with features like attribute tagging and automatic segmentation. Similarly, Label Studio offers an easy-to-use interface for bounding box object labeling in images.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

LabelStudio is free for single use. https://labelstud.io/

LabelStudio is pretty nice, and free & open source, but I have yet to try out their ML integration with a YOLO object detection model.

Label Studio is an open source data labeling tool that includes annotation functionality. It provides a simple user interface (UI) that lets you label various data types, including text, audio, time series data, videos, and images, and export the information to various model formats.