monogon VS label-studio

It's somewhere in my git stack :).

Until I get to publishing it, the proto/gRPC definitions for node management are a good enough start: https://github.com/monogon-dev/monogon/blob/main/metropolis/...

And the top level API to actually deploy workloads is plain Kubernetes.

Monogon, a fully remote, self-funded and engineer-led technology company, is hiring software engineers to work on Metropolis, an open source [1], secure, distributed cluster operating system based on Linux and Kubernetes.

Metropolis runs on a fleet of bare metal or cloud machines and provides users with a hardened, production ready Kubernetes - without the overhead of traditional Linux distributions or configuration management systems. It does away with the scripting/YAML duct tape and configuration drift inherent to traditional deployments, and instead provides a stable, API-driven, secure and vendor-lock-in-free platform for companies to build their products upon.

We're looking for senior candidates who can design, implement and verify complex systems that will make up part of Metropolis. We offer a kind and honest work environment in which we prioritize quality over quantity. You'll be the fourth member of a team working on an ambitious, industry-challenging product.

Our ideal candidate is a generalist with deeper knowledge in one or more of the following areas:

- Distributed systems;

- Software engineering of systems built to last;

- Security engineering, especially experience with secure boot chains;

- Low-level programming and debugging (C, Linux Kernel, …);

- Kubernetes, especially practical experience of running bare-metal production deployments;

- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].

Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).

To get in touch, email me at at nexantic.com.

[1] - https://github.com/monogon-dev/monogon

[2] - https://rachelbythebay.com/w/2020/05/19/abc/

- Platform development, ie. running a 'Company A' style infrastructure/DevOps team [2].

Our codebase is mostly Go (including pid1!), so knowledge of the language is a plus, but not a requirement (given the seniority of the position, we expect any candidate to be able to ramp up on Go within a few weeks).

To get in touch, email me at at nexantic.com.

[1] - https://github.com/monogon-dev/monogon

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.