garden VS label-studio

Hey there! Have you tried garden.io for caching? We also cache tests. Pretty much anything that's possible to cache. We're open source at https://github.com/garden-io/garden

To add to what's already been said: If you think about it, CI pipelines are typically a complete description of how your system is built, tested, and deployed.

Which is pretty fantastic except for how walled off they are. You can't really re-use these descriptions for e.g. development, they're not vendor agnostic, and they only way to run them is by pushing your code.

Maybe it's a silly analogy but it's almost like being a web dev that doesn't have a browser and needs to send their code to a friend who can tell them if that font size looks good.

I think we're way over due for freeing these "blueprints" of our system from the confines of CI and making them portable and flexible. And containers are the technology that's enabling that.

Full disclaimer (as always): I work at Garden[0] where we're also solving that problem but taking a slightly different approach to Dagger (it's still a DAG). Garden config is declarative and the jobs (we call them actions) have a semantic meaning. You can e.g. have a Build action of type container or a Deploy action of type Helm and Garden will figure out what to do with it.

[0] https://github.com/garden-io/garden

Yes, there's us over at https://github.com/garden-io/garden! We're big believers in pipelines that run anywhere. I even made a short little video that should give you the gist. [1]

Some of the short-list of differences: we use YAML for our configuration language, Dagger can use full-fat languages to define its pipelines. Our feature scope is broader: you can use us to vend IDP-like stacks to your developers if you're a Platform Team; we make development with remote Kubernetes clusters very easy, including all the remote image builds; and we have a number of integrations so you can bring your IaC tool of choice (Pulumi, Terraform) into your pipeline and set up service -> infra dependencies.

[1] https://www.youtube.com/watch?v=JFnan6s2cDg

Me too. In fact Garden (dev tooling for the Kubernetes)[0] is a Berlin start-up with three Icelandic founders.

And if I'm not mistaken, two of us worked briefly with @halldorel (above commenter) at an earlier Icelandic start-up. It's a small world (if you're Icelandic).

[0] https://garden.io

There are dedicated tools just for that. Apart from skaffold check also tilt.dev, garden.io, devspace.sh, okteto.com

Would appreciate any insights on garden.io. Thanks.

Telepresence, tilt, garden.io, okteto, skaffold etc.

- https://garden.io/

haha, do my pictures give off a strong not-web-dev vibe? Either way your right, I'm focusing on devxp and automation for kubernetes. Because my work is open source you can see it here https://github.com/garden-io/garden (btw we're also hiring another open core dev like me)

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.

I’m fairly new to deep learning and learning as I got so sorry if this is very basic, but I’m working on a model for detecting invasive coconut rhinoceros beetles destroying palm trees using drone photography. The 1080p photos I’m given were taken 250ft AGL and were cropped into equal size smaller images with some having one or more palm trees and some having none. Im using I’m using labelStudio to generate the XML files that point to their jpg counterparts path.