autodistill VS opentofu

Roboflow | Open Source Software Engineer, Web Designer / Developer, and more. | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0224

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].

We have several openings available but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping us figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)

We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.

[1]: https://roboflow.com/?ref=whoishiring0224

[2]: https://roboflow.com/universe?ref=whoishiring0224

[3]: https://github.com/autodistill/autodistill

[4]: https://github.com/roboflow/supervision

[5]: https://blog.roboflow.com/?ref=whoishiring0224

[6]: https://www.youtube.com/@Roboflow

The places in which a vision model is deployed are different than that of a language model.

A vision model may be deployed on cameras without an internet connection, with data retrieved later; a vision model may be used on camera streams in a factory; sports broadcasts on which you need low latency. In many cases, real-time -- or close to real-time -- performance is needed.

Fine-tuned models can deliver the requisite performance for vision tasks with relatively low computational power compared to the LLM equivalent. The weights are small relative to LLM weights.

LLMs are often deployed via API. This is practical for some vision applications (i.e. bulk processing), but for many use cases not being able to run on the edge is a dealbreaker.

Foundation models certainly have a place.

CLIP, for example, works fast, and may be used for a task like classification on videos. Where I see opportunity right now is in using foundation models to train fine-tuned models. The foundation model acts as an automatic labeling tool, then you can use that model to get your dataset. (Disclosure: I co-maintain a Python package that lets you do this, Autodistill -- https://github.com/autodistill/autodistill).

SAM (segmentation), CLIP (embeddings, classification), Grounding DINO (zero-shot object detection) in particular have a myriad of use cases, one of which is automated labeling.

I'm looking forward to seeing foundation models improve for all the opportunities that will bring!

Autodistill

Thanks for the suggestion! Definitely agree, we’ve seen that work extremely well for Supervision[1] and Autodistill, some of our other open source projects.

There’s still a lot of polish like this we need to do; we’ve spent most of our effort cleaning up the code and documentation to prep for open sourcing the repo.

Next step is improving the usability of the pip pathway (that interface was just added; the http server was all we had for internal use). Then we’re going to focus on improving the content and expanding the models it supports.

[1] https://github.com/roboflow/supervision

[2] https://github.com/autodistill/autodistill

Roboflow | Multiple Roles | Full-time (Remote, SF, NYC) | https://roboflow.com/careers?ref=whoishiring0823

Roboflow is the fastest way to use computer vision in production. We help developers give their software the sense of sight. Our end-to-end platform[1] provides tooling for image collection, annotation, dataset exploration and curation, training, and deployment.

Over 250k engineers (including engineers from 2/3 Fortune 100 companies) build with Roboflow. We now host the largest collection of open source computer vision datasets and pre-trained models[2]. We are pushing forward the CV ecosystem with open source projects like Autodistill[3] and Supervision[4]. And we've built one of the most comprehensive resources for software engineers to learn to use computer vision with our popular blog[5] and YouTube channel[6].

We have several openings available, but are primarily looking for strong technical generalists who want to help us democratize computer vision and like to wear many hats and have an outsized impact. Our engineering culture is built on a foundation of autonomy & we don't consider an engineer fully ramped until they can "choose their own loss function". At Roboflow, engineers aren't just responsible for building things but also for helping figure out what we should build next. We're builders & problem solvers; not just coders. (For this reason we also especially love hiring past and future founders.)

We're currently hiring full-stack engineers for our ML and web platform teams, a web developer to bridge our product and marketing teams, several technical roles on the sales & field engineering teams, and our first applied machine learning researcher to help push forward the state of the art in computer vision.

[1]: https://roboflow.com/?ref=whoishiring0823

[2]: https://roboflow.com/universe?ref=whoishiring0823

[3]: https://github.com/autodistill/autodistill

[4]: https://github.com/roboflow/supervision

[5]: https://blog.roboflow.com/?ref=whoishiring0823

[6]: https://www.youtube.com/@Roboflow

> Their SKILL tool involves a set of algorithms that make the process go much faster, they said, because the agents learn at the same time in parallel. Their research showed if 102 agents each learn one task and then share, the amount of time needed is reduced by a factor of 101.5 after accounting for the necessary communications and knowledge consolidation among agents.

This is a really interesting idea. It's like the reverse of knowledge distillation (which I've been thinking about a lot[1]) where you have one giant model that knows a lot about a lot & you use that model to train smaller, faster models that know a lot about a little.

Instead, you if you could train a lot of models that know a lot about a little (which is a lot less computationally intensive because the problem space is so confined) and combine them into a generalized model, that'd be hugely beneficial.

Unfortunately, after a bit of digging into the paper & Github repo[2], this doesn't seem to be what's happening at all.

> The code will learn 102 small and separte heads(either a linear head or a linear head with a task bias) for each tasks respectively in order. This step can be parallized on multiple GPUS with one task per GPU. The heads will be saved in the weight folder. After that, the code will learn a task mapper(Either using GMMC or Mahalanobis) to distinguish image task-wisely. Then, all images will be evaluated in the same time without a task label.

So the knowledge isn't being combined (and the agents aren't learning from each other) into a generalized model. They're just training a bunch of independent models for specific tasks & adding a model-selection step that maps an image to the most relevant "expert". My guess is you could do the same thing using CLIP vectors as the routing method to supervised models trained on specific datasets (we found that datasets largely live in distinct regions of CLIP-space[3]).

[1] https://github.com/autodistill/autodistill

[2] https://github.com/gyhandy/Shared-Knowledge-Lifelong-Learnin...

[3] https://www.rf100.org

and more.

Hey!

> With OpenTofu exclusive features making such an early debut, is the intention to remain a superset of upstream Terraform functionality and spec, or allow OpenTofu to diverge and move in its own direction?

The intention is to let it diverge. There will surely be some amount of shared new features, but we're generally going our own way.

> Will you aim to stick to compatibility with Terraform providers/modules?

Yes.

Regarding providers, we might introduce some kind of superset protocol for providers at some point, for tofu-exclusive functionality, but we'll make sure to design it in a way where providers keep working with both Terraform and OpenTofu.

Regarding modules, this one will be more tricky, as there might Terraform languages features that aren't supported in OpenTofu and vice-versa. We have a proposal[0] to tackle this, and enable module authors to easily create modules with support for both, even when using some exclusive features of any one of them.

> Is the potential impact of community fragmentation on your mind as many commercial users who don’t care about open source ideology stick to the tried-and-true Hashicorp Terraform?

We've talked to a lot of people, and we've met many who see the license changes as a risk for them, while OpenTofu, with its open-source nature, is the less-risky choice. That includes large enterprises.

> Is there any intention to try and supplement the tooling around the core product to provide an answer to features like Terraform Cloud dashboard, sentinel policies and other things companies may want out of the product outside of the command line tool itself?

That's mostly covered by the companies sponsoring OpenTofu's development: Spacelift (I work here), env0, Scalr, Harness, Gruntworks.

[0]: https://github.com/opentofu/opentofu/issues/1328

Please remember to file in a calm and orderly fashion toward the exits and remember: IBM killed Centos for profit.

Terraform users can pick up their new alternative here:

https://opentofu.org/

and for those of you with Vault, you can find your new alternative here:

https://openbao.org/

OpenTofu v1.6

Note: New versions of Terraform will be placed under the BUSL license, but everything created before version 1.5.x stays open-source. OpenTofu is an open-source version of Terraform that will expand on Terraform's existing concepts and offerings. It is a viable alternative to HashiCorp's Terraform, being forked from Terraform version 1.5.6. OpenTofu retained all the features and functionalities that had made Terraform popular among developers while also introducing improvements and enhancements. OpenTofu is not going to have its own providers and modules, but it is going to use its own registry for them.

opentofu is solving this with proper state encryption support: https://github.com/opentofu/opentofu/issues/874

As with all other Linux Foundation and CNCF projects, OpenTofu is guided by the Technical Steering Committee(TSC), which works in open collaboration with the community on the development of new features, upgrades, bug fixes, etc. The current TSC consists of representatives from Harness, Spacelift, Scalr, Gruntworks, and env0.