DagsHub's company blog covers data science and machine learning tooling, including data versioning, DVC, experiment tracking, and ML research commentary.
HN commenters, including its creators, point to DagsHub as a host for DVC remotes and data science pull requests with data, models, and experiments.
Full Disclosure: I'm a ML Team Lead at DagsHub.
TL;DR
DagsHub lets you stream datasets from any repo you can access for free. We have open-source datasets for various tasks and domains (image, video, MRI, audio, etc.) that you can use, or upload yours and stream it.
Learn more - https://dagshub.com/docs/feature_guide/direct_data_access/
How does it work?
Every DagsHub repo comes with a configured remote storage, where users can host models, datasets, or any other large file.
We recently added a new capability to our open-source client and free-to-use API that enables the streaming of files stored on DagsHub Storage.
It enables access to any dataset stored on DagsHub, stream it to your machine, version it, and upload it to your DagsHub repo - all from your python code.
I think the coolest part of this feature is that it doesn't require any modifications to your code base or data format.
You can find more info about it here -> https://dagshub.com/docs/feature_guide/direct_data_access/
Feel free to reach out if you have any other questions (:
Hey HN, I'm one of the creators of DAGsHub (https://DAGsHub.com). Data Science Pull Requests (DS PRs) expand Pull Requests (PRs) to include data, models, and experiments. The idea behind DS PRs is to automate the data science review process and enable Open Source Data Science.
Concretely this means:
- Reviewing, comparing, and commenting on your experiments (metrics, parameters, visualizations), in context
- Seeing what data and models have changed (not just code)
- Comparing and diffing notebooks
- After reviewing the DS PR, you can merge it in, which will merge code, data, and models all at once
Learning to use Data Science PRs is very straightforward, read more here: https://dagshub.com/docs/collaborating_on_dagshub/data_science_pull_requests/
There is a lot of work to be done, and many things to be improved. I really want to make this workflow as simple and effective for everyone, and your input would be greatly appreciated.
Great to see DVC being discussed here! As a tool, it’s done a lot to simplify version control for data and models, and it’s been a game-changer for many in the MLOps space.
Specifically, it's a genius way to store large files in git repos directly on any object storage without custom application servers like git-lfs or rewriting git from scratch...
At DagsHub [0], we've integrated directly with DVC for a looong time, so teams can use it with added features like visualizing and labeling datasets managing and models, running experiments collaboratively, and tracking everything (code, data, models, etc.) all in one place.
Just wanted to share that for those already using or considering DVC—there are some options to use it as a building block in a more end-to-end toolchain.
[0] https://dagshub.com
DAGsHub | Senior Fullstack Developer | Tel-Aviv, IL | Full-time | ONSITE | https://DAGsHub.com/careers
DAGsHub is looking for a strong Full Stack Developer to join our very early team.
As one of the first members of the team, you will have a central role in shaping the company, its culture, and the future of data science tools.
DAGsHub is creating a home for data science collaboration - the field is rapidly developing, creating value and disrupting industries, and we are living in the Wild West. Now is the best time to create a place where data scientists can figure out how to work together.
Join us to be a founding member of the next GitHub.
https://github.com/meituan/YOLOv6/blob/main/docs/About_naming_yolov6.md
> P.S. We are contacting the authors of YOLO series about the naming of YOLOv6.
You should ask _before_ publishing, not _after_.
They claim it runs faster and is more accurate than YOLOv5, yet requires 3x as much computation (GFLOPs)? Something doesn't add up here.
There is unbelievably little information about the architecture too. Unfortunately it's not in a format I can easily throw the cfg in as visualize it: https://gitlab.com/danbarry16/darknet-visual
This appears to be on purpose to advertise DagsHub: https://dagshub.com/pricing
I'd love to hear what you think about https://dagshub.com/. We're building it with community collaboration in mind. It doesn't cover all the bases you mention, but we do:
* data and model storage
* experiment tracking
* pipeline management
* access control
* data, model, code, pipeline versioning
We're also strictly based on Git and other Open Source formats and tools so connecting with other tools you use like Colab for IDEs or Jenkins/Kubeflow for training is super straightforward (we have examples for some)