Collaboration in ML made easy with W&B Teams

How to use W&B Teams on your teams next machine learning project . Made by Scott Condron using Weights & Biases
Scott Condron
Everyone on your team cheering as your model converges
You know what's better than watching your model converge? Everyone else on your team watching your models converge with you.
Bask in that glory when you finally beat your predecessor's best performing model. Shout it from the rooftops and pin a link to your W&B project in every Slack channel!
A few of the nice things about W&B Teams:

Case Study: Collaborative Training of DALL·E mini

The dalle-mini team has worked on reproducing the results of OpenAI's DALL·E with a smaller architecture and used W&B pretty extensively throughout the process.
They needed to make sure their experiments were reproducible and that they could easily collaborate.
Here are a few things they used:

A Shared Workspace

This is where they watched their models train and checked whether it was finished training or had crashed. You can see each time they trained models, all of the configurations of each run and compare metrics across runs.
You can see the dall-e mini team's Workspace here.
The Dall-e mini team's Workspace: see it here

Sharing their latest model predictions

The team put the predictions their latest model produced in a W&B Table so they could also evaluate how it was performing:

Sharing results, thoughts and next steps

They used Reports to explain some of the ideas that went into making this project a success and some areas for improvement. This was aimed at helping people reproduce their work and pick up from where they've left off because they've captured all of the key concepts needed to understand their work.
Here's their writeup of their findings in a W&B Report:
Report Gallery

Squeezing all of juice out of their models

By using W&B Sweeps, they were able to find the optimal learning rate their model needed. Sweeps allowed them to efficiently search across possible learning rates, rather than wasting time trying all of the different values manually.

Create a new team project and add some runs!

🤝 Create a team

  1. Click Invite Team in the navigation bar.
  2. Sign up or log in to your free W&B account.
  3. Create your team and invite collaborators.

Move runs from one of your projects

See the documentation for more details.

Conclusion

Summing up, if you care about tracking your work and collaborating closely with other ML practitioners, you should give W&B Teams a try.
W&B has everything you need to collaborate with your ML team:
Good luck and thanks for reading!