Show HN: DVC Studio – Git-Based ML Experiments Management (opens in new tab)

Hey everyone, our team is working on open-source tools for data scientists: dvc.org and cml.dev. These two products help ML teams track ML experiments and run training in the cloud using Git & GitOps approach.

Today we are launching DVC Studio - User Interface for DVC and CML. This UI works on top of GitLab, GitHub or BitBucket and extends it by ML specific scenarios:

- Visualizing dashboard of ML experiments

- Graphs for your ML training

- Manages connections to your clouds - data is not stored in Git, but cloud storages :)

- Modify hyperparameters in UI & run ML experiments in clouds or Kubernetes

All of this through Git, GitOps paradigm and with connection to GitLab, GitHub and BitBucket.

Looking forward to your feedback!

I have been in search of a very lightweight way to track experiments, so I went to the dvc page and was completely overwhelmed by all of the options. I tried to find the answer to a simple question — how do I log metrics and artifacts from a train/test run? I saw ‘dvc exp run’ (or something like that), but how does it know what my training script is? And what should I add to my code to checkpoint metrics or other stuff at various points in a script?

I was looking for a simple, self contained “getting started” sequence of pip installs and example code, but I found the docs linking all over the place.

I was previously looking at keepsake, an extremely lightweight experiment tracker/logger. But it had some issues working with PyTorch lightning, so I was back searching for something else.