Show HN: DVC Studio – Git-Based ML Experiments Management(studio.iterative.ai)
studio.iterative.ai
Show HN: DVC Studio – Git-Based ML Experiments Management
https://studio.iterative.ai/
2 comments
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.
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.
DVC has metrics logger similar to other experiment management tool: https://github.com/iterative/dvclive/
Also, metrics & params section of the docs explains this (but yes, it is not perfect yet): https://dvc.org/doc/start/metrics-parameters-plots
Also, metrics & params section of the docs explains this (but yes, it is not perfect yet): https://dvc.org/doc/start/metrics-parameters-plots
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!