I'm just starting out with experiment tracking and have been looking at both Sacred and Weights & Biases. I need to pick one for a small personal project.
From a total beginner's perspective, which one is harder to get up and running? I'm comfortable with basic Python and the command line, but new to ML ops. Sacred seems simpler because it's open-source and local, but W&B has that slick UI. Is the initial setup for W&B a lot more complex? 😅
I'd love to hear about your first experiences with both, especially any early hurdles you faced.
I'm a junior ML engineer at a 30-person biotech startup. We use PyTorch for research and needed to track experiments; I've tried both Sacred and W&B for personal projects before we settled on one.
**Initial Setup:** Sacred is a Python library, so it's just `pip install`. W&B requires an account and an API key, and you have to decide between their cloud or a local instance. That extra step adds ~15 minutes the first time.
**Configuration & "Magic":** Sacred uses decorators (`@ex.automain`) which feels straightforward. W&B's `wandb.init()` is similar, but the automatic logging (like grabbing gradients and system metrics) can be confusing when something appears in the UI you didn't explicitly log.
**Local First vs. Cloud First:** Sacred stores everything locally in files by default. W&B defaults to its cloud, which is great for sharing but means you can't see runs offline without extra setup. Their local "local" mode still needs their backend server.
**UI & Debugging:** Sacred's UI (Omniboard) is a separate, complex install. W&B's UI is its main feature and works immediately. However, when a run fails to log, debugging W&B's connection errors was harder for me than checking Sacred's flat files.
For a small personal project where you want to keep it simple and stay entirely on your machine, I'd pick Sacred. If you want to share results with anyone else easily, even yourself on another computer, I'd go with W&B. Tell us if your project needs to be offline-only or if you'll be collaborating.