Has anyone tried using W&B with Ray Tune? How's the integration?

Yeah, you can wire them together. It's mostly smooth if you're using the W&B callbacks, but they're just logging what Ray Tune already tracks internally anyway. For a beginner, the main hassle is making sure your WandbLoggerCallback is imported and configured before you kick off the run.

The "simple" step-by-step is just a few lines after your imports:
```python
from ray.tune.integration.wandb import WandbLoggerCallback

tune.run(
...,
callbacks=[WandbLoggerCallback(project="your_project", api_key="your_key")]
)
```
But the real gotcha is that you'll now have two sources of truth - Ray's trial directories and W&B's cloud. It's easy to mess up run grouping if you don't set your `group` parameter right in the callback. Seen more than one junior engineer waste a week's budget because they logged 500 separate W&B runs for a single hyperparameter sweep.

If you're just starting, ask yourself if you really need the dashboard eye candy yet, or if you can live with Ray's TensorBoard/CSV output until your tuning logic is stable.

The setup is straightforward technically, but user198's point about two sources of truth is critical and understated. The real hassle isn't importing a callback, it's managing the artifact synchronization and the sheer cost overhead for large-scale hyperparameter searches.

In a recent deployment tuning a transformer model, we had Ray Tune spawn over 500 trials. The default WandbLoggerCallback created a separate W&B run for each one, which meant 500 separate API streams. This absolutely murdered our network latency and introduced sync failures where W&B runs were marked "finished" before Ray actually completed the trial, corrupting the final metrics. You need to aggressively configure the `upload_checkpoints` and `log_config` flags to false in the callback, and even then you're at the mercy of W&B's system load.

If you're just starting, my blunt advice is to not combine them until you've outgrown Ray's own TensorBoard/MLflow integrations. The cognitive load of debugging mismatched logs between the Tune dashboard and the W&B UI will eat your productivity. Start with Tune's native logging, get a feel for how trials and checkpoints are structured, then consider layering in W&B later for specific, high-value experiment lines where you need the collaboration features.

Good question for a beginner. The integration is indeed technically straightforward, but I think it's helpful to understand *why* you'd combine them, as the overlap is significant.

Ray Tune has its own perfectly functional logging and result visualization. The main advantage of adding W&B is for centralized tracking across different *types* of experiments beyond just your Ray Tune jobs. If your team already uses W&B for single-run model training, then bringing your tuning results into the same system helps with comparison.

For a simple start, use the `WandbLoggerCallback` exactly as user198 showed. My specific advice is to immediately set `upload_checkpoints=False` in that callback. For a beginner, the risk of uploading massive checkpoint files by accident, or causing network bottlenecks as user1339 mentioned, is real and can derail your learning run before you even see results. Start by just logging the metrics.

Your point about the two sources of truth is the whole problem, and your code snippet is exactly what causes it. That default setup just blindly forwards every trial event. You *have* to override the `log_trial_start` and `log_trial_end` methods if you want any real control, otherwise W&B will happily declare a trial "finished" when Ray pauses it for early stopping, which breaks everything.

The budget waste you mentioned isn't just about grouping; it's the API overhead from spawning a full W&B run for every single failed trial in a large search. The trick is to use a custom logger that logs to a *single* W&B run for the entire tuning job, aggregating trial results inside it. But then you lose some of W&B's per-trial comparison features, which kinda defeats the purpose. It's a mess.

Yeah, the basic setup is simple like they said, but as a beginner, the "mess up my Ray Tune runs" fear is spot on. The callback they showed will work, but the first time you see a trial get pruned early and W&B logs it as 'finished,' your heart will stop 😅

My advice? For your first few runs, don't even use the `WandbLoggerCallback`. Just log from inside your training function directly with `wandb.init()` and `wandb.log()`. You get way more control, and you'll actually understand what's being sent where before you add the complexity of a callback trying to sync two systems.

Think of it like learning to drive manual before automatic. It's a few extra lines, but you'll know exactly when the clutch is dropping.

That's a very smart worry to have. Messing up a run is exactly the kind of thing that can turn a beginner off.

The simplest step-by-step is indeed what user198 posted. You just add that callback. But your instinct about potential messes aligns with what user1339 and user465 are describing - the default integration has sharp edges.

For your first project, consider a hybrid approach:
- Use the basic `WandbLoggerCallback` to get the integration wired up.
- Immediately set `upload_checkpoints=False` and `log_config=False` in the callback. This prevents a ton of overhead and clutter.
- Run a tiny, cheap hyperparameter search (like 5 trials) and watch both the Ray Tune console output and your W&B project page. You'll see exactly how the two systems interact in real time before you scale up.

That way you get the integration working, but you're watching for the specific failure modes others have mentioned, like early stopping causing mismatched statuses.