Use this file to discover all available pages before exploring further.
The Monitoring tab surfaces training and RL telemetry — loss curves, reward signals, gradient norms, validation metrics — streamed live from a running job and persisted for the run’s lifetime. Pick multiple runs to compare them side-by-side on the same axes.Monitoring covers training-time signals only. For inference-time signals (TTFT, latency, token counts) and post-completion feedback, see Metrics.
For custom recipes, get a logger from the recipe context and call it like a function:
from adaptive_harmony.metric_logger import get_prod_logger@recipe_mainasync def my_recipe(config: MyConfig, ctx: RecipeContext): logger = get_prod_logger(ctx) # Wire the dashboard URL into the run record so users can click through if logger.training_monitoring_link: ctx.job.set_monitoring_link(logger.training_monitoring_link) for step in range(num_steps): loss = await train_one_step(...) logger({"train/loss": loss, "train/lr": current_lr})
The logger accepts any Mapping[str, int | float | Table]. Each call advances the internal step counter once.
Tables appear in the run detail view, paginated and searchable.
Backend selection
get_prod_logger auto-selects a logging backend based on environment variables present in the recipe sandbox: WandB → MLflow → TensorBoard → stdout. The Adaptive monitoring backend is added in addition (not as a replacement) when ADAPTIVE_BASE_URL and ADAPTIVE_API_KEY are set, so metrics always reach the platform UI even if you also log to a third-party tracker.Set ADAPTIVE_MONITORING_DISABLED=1 to opt out of the Adaptive backend (e.g., for local-only development).
Open the Monitoring tab in your project. Each running or completed run appears in the list with status, recipe, and headline metric. Click a run to open its detail view — charts update in real time as the job emits metrics (no refresh required).Use the smoothing controls (EMA / SMA / Gaussian) to denoise noisy curves like RL reward without losing the underlying signal.
Select multiple runs from the list and open Compare. The compare view overlays each run’s curves on the same axes and shows a config diff — every recipe argument that differs between the runs is highlighted. Use this for hyperparameter sweeps, A/B comparisons of dataset versions, or picking the best checkpoint to promote.Run selection is unrestricted — you can compare any two (or more) runs in the project, even across different recipe types.
Charts are most useful right before promotion: scrub the run timeline, identify the step where validation loss bottoms out (or reward stabilizes), then jump to the run’s Checkpoints tab to promote the matching saved checkpoint. See Promoted checkpoints.The platform does not auto-rank checkpoints by metric — promotion is a manual decision based on what you see in the charts.
Monitoring metrics are tied to the run’s lifetime — they live as long as the run record does. Deleting a run deletes its metrics. There is no separate TTL for metrics.
