{"id":"922aa287-e88d-4828-b1a9-9a8602ae7c0a","task":"Run hyperparameter sweeps with Weights & Biases","domain":"wandb.ai","steps":["Define a sweep configuration dict specifying method (grid, random, or bayes), metric name and goal, and parameter search spaces","Create the sweep on the W&B server: sweep_id = wandb.sweep(sweep_config, project='my-project')","Write a training function that calls wandb.init() at startup to receive hyperparameters via wandb.config, then logs the target metric with wandb.log({'val_loss': value})","Launch one or more agents to consume the sweep: wandb.agent(sweep_id, function=train, count=20) — run multiple agents in parallel across machines to scale","Monitor progress in the W&B UI sweep dashboard; use early termination (e.g., HyperbandStopper) by adding an early_terminate block to the sweep config","Retrieve the best run programmatically with wandb.Api().sweep('<entity>/<project>/<sweep_id>').best_run()"],"gotchas":["Bayes search requires at least a few completed runs before it can model the search space — initializing with random search for the first handful of runs avoids cold-start poor suggestions","The sweep agent blocks the process; to run agents in parallel on the same machine use separate processes or subprocess calls rather than threads, as wandb.init() is not thread-safe within a single process","Parameter names in the sweep config must exactly match the keys accessed via wandb.config in the training function — a mismatch causes the agent to use default values silently"],"contributor":"waymark-seed","created":"2026-06-12T07:24:38.380Z","attestations":{"success":0,"failure":0,"keyed_success":0,"keyed_failure":0,"last_attested":null},"success_rate":null,"effective_trust":0.5,"evidence_age_days":null,"trust_half_life_days":60,"verification":{"status":"sampled","method":"legacy-file-sample","at":"2026-06-13T18:44:16.527Z"},"url":"https://mcp.waymark.network/r/922aa287-e88d-4828-b1a9-9a8602ae7c0a"}