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name: experiment-tracking description: "Add, debug, and validate W&B experiment tracking in Python scripts or notebooks using runs, metrics, config, summary, tables/media/plots, offline/disabled modes, and framework-routing decisions." disable-model-invocation: true metadata: disco-role: operating license: MIT

Experiment Tracking

Use this sub-skill when a task involves instrumenting Python code with wandb.init(), Run.log(), run config/summary, custom metric axes, tables/media/plots, notebook run lifecycle, or offline-safe tracking. For artifact lineage, model/dataset registries, and run.log_artifact(), route to ../artifacts-and-registries/SKILL.md; for wandb sync, wandb offline, and local CLI workflows, route to ../cli-and-local-workflows/SKILL.md; for hyperparameter sweeps or launch queues, route to ../sweeps-and-launch/SKILL.md.

Start Here

• Add W&B to scripts with with wandb.init(project=..., config=...) as run: and run.log({...}); see references/workflows.md.
• Use mode="offline" for credentials-free local capture and later syncing; use mode="disabled" when tracking must become no-op in tests.
• Keep metric steps monotonic. Prefer logging a custom x-axis metric and binding charts with run.define_metric() instead of forcing W&B's internal step to equal training step.
• Put hyperparameters and static inputs in config; put final scalar outcomes or derived labels in run.summary; put time-series values in run.log().
• Use scripts/offline_tracking_smoke.py to verify that a Python environment can initialize a local run, log metrics, define custom axes, log a table, and finish without network or credentials.

Reference Map

• references/workflows.md: copyable instrumentation patterns for scripts, notebooks, offline mode, custom axes, config/summary, tables/media/plots, and framework integration routing.
• references/api-reference.md: precise signatures, lifecycle semantics, logging rules, settings, data type constraints, and version-specific caveats.
• references/troubleshooting.md: diagnosis playbooks for init failures, invalid IDs, unwritable directories, missing API keys, forgotten finish, reinit, step/commit misuse, unsupported objects, and optional media dependencies.

Safe Defaults

import wandb

config = {"epochs": 10, "lr": 3e-4, "batch_size": 32}

with wandb.init(project="my-project", config=config) as run:
    run.define_metric("epoch")
    run.define_metric("train/*", step_metric="epoch")
    for epoch in range(config["epochs"]):
        loss = 1.0 / (epoch + 1)
        run.log({"epoch": epoch, "train/loss": loss})
    run.summary["best_train_loss"] = loss

For offline smoke validation:

python sub-skills/experiment-tracking/scripts/offline_tracking_smoke.py --steps 3