L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

public-api-and-automation

public-api-and-automation

Query, export, and automate W&B resources with wandb.Api, including runs, projects, sweeps, reports, files, history exports, and automation events/actions/scopes.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: public-api-and-automation description: "Query, export, and automate W&B resources with wandb.Api, including runs, projects, sweeps, reports, files, history exports, and automation events/actions/scopes." disable-model-invocation: true metadata: disco-role: operating license: MIT

W&B Public API and Automation

Use this sub-skill when a task asks an agent to read existing W&B data, export run history or metadata, inspect files/reports/sweeps, or create/update W&B Automations. This is for post-hoc querying and server-side automation, not live experiment logging, artifact lifecycle design, or running sweep agents.

Route by task

  • For wandb.Api construction, path shapes, run/project/sweep/report access, filters, and object properties, read API reference.
  • For large or selective run-history exports, sampled vs unsampled history, pagination, file downloads, and the bundled export helper, read export and pagination.
  • For automation scopes, events, filters, integrations, actions, create/update/delete flows, and compatibility checks, read automations.
  • For auth, host, path, pagination, missing-field, GraphQL/network, and automation mismatch failures, read troubleshooting.

Core patterns

import wandb

api = wandb.Api(timeout=60)
run = api.run("entity/project/run_id")
print(run.config)
print(dict(run.summary))
for row in run.scan_history(keys=["loss", "accuracy"], page_size=1000):
    ...
import wandb
from wandb.automations import OnRunMetric, RunEvent, SendWebhook

api = wandb.Api()
project = api.project("project", entity="entity")
webhook = next(api.webhook_integrations(entity="entity"))
event = OnRunMetric(scope=project, filter=RunEvent.metric("loss").mean(5).gt(1.0))
action = SendWebhook.from_integration(webhook, payload={"metric": "loss"})
automation = api.create_automation(event >> action, name="loss-alert")

Bundled script

Use scripts/export_run_history.py for safe CSV or JSONL exports:

python scripts/export_run_history.py --entity ENTITY --project PROJECT --run RUN_ID --out history.csv --format csv --keys loss accuracy --max-rows 10000

Start with --dry-run or --max-rows before exporting large histories.

Skills enregistrées