datarobot-workload-api
Use when the user wants to create, configure, scale, debug, observe, or roll out container workloads on DataRobot's Workload API. Triggers: deploy a container as a managed service; list/start/stop workloads; change replica counts or autoscaling; pick CPU/GPU compute bundles; inject DataRobot credentials as env vars; diagnose stuck/errored/crash-looping workloads (CrashLoopBackOff, ImagePullBackOff, OOMKilled, probe failures, exec format error); pull application logs/OpenTelemetry traces/metrics/request stats; create or iterate container artifacts; build images server-side; lock artifacts for production; zero-downtime rolling artifact replacement.
Pinned to revision eb2a02b36889, so it is the text this page describes rather than whatever the author pushed since.
Files
- skills/datarobot-workload-api/SKILL.md
- skills/datarobot-workload-api/references/code-to-workload.md
- skills/datarobot-workload-api/references/common-error-patterns.md
- skills/datarobot-workload-api/references/declarative-cli-deploy.md
- skills/datarobot-workload-api/references/lifecycle-flows.md
- skills/datarobot-workload-api/references/schema-reference.md
- skills/datarobot-workload-api/references/status-vocabulary.md
- skills/datarobot-workload-api/references/web-uis-behind-the-edge.md
- skills/datarobot-workload-api/scripts/check_limits.py
- skills/datarobot-workload-api/scripts/diagnose_workload.py
- skills/datarobot-workload-api/scripts/wait_for_build.py
- skills/datarobot-workload-api/scripts/wait_for_replacement.py
- skills/datarobot-workload-api/scripts/wait_for_running.py
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