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using-model-endpoint

Call a registered model endpoint over its native HTTP API from the endpoint's scoped inference kernel (BASE_URL preloaded). Load once a task needs predictions from a registered model endpoint.

personAuthor: TashanworldhubOpenAPI
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You are a pure HTTP client of BASE_URL. Each registered model endpoint gets its own inference kernel — a Python REPL whose network egress is scoped to exactly that endpoint — reached via compute_provider({'provider': '<slug>', 'code': '…'}) (<slug> from list_compute, without the infer: prefix).

  • BASE_URL is preloaded (as a Python variable AND as os.environ["BASE_URL"]) — build request URLs from it, never hardcode hosts/ports. Call the model's native API with httpx (preinstalled) or requests; request shapes live in the provider's own runbook skill (the registration's skillName).
  • Hosted endpoints: send Authorization: Bearer $INFER_API_KEY (always the canonical env name when a credential is delivered; the credential's own name is usually aliased too). Local endpoints need no auth header.
  • Requests ride the sandbox HTTP proxy (HTTP_PROXY/HTTPS_PROXY are set) — don't disable it (e.g. trust_env=False) or the endpoint is unreachable.
  • No job lifecycle here (no submit/harvest) — direct request/response only.

Managed endpoints (entries with managed: true / a location field in list_compute): their lifecycle — daemon-owned start/stop, registration, free_port()/register() — lives in the managed-model-endpoints skill. Cells against them are still just HTTP calls to BASE_URL; the daemon brings the model up on demand (a cold start streams its progress into your cell and can take minutes).