domino-model-serving

Deploy, invoke, and retire Domino model APIs and registered models via REST. Covers modelServing lifecycle, registered-models v1 vs v2 paths, MLflow tracking vs registry API, inference URLs vs management API, and GenAI endpoint vanity URLs. Use when automating model deployment, predictions, registry updates, or debugging stop/archive/delete behavior.

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Domino model serving (REST)

Programmatic model deployment and inference on Domino. UI-focused endpoint monitoring stays in model-endpoints. Shared REST field catalogs live in API-MODEL-SERVING.md and API-MODELS.md.

Authentication: https://docs.domino.ai/cloud/reference/api/domino-api-authentication . Do not use API keys.

Configuration

Context	Base	Authorization
In-run, `DOMINO_API_PROXY` set	`{DOMINO_API_PROXY}`	None
In-run, no proxy	`{DOMINO_USER_HOST or DOMINO_API_HOST}`	Bearer from `http://localhost:8899/access-token`
Outside run	Public deployment URL	Bearer PAT or SA

Management paths include /api/modelServing/v1/..., /api/registeredmodels/v1|v2/.... Confirm with GET {base}/api/modelServing/v1/modelApis?limit=1 returning HTTP 200.

curl ${TOKEN:+-H "Authorization: Bearer $TOKEN"} "$BASE/api/modelServing/v1/modelApis?limit=1"

Three surfaces (do not merge)

Surface	Purpose
Model Serving REST	`/api/modelServing/v1/modelApis` deploy, versions, lifecycle
Registered models REST	`/api/registeredmodels/v2` list/register; `/api/registeredmodels/v1/{modelName}` get/update/versions
MLflow tracking	`MLFLOW_TRACKING_URI` / runs UI; not the same as registered-models REST

Logging a run to MLflow does not replace registry or modelServing calls for deployment automation.

Management vs inference URL

Create/list/update: platform base + /api/modelServing/v1/... (or registered-models paths).
Predict: use the url field on the model API or version (often .../models/.../latest/model), on the deployment ingress. That path is not routed through DOMINO_API_HOST sidecar the same way as /api/.

GenAI: management API vs https://.../endpoints/{vanity} split. See model-endpoints and the GenAI section in API-MODEL-SERVING.md.

Lifecycle (model APIs)

Typical order:

Train / register (MLflow and/or POST /api/registeredmodels/v2/...).
POST /api/modelServing/v1/modelApis (or new version on existing API).
Poll GET /api/modelServing/v1/modelApis/{modelApiId} until status is Running (or Failed); then read url for invoke.
Stop vs archive vs delete are different product operations; names and reliability differ by route.
Treat DELETE on deployments/model APIs as best-effort; verify resource gone before assuming cleanup.
Stop may be long-running and not idempotent; poll status instead of fire-and-forget retry loops.

Details and route list: API-MODEL-SERVING.md.

Registered model path key

modelName in /api/registeredmodels/v1/{modelName} is the registered model name string, not an opaque UUID. List/register often use v2; get/update/versions use v1 with that name in the path.

Invocation auth

Use the model API token documented on https://docs.domino.ai for the model endpoint invoke path. Older examples may show a basic-auth pattern with the token as both user and password; treat that as legacy and confirm in API-SPECS.md before hardcoding.