> ## Documentation Index > Fetch the complete documentation index at: https://docs.domino.ai/llms.txt > Use this file to discover all available pages before exploring further. # Get Logged Model ## OpenAPI ````yaml /api-specs/cloud/public-api.json get /api/2.0/mlflow/logged-models/{model_id} openapi: 3.0.3 info: title: Domino Public API description: Reference for Domino's public REST API endpoints. version: 6.4.0 x-catalog-key: nucleus servers: - url: https://mycluster.domino.tech description: >- Replace 'mycluster.domino.tech' with your Domino cluster hostname. For Domino Cloud customers, that is `your-subdomain`.domino.tech (e.g., acme.domino.tech). For self-hosted deployments, it is the hostname you reach the Domino UI at. security: [] tags: - name: Projects - name: Project templates - name: Workspaces - name: Jobs - name: HPC Jobs - name: Environments - name: Hardware Tiers - name: Datasets - name: Data Sources - name: GenAI - name: AI Systems - name: MLflow experiments and runs - name: MLflow models - name: MLflow artifacts - name: MLflow traces - name: Apps - name: App versions - name: App instances - name: Model APIs - name: Registered models - name: Model deployment - name: Extensions - name: Cost and billing - name: Users and organizations - name: Service accounts - name: Personal Access Tokens - name: Personal Access Tokens (admin) - name: Audit Trail - name: Custom metrics - name: PPM - name: Model Monitoring models - name: Model Monitoring drift and quality - name: Model Monitoring settings - name: Model Monitoring authentication - name: Governance bundles - name: Governance policies - name: Governance evidence and results - name: Governance operations - name: NetApp Volumes - name: NetApp Volumes snapshots - name: NetApp Volumes filesystems - name: NetApp Volumes transfers - name: Tags - name: Properties externalDocs: description: OpenAPI url: https://swagger.io/resources/open-api/ paths: /api/2.0/mlflow/logged-models/{model_id}: get: tags: - MLflow models summary: Get Logged Model operationId: loggedmodel.getLoggedModel parameters: - name: model_id in: path required: true description: The ID of the LoggedModel to retrieve. schema: type: string responses: '200': description: Successful response content: application/json: schema: $ref: '#/components/schemas/mlflow-proxy_GetLoggedModelResponse' components: schemas: mlflow-proxy_GetLoggedModelResponse: type: object description: Response body for retrieving a logged model. properties: model: $ref: '#/components/schemas/mlflow-proxy_LoggedModel' mlflow-proxy_LoggedModel: type: object description: >- A LoggedModel message includes logged model attributes, tags, registration info, params, and linked run metrics. properties: info: $ref: '#/components/schemas/mlflow-proxy_LoggedModelInfo' data: $ref: '#/components/schemas/mlflow-proxy_LoggedModelData' mlflow-proxy_LoggedModelInfo: type: object description: >- A LoggedModelInfo includes logged model attributes, tags, and registration info. properties: model_id: type: string description: A unique identifier for the model. example: logged_model_12345 experiment_id: type: string description: The ID of the experiment that owns the model. example: exp_001 name: type: string description: Name of the model. example: my_awesome_model creation_timestamp_ms: type: integer format: int64 description: >- Timestamp when the model was created, in milliseconds since the UNIX epoch. example: 1678886400000 last_updated_timestamp_ms: type: integer format: int64 description: >- Timestamp when the model was last updated, in milliseconds since the UNIX epoch. example: 1678887000000 artifact_uri: type: string description: URI of the directory where model artifacts are stored. example: s3://my-bucket/models/my_awesome_model status: $ref: '#/components/schemas/mlflow-proxy_LoggedModelStatus' creator_id: type: integer format: int64 description: The ID of the user or principal that created the model. example: 12345 model_type: type: string description: The type of model, such as "Agent", "Classifier", "LLM". example: Classifier source_run_id: type: string description: Run ID of the run that created the model. example: run_abcd1234efgh5678 status_message: type: string description: Details on the current status. example: Model successfully logged. tags: type: array description: Mutable String key-value pairs set on the model. items: $ref: '#/components/schemas/mlflow-proxy_Tag' registrations: type: array description: >- If the model has been promoted to the Model Registry, this field includes information like the Registered Model name, Model Version number, etc. items: $ref: '#/components/schemas/mlflow-proxy_LoggedModelRegistrationInfo' mlflow-proxy_LoggedModelData: type: object description: >- A LoggedModelData message includes logged model params and linked metrics. properties: params: type: array description: Immutable String key-value pairs of the model. items: $ref: '#/components/schemas/mlflow-proxy_LoggedModelParameter' metrics: type: array description: Performance metrics linked to the model. items: $ref: '#/components/schemas/mlflow-proxy_Metric' mlflow-proxy_LoggedModelStatus: type: string description: > Whether or not the model is ready for use. Valid values in this message: LOGGED_MODEL_READY, LOGGED_MODEL_UPLOAD_FAILED ("LOGGED_MODEL_UPLOAD_FAILED" indicates that something went wrong when logging the model weights / agent code) enum: - LOGGED_MODEL_STATUS_UNSPECIFIED - LOGGED_MODEL_PENDING - LOGGED_MODEL_READY - LOGGED_MODEL_UPLOAD_FAILED mlflow-proxy_Tag: properties: key: description: >- Name of the tag. Maximum size depends on storage backend. If a tag with this name already exists, its preexisting value will be replaced by the specified value. All storage backends are guaranteed to support key values up to 250 bytes in size. This field is required. type: string value: description: >- String value of the tag being logged. Maximum size depends on storage backend. All storage backends are guaranteed to support key values up to 5000 bytes in size. This field is required. type: string required: - key - value type: object mlflow-proxy_LoggedModelRegistrationInfo: type: object description: RegistrationInfo for a LoggedModel. properties: name: type: string description: >- The name of the Registered Model to which the model has been promoted. version: type: string description: The version number of the promoted model. required: - name - version mlflow-proxy_LoggedModelParameter: type: object description: Parameter associated with a LoggedModel. properties: key: type: string description: Key identifying this parameter. value: type: string description: Value associated with this parameter. required: - key - value mlflow-proxy_Metric: properties: key: description: Key identifying this metric. type: string step: default: '0' description: Step at which to log the metric. type: integer format: int64 timestamp: description: The timestamp at which this metric was recorded. oneOf: - format: int64 type: integer - type: string value: description: Value associated with this metric. oneOf: - format: double type: number - type: string required: - key - value - step - timestamp type: object ```` ## Related topics - [Get unregistered models for an experiment](/api-reference/registered-models/get-unregistered-models-for-an-experiment.md) - [Finalize Logged Model](/api-reference/mlflow-models/finalize-logged-model.md) - [Create Logged Model](/api-reference/mlflow-models/create-logged-model.md) - [Delete a Logged Model](/api-reference/mlflow-models/delete-a-logged-model.md)