> ## 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. # Search LoggedModels ## OpenAPI ````yaml /api-specs/cloud/public-api.json post /api/2.0/mlflow/logged-models/search 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/search: post: tags: - MLflow models summary: Search LoggedModels operationId: loggedmodel.searchLoggedModels requestBody: required: true content: application/json: schema: $ref: '#/components/schemas/mlflow-proxy_SearchLoggedModels' responses: '200': description: Successful response content: application/json: schema: $ref: '#/components/schemas/mlflow-proxy_SearchLoggedModelsResponse' components: schemas: mlflow-proxy_SearchLoggedModels: type: object description: Request body for searching logged models. properties: experiment_ids: type: array description: IDs of the Experiments in which to search for Logged Models. items: type: string filter: type: string description: > A filter expression over Logged Model info and data that allows returning a subset of Logged Models. The syntax is a subset of SQL that supports ANDing together binary operations Example: ``params.alpha < 0.3 AND metrics.accuracy > 0.9``. example: params.alpha < 0.3 AND metrics.accuracy > 0.9 datasets: type: array description: > List of datasets on which to apply the metrics filter clauses. For example, a filter with `metrics.accuracy > 0.9` and dataset info with name "test_dataset" means we will return all logged models with accuracy > 0.9 on the test_dataset. Metric values from ANY dataset matching the criteria are considered. If no datasets are specified, then metrics across all datasets are considered in the filter. items: $ref: '#/components/schemas/mlflow-proxy_Dataset' max_results: type: integer format: int32 description: Maximum number of Logged Models to return. Max threshold is 50. default: 50 example: 10 order_by: type: array description: >- List of columns for ordering the results, with additional fields for sorting criteria. items: $ref: '#/components/schemas/mlflow-proxy_OrderBy' page_token: type: string description: Token indicating the page of Logged Models to fetch. mlflow-proxy_SearchLoggedModelsResponse: type: object description: Response body for searching logged models. properties: models: type: array description: Logged Models that match the search criteria. items: $ref: '#/components/schemas/mlflow-proxy_LoggedModel' next_page_token: type: string description: Token that can be used to retrieve the next page of Logged Models. mlflow-proxy_Dataset: description: >- Represents a reference to data used for training, testing, or evaluation during the model development process. Experimental: This API may change or be removed in a future release without warning. properties: name: description: >- The name of the dataset. E.g. "my.uc.table@2" "nyc-taxi-dataset", "fantastic-elk-3" This field is required. type: string digest: description: >- Dataset digest, e.g. an md5 hash of the dataset that uniquely identifies it within datasets of the same name. This field is required. type: string source_type: description: >- Source information for the dataset. Note that the source may not exactly reproduce the dataset if it was transformed / modified before use with MLflow. This field is required. type: string source: description: >- The type of the dataset source, e.g. "databricks-uc-table", "DBFS", "S3", … This field is required. type: string schema: description: >- The schema of the dataset. E.g., MLflow ColSpec JSON for a dataframe, MLflow TensorSpec JSON for an ndarray, or another schema format. type: string profile: description: >- The profile of the dataset. Summary statistics for the dataset, such as the number of rows in a table, the mean / std / mode of each column in a table, or the number of elements in an array. type: string required: - name - digest - source_type - source type: object mlflow-proxy_OrderBy: type: object description: Criteria for ordering search results. properties: field_name: type: string description: Name of the field to order by, e.g. "metrics.accuracy". example: metrics.accuracy ascending: type: boolean description: Whether the order is ascending or not. default: true example: true dataset_name: type: string description: > If ``field_name`` refers to a metric, this field specifies the name of the dataset associated with the metric. Only metrics associated with the specified dataset name will be considered for ordering. This field may only be set if ``field_name`` refers to a metric. example: validation_set dataset_digest: type: string description: > If ``field_name`` refers to a metric, this field specifies the digest of the dataset associated with the metric. Only metrics associated with the specified dataset name and digest will be considered for ordering. This field may only be set if ``dataset_name`` is also set. required: - field_name 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 - [Create Logged Model](/api-reference/mlflow-models/create-logged-model.md) - [Finalize Logged Model](/api-reference/mlflow-models/finalize-logged-model.md) - [Delete a Logged Model](/api-reference/mlflow-models/delete-a-logged-model.md) - [Get Logged Model](/api-reference/mlflow-models/get-logged-model.md)