Package com.databricks.sdk.service.ml

Interface ExperimentsService

  • @Generated
public interface ExperimentsService
    Experiments are the primary unit of organization in MLflow; all MLflow runs belong to an experiment. Each experiment lets you visualize, search, and compare runs, as well as download run artifacts or metadata for analysis in other tools. Experiments are maintained in a Databricks hosted MLflow tracking server.

    Experiments are located in the workspace file tree. You manage experiments using the same tools you use to manage other workspace objects such as folders, notebooks, and libraries.

    This is the high-level interface, that contains generated methods.

    Evolving: this interface is under development. Method signatures may change.

    • Method Detail

      • createExperiment

        CreateExperimentResponse createExperiment​(CreateExperiment createExperiment)
        Create experiment.

        Creates an experiment with a name. Returns the ID of the newly created experiment. Validates that another experiment with the same name does not already exist and fails if another experiment with the same name already exists.

        Throws `RESOURCE_ALREADY_EXISTS` if a experiment with the given name exists.

      • createRun

        CreateRunResponse createRun​(CreateRun createRun)
        Create a run.

        Creates a new run within an experiment. A run is usually a single execution of a machine learning or data ETL pipeline. MLflow uses runs to track the `mlflowParam`, `mlflowMetric` and `mlflowRunTag` associated with a single execution.

      • deleteExperiment

        void deleteExperiment​(DeleteExperiment deleteExperiment)
        Delete an experiment.

        Marks an experiment and associated metadata, runs, metrics, params, and tags for deletion. If the experiment uses FileStore, artifacts associated with experiment are also deleted.

      • deleteRun

        void deleteRun​(DeleteRun deleteRun)
        Delete a run.

        Marks a run for deletion.

      • deleteTag

        void deleteTag​(DeleteTag deleteTag)
        Delete a tag.

        Deletes a tag on a run. Tags are run metadata that can be updated during a run and after a run completes.

      • getByName

        GetExperimentByNameResponse getByName​(GetByNameRequest getByNameRequest)
        Get metadata.

        Gets metadata for an experiment.

        This endpoint will return deleted experiments, but prefers the active experiment if an active and deleted experiment share the same name. If multiple deleted experiments share the same name, the API will return one of them.

        Throws `RESOURCE_DOES_NOT_EXIST` if no experiment with the specified name exists.

      • getExperiment

        Experiment getExperiment​(GetExperimentRequest getExperimentRequest)
        Get an experiment.

        Gets metadata for an experiment. This method works on deleted experiments.

      • getRun

        GetRunResponse getRun​(GetRunRequest getRunRequest)
        Get a run.

        Gets the metadata, metrics, params, and tags for a run. In the case where multiple metrics with the same key are logged for a run, return only the value with the latest timestamp.

        If there are multiple values with the latest timestamp, return the maximum of these values.

      • listArtifacts

        ListArtifactsResponse listArtifacts​(ListArtifactsRequest listArtifactsRequest)
        Get all artifacts.

        List artifacts for a run. Takes an optional `artifact_path` prefix. If it is specified, the response contains only artifacts with the specified prefix.",

      • logBatch

        void logBatch​(LogBatch logBatch)
        Log a batch.

        Logs a batch of metrics, params, and tags for a run. If any data failed to be persisted, the server will respond with an error (non-200 status code).

        In case of error (due to internal server error or an invalid request), partial data may be written.

        You can write metrics, params, and tags in interleaving fashion, but within a given entity type are guaranteed to follow the order specified in the request body.

        The overwrite behavior for metrics, params, and tags is as follows:

        * Metrics: metric values are never overwritten. Logging a metric (key, value, timestamp) appends to the set of values for the metric with the provided key.

        * Tags: tag values can be overwritten by successive writes to the same tag key. That is, if multiple tag values with the same key are provided in the same API request, the last-provided tag value is written. Logging the same tag (key, value) is permitted. Specifically, logging a tag is idempotent.

        * Parameters: once written, param values cannot be changed (attempting to overwrite a param value will result in an error). However, logging the same param (key, value) is permitted. Specifically, logging a param is idempotent.

        Request Limits ------------------------------- A single JSON-serialized API request may be up to 1 MB in size and contain:

        * No more than 1000 metrics, params, and tags in total * Up to 1000 metrics * Up to 100 params * Up to 100 tags

        For example, a valid request might contain 900 metrics, 50 params, and 50 tags, but logging 900 metrics, 50 params, and 51 tags is invalid.

        The following limits also apply to metric, param, and tag keys and values:

        * Metric keys, param keys, and tag keys can be up to 250 characters in length * Parameter and tag values can be up to 250 characters in length

      • logInputs

        void logInputs​(LogInputs logInputs)
        Log inputs to a run.

        **NOTE:** Experimental: This API may change or be removed in a future release without warning.

      • logMetric

        void logMetric​(LogMetric logMetric)
        Log a metric.

        Logs a metric for a run. A metric is a key-value pair (string key, float value) with an associated timestamp. Examples include the various metrics that represent ML model accuracy. A metric can be logged multiple times.

      • logModel

        void logModel​(LogModel logModel)
        Log a model.

        **NOTE:** Experimental: This API may change or be removed in a future release without warning.

      • logParam

        void logParam​(LogParam logParam)
        Log a param.

        Logs a param used for a run. A param is a key-value pair (string key, string value). Examples include hyperparameters used for ML model training and constant dates and values used in an ETL pipeline. A param can be logged only once for a run.

      • restoreExperiment

        void restoreExperiment​(RestoreExperiment restoreExperiment)
        Restores an experiment.

        Restore an experiment marked for deletion. This also restores associated metadata, runs, metrics, params, and tags. If experiment uses FileStore, underlying artifacts associated with experiment are also restored.

        Throws `RESOURCE_DOES_NOT_EXIST` if experiment was never created or was permanently deleted.

      • restoreRun

        void restoreRun​(RestoreRun restoreRun)
        Restore a run.

        Restores a deleted run.

      • searchRuns

        SearchRunsResponse searchRuns​(SearchRuns searchRuns)
        Search for runs.

        Searches for runs that satisfy expressions.

        Search expressions can use `mlflowMetric` and `mlflowParam` keys.",

      • setExperimentTag

        void setExperimentTag​(SetExperimentTag setExperimentTag)
        Set a tag.

        Sets a tag on an experiment. Experiment tags are metadata that can be updated.

      • setTag

        void setTag​(SetTag setTag)
        Set a tag.

        Sets a tag on a run. Tags are run metadata that can be updated during a run and after a run completes.

      • updateExperiment

        void updateExperiment​(UpdateExperiment updateExperiment)
        Update an experiment.

        Updates experiment metadata.