ExperimentsService (Databricks SDK for Java 0.94.0 API)

```
@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 Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method and Description
`CreateExperimentResponse`	`createExperiment(CreateExperiment createExperiment)` Creates an experiment with a name.
`CreateLoggedModelResponse`	`createLoggedModel(CreateLoggedModelRequest createLoggedModelRequest)` Create a logged model.
`CreateRunResponse`	`createRun(CreateRun createRun)` Creates a new run within an experiment.
`void`	`deleteExperiment(DeleteExperiment deleteExperiment)` Marks an experiment and associated metadata, runs, metrics, params, and tags for deletion.
`void`	`deleteLoggedModel(DeleteLoggedModelRequest deleteLoggedModelRequest)` Delete a logged model.
`void`	`deleteLoggedModelTag(DeleteLoggedModelTagRequest deleteLoggedModelTagRequest)` Delete a tag on a logged model.
`void`	`deleteRun(DeleteRun deleteRun)` Marks a run for deletion.
`DeleteRunsResponse`	`deleteRuns(DeleteRuns deleteRuns)` Bulk delete runs in an experiment that were created prior to or at the specified timestamp.
`void`	`deleteTag(DeleteTag deleteTag)` Deletes a tag on a run.
`FinalizeLoggedModelResponse`	`finalizeLoggedModel(FinalizeLoggedModelRequest finalizeLoggedModelRequest)` Finalize a logged model.
`GetExperimentByNameResponse`	`getByName(GetByNameRequest getByNameRequest)` Gets metadata for an experiment.
`GetExperimentResponse`	`getExperiment(GetExperimentRequest getExperimentRequest)` Gets metadata for an experiment.
`GetMetricHistoryResponse`	`getHistory(GetHistoryRequest getHistoryRequest)` Gets a list of all values for the specified metric for a given run.
`GetLoggedModelResponse`	`getLoggedModel(GetLoggedModelRequest getLoggedModelRequest)` Get a logged model.
`GetExperimentPermissionLevelsResponse`	`getPermissionLevels(GetExperimentPermissionLevelsRequest getExperimentPermissionLevelsRequest)` Gets the permission levels that a user can have on an object.
`ExperimentPermissions`	`getPermissions(GetExperimentPermissionsRequest getExperimentPermissionsRequest)` Gets the permissions of an experiment.
`GetRunResponse`	`getRun(GetRunRequest getRunRequest)` Gets the metadata, metrics, params, and tags for a run.
`ListArtifactsResponse`	`listArtifacts(ListArtifactsRequest listArtifactsRequest)` List artifacts for a run.
`ListExperimentsResponse`	`listExperiments(ListExperimentsRequest listExperimentsRequest)` Gets a list of all experiments.
`void`	`logBatch(LogBatch logBatch)` Logs a batch of metrics, params, and tags for a run.
`void`	`logInputs(LogInputs logInputs)` Logs inputs, such as datasets and models, to an MLflow Run.
`void`	`logLoggedModelParams(LogLoggedModelParamsRequest logLoggedModelParamsRequest)` Logs params for a logged model.
`void`	`logMetric(LogMetric logMetric)` Log a metric for a run.
`void`	`logModel(LogModel logModel)` Note: the [Create a logged model](/api/workspace/experiments/createloggedmodel) API replaces this endpoint.
`void`	`logOutputs(LogOutputsRequest logOutputsRequest)` Logs outputs, such as models, from an MLflow Run.
`void`	`logParam(LogParam logParam)` Logs a param used for a run.
`void`	`restoreExperiment(RestoreExperiment restoreExperiment)` Restore an experiment marked for deletion.
`void`	`restoreRun(RestoreRun restoreRun)` Restores a deleted run.
`RestoreRunsResponse`	`restoreRuns(RestoreRuns restoreRuns)` Bulk restore runs in an experiment that were deleted no earlier than the specified timestamp.
`SearchExperimentsResponse`	`searchExperiments(SearchExperiments searchExperiments)` Searches for experiments that satisfy specified search criteria.
`SearchLoggedModelsResponse`	`searchLoggedModels(SearchLoggedModelsRequest searchLoggedModelsRequest)` Search for Logged Models that satisfy specified search criteria.
`SearchRunsResponse`	`searchRuns(SearchRuns searchRuns)` Searches for runs that satisfy expressions.
`void`	`setExperimentTag(SetExperimentTag setExperimentTag)` Sets a tag on an experiment.
`void`	`setLoggedModelTags(SetLoggedModelTagsRequest setLoggedModelTagsRequest)` Set tags for a logged model.
`ExperimentPermissions`	`setPermissions(ExperimentPermissionsRequest experimentPermissionsRequest)` Sets permissions on an object, replacing existing permissions if they exist.
`void`	`setTag(SetTag setTag)` Sets a tag on a run.
`void`	`updateExperiment(UpdateExperiment updateExperiment)` Updates experiment metadata.
`ExperimentPermissions`	`updatePermissions(ExperimentPermissionsRequest experimentPermissionsRequest)` Updates the permissions on an experiment.
`UpdateRunResponse`	`updateRun(UpdateRun updateRun)` Updates run metadata.

- Method Detail
  - createExperiment
```
CreateExperimentResponse createExperiment(CreateExperiment createExperiment)
```
    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 an experiment with the given name exists.
  - createLoggedModel
```
CreateLoggedModelResponse createLoggedModel(CreateLoggedModelRequest createLoggedModelRequest)
```
    Create a logged model.
  - createRun
```
CreateRunResponse createRun(CreateRun createRun)
```
    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)
```
    Marks an experiment and associated metadata, runs, metrics, params, and tags for deletion. If the experiment uses FileStore, artifacts associated with the experiment are also deleted.
  - deleteLoggedModel
```
void deleteLoggedModel(DeleteLoggedModelRequest deleteLoggedModelRequest)
```
    Delete a logged model.
  - deleteLoggedModelTag
```
void deleteLoggedModelTag(DeleteLoggedModelTagRequest deleteLoggedModelTagRequest)
```
    Delete a tag on a logged model.
  - deleteRun
```
void deleteRun(DeleteRun deleteRun)
```
    Marks a run for deletion.
  - deleteRuns
```
DeleteRunsResponse deleteRuns(DeleteRuns deleteRuns)
```
    Bulk delete runs in an experiment that were created prior to or at the specified timestamp. Deletes at most max_runs per request. To call this API from a Databricks Notebook in Python, you can use the client code snippet on
  - deleteTag
```
void deleteTag(DeleteTag deleteTag)
```
    Deletes a tag on a run. Tags are run metadata that can be updated during a run and after a run completes.
  - finalizeLoggedModel
```
FinalizeLoggedModelResponse finalizeLoggedModel(FinalizeLoggedModelRequest finalizeLoggedModelRequest)
```
    Finalize a logged model.
  - getByName
```
GetExperimentByNameResponse getByName(GetByNameRequest getByNameRequest)
```
    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
```
GetExperimentResponse getExperiment(GetExperimentRequest getExperimentRequest)
```
    Gets metadata for an experiment. This method works on deleted experiments.
  - getHistory
```
GetMetricHistoryResponse getHistory(GetHistoryRequest getHistoryRequest)
```
    Gets a list of all values for the specified metric for a given run.
  - getLoggedModel
```
GetLoggedModelResponse getLoggedModel(GetLoggedModelRequest getLoggedModelRequest)
```
    Get a logged model.
  - getPermissionLevels
```
GetExperimentPermissionLevelsResponse getPermissionLevels(GetExperimentPermissionLevelsRequest getExperimentPermissionLevelsRequest)
```
    Gets the permission levels that a user can have on an object.
  - getPermissions
```
ExperimentPermissions getPermissions(GetExperimentPermissionsRequest getExperimentPermissionsRequest)
```
    Gets the permissions of an experiment. Experiments can inherit permissions from their root object.
  - getRun
```
GetRunResponse getRun(GetRunRequest getRunRequest)
```
    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)
```
    List artifacts for a run. Takes an optional `artifact_path` prefix which if specified, the response contains only artifacts with the specified prefix. A maximum of 1000 artifacts will be retrieved for UC Volumes. Please call `/api/2.0/fs/directories{directory_path}` for listing artifacts in UC Volumes, which supports pagination. See [List directory contents | Files API](/api/workspace/files/listdirectorycontents).
  - listExperiments
```
ListExperimentsResponse listExperiments(ListExperimentsRequest listExperimentsRequest)
```
    Gets a list of all experiments.
  - logBatch
```
void logBatch(LogBatch logBatch)
```
    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)
```
    Logs inputs, such as datasets and models, to an MLflow Run.
  - logLoggedModelParams
```
void logLoggedModelParams(LogLoggedModelParamsRequest logLoggedModelParamsRequest)
```
    Logs params for a logged model. A param is a key-value pair (string key, string value). Examples include hyperparameters used for ML model training. A param can be logged only once for a logged model, and attempting to overwrite an existing param with a different value will result in an error
  - logMetric
```
void logMetric(LogMetric logMetric)
```
    Log 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)
```
    **Note:** the [Create a logged model](/api/workspace/experiments/createloggedmodel) API replaces this endpoint.
    Log a model to an MLflow Run.
  - logOutputs
```
void logOutputs(LogOutputsRequest logOutputsRequest)
```
    Logs outputs, such as models, from an MLflow Run.
  - logParam
```
void logParam(LogParam logParam)
```
    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)
```
    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)
```
    Restores a deleted run. This also restores associated metadata, runs, metrics, params, and tags.
    Throws `RESOURCE_DOES_NOT_EXIST` if the run was never created or was permanently deleted.
  - restoreRuns
```
RestoreRunsResponse restoreRuns(RestoreRuns restoreRuns)
```
    Bulk restore runs in an experiment that were deleted no earlier than the specified timestamp. Restores at most max_runs per request. To call this API from a Databricks Notebook in Python, you can use the client code snippet on
  - searchExperiments
```
SearchExperimentsResponse searchExperiments(SearchExperiments searchExperiments)
```
    Searches for experiments that satisfy specified search criteria.
  - searchLoggedModels
```
SearchLoggedModelsResponse searchLoggedModels(SearchLoggedModelsRequest searchLoggedModelsRequest)
```
    Search for Logged Models that satisfy specified search criteria.
  - searchRuns
```
SearchRunsResponse searchRuns(SearchRuns searchRuns)
```
    Searches for runs that satisfy expressions.
    Search expressions can use `mlflowMetric` and `mlflowParam` keys.
  - setExperimentTag
```
void setExperimentTag(SetExperimentTag setExperimentTag)
```
    Sets a tag on an experiment. Experiment tags are metadata that can be updated.
  - setLoggedModelTags
```
void setLoggedModelTags(SetLoggedModelTagsRequest setLoggedModelTagsRequest)
```
    Set tags for a logged model.
  - setPermissions
```
ExperimentPermissions setPermissions(ExperimentPermissionsRequest experimentPermissionsRequest)
```
    Sets permissions on an object, replacing existing permissions if they exist. Deletes all direct permissions if none are specified. Objects can inherit permissions from their root object.
  - setTag
```
void setTag(SetTag setTag)
```
    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)
```
    Updates experiment metadata.
  - updatePermissions
```
ExperimentPermissions updatePermissions(ExperimentPermissionsRequest experimentPermissionsRequest)
```
    Updates the permissions on an experiment. Experiments can inherit permissions from their root object.
  - updateRun
```
UpdateRunResponse updateRun(UpdateRun updateRun)
```
    Updates run metadata.

Interface ExperimentsService

Method Summary

Method Detail

createExperiment

createLoggedModel

createRun

deleteExperiment

deleteLoggedModel

deleteLoggedModelTag

deleteRun

deleteRuns

deleteTag

finalizeLoggedModel

getByName

getExperiment

getHistory

getLoggedModel

getPermissionLevels

getPermissions

getRun

listArtifacts

listExperiments

logBatch

logInputs

logLoggedModelParams

logMetric

logModel

logOutputs

logParam

restoreExperiment

restoreRun

restoreRuns

searchExperiments

searchLoggedModels

searchRuns

setExperimentTag

setLoggedModelTags

setPermissions

setTag

updateExperiment

updatePermissions

updateRun