experiment_runs

Operations on a experiment_runs resource.

Overview

Name	experiment_runs
Type	Resource
Id	databricks_workspace.machinelearning.experiment_runs

Fields

Name	Datatype
data	object
info	object
inputs	object

Methods

Name	Accessible by	Required Params	Description
getrun	SELECT	run_id, deployment_name	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.
searchruns	SELECT	deployment_name	Searches for runs that satisfy expressions.
createrun	INSERT	deployment_name	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
deleterun	DELETE	deployment_name	Marks a run for deletion.
deleteruns	DELETE	deployment_name	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
updaterun	UPDATE	deployment_name	Updates run metadata.
logbatch	EXEC	deployment_name	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).
loginputs	EXEC	deployment_name	Experimental: This API may change or be removed in a future release without warning.
logmetric	EXEC	deployment_name	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	EXEC	deployment_name	Experimental: This API may change or be removed in a future release without warning.
logparam	EXEC	deployment_name	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.
restorerun	EXEC	deployment_name	Restores a deleted run.
restoreruns	EXEC	deployment_name	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

SELECT examples

experiment_runs (searchruns)

SELECT
data,
info,
inputs
FROM databricks_workspace.machinelearning.experiment_runs
WHERE deployment_name = '{{ deployment_name }}';

experiment_runs (getrun)

SELECT
data,
info,
inputs
FROM databricks_workspace.machinelearning.experiment_runs
WHERE run_id = '{{ run_id }}' AND
deployment_name = '{{ deployment_name }}';

INSERT example

Use the following StackQL query and manifest file to create a new experiment_runs resource.

experiment_runs

/*+ create */
INSERT INTO databricks_workspace.machinelearning.experiment_runs (
deployment_name,
data__experiment_id,
data__user_id,
data__start_time,
data__tags
)
SELECT 
'{{ deployment_name }}',
'{{ experiment_id }}',
'{{ user_id }}',
'{{ start_time }}',
'{{ tags }}'
;

Manifest

- name: your_resource_model_name
  props:
  - name: experiment_id
    value: string
  - name: user_id
    value: string
  - name: start_time
    value: 0
  - name: tags
    value:
    - key: string
      value: string

UPDATE example

Updates a experiment_runs resource.

/*+ update */
-- replace field1, field2, etc. with the fields you want to update        
UPDATE databricks_workspace.machinelearning.experiment_runs
SET field1 = '{{ value1 }}',
field2 = '{{ value2 }}', ...
WHERE deployment_name = '{{ deployment_name }}';

DELETE example

Deletes a experiment_runs resource.

deleterun

/*+ delete */
DELETE FROM databricks_workspace.machinelearning.experiment_runs
WHERE deployment_name = '{{ deployment_name }}';

deleteruns

/*+ delete */
DELETE FROM databricks_workspace.machinelearning.experiment_runs
WHERE deployment_name = '{{ deployment_name }}';