Class ExperimentsClient

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. Note: In some contexts, this error may be remapped to ALREADY_EXISTS. To be safe, clients should check for both error codes.

Create a logged model.

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.

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.

Delete a logged model.

Delete a tag on a logged model.

Marks a run for deletion.

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

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

Finalize a logged model.

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

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.

Get a logged model.

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.

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.

Gets a list of all experiments.

Gets a list of all values for the specified metric for a given run.

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

Logs inputs, such as datasets and models, to an MLflow Run.

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

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.

Note: the Create a logged model API replaces this endpoint.

Log a model to an MLflow Run.

Logs outputs, such as models, from an MLflow Run.

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.

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.

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.

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

Searches for experiments that satisfy specified search criteria.

Search for Logged Models that satisfy specified search criteria.

Searches for runs that satisfy expressions.

Search expressions can use mlflowMetric and mlflowParam keys.

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

Set tags for a logged model.

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

Updates experiment metadata.

Updates run metadata.