mlflow.tracking

The mlflow.tracking module provides a Python CRUD interface to MLflow experiments and runs. This is a lower level API that directly translates to MLflow REST API calls. For a higher level API for managing an “active run”, use the mlflow module.

class mlflow.tracking.MlflowClient(tracking_uri=None, registry_uri=None)

Bases: object

Client of an MLflow Tracking Server that creates and manages experiments and runs, and of an MLflow Registry Server that creates and manages registered models and model versions. It’s a thin wrapper around TrackingServiceClient and RegistryClient so there is a unified API but we can keep the implementation of the tracking and registry clients independent from each other.

create_experiment(name, artifact_location=None)

Create an experiment.

Parameters:
  • name – The experiment name. Must be unique.
  • artifact_location – The location to store run artifacts. If not provided, the server picks an appropriate default.
Returns:

Integer ID of the created experiment.

create_model_version(name, source, run_id)

Note

Experimental: This method may change or be removed in a future release without warning.

Create a new model version from given source or run ID.

Parameters:
  • name – Name ID for containing registered model.
  • source – Source path where the MLflow model is stored.
  • run_id – Run ID from MLflow tracking server that generated the model
Returns:

Single mlflow.entities.model_registry.ModelVersion object created by backend.

create_registered_model(name)

Note

Experimental: This method may change or be removed in a future release without warning.

Create a new registered model in backend store.

Parameters:name – Name of the new model. This is expected to be unique in the backend store.
Returns:A single object of mlflow.entities.model_registry.RegisteredModel created by backend.
create_run(experiment_id, start_time=None, tags=None)

Create a mlflow.entities.Run object that can be associated with metrics, parameters, artifacts, etc. Unlike mlflow.projects.run(), creates objects but does not run code. Unlike mlflow.start_run(), does not change the “active run” used by mlflow.log_param().

Parameters:
  • experiment_id – The ID of then experiment to create a run in.
  • start_time – If not provided, use the current timestamp.
  • tags – A dictionary of key-value pairs that are converted into mlflow.entities.RunTag objects.
Returns:

mlflow.entities.Run that was created.

delete_experiment(experiment_id)

Delete an experiment from the backend store.

Parameters:experiment_id – The experiment ID returned from create_experiment.
delete_model_version(name, version)

Note

Experimental: This method may change or be removed in a future release without warning.

Delete model version in backend.

Parameters:
  • name – Name of the containing registered model.
  • version – Version number of the model version.
delete_registered_model(name)

Note

Experimental: This method may change or be removed in a future release without warning.

Delete registered model. Backend raises exception if a registered model with given name does not exist.

Parameters:name – Name of the registered model to update.
delete_run(run_id)

Deletes a run with the given ID.

delete_tag(run_id, key)

Delete a tag from a run. This is irreversible.

Parameters:
  • run_id – String ID of the run
  • key – Name of the tag
download_artifacts(run_id, path, dst_path=None)

Download an artifact file or directory from a run to a local directory if applicable, and return a local path for it.

Parameters:
  • run_id – The run to download artifacts from.
  • path – Relative source path to the desired artifact.
  • dst_path – Absolute path of the local filesystem destination directory to which to download the specified artifacts. This directory must already exist. If unspecified, the artifacts will either be downloaded to a new uniquely-named directory on the local filesystem or will be returned directly in the case of the LocalArtifactRepository.
Returns:

Local path of desired artifact.

get_experiment(experiment_id)

Retrieve an experiment by experiment_id from the backend store

Parameters:experiment_id – The experiment ID returned from create_experiment.
Returns:mlflow.entities.Experiment
get_experiment_by_name(name)

Retrieve an experiment by experiment name from the backend store

Parameters:name – The experiment name.
Returns:mlflow.entities.Experiment
get_latest_versions(name, stages=None)

Note

Experimental: This method may change or be removed in a future release without warning.

Latest version models for each requests stage. If no stages provided, returns the latest version for each stage.

Parameters:
  • name – Name of the registered model to update.
  • stages – List of desired stages. If input list is None, return latest versions for for ALL_STAGES.
Returns:

List of :py:class:`mlflow.entities.model_registry.ModelVersionDetailed objects.

get_metric_history(run_id, key)

Return a list of metric objects corresponding to all values logged for a given metric.

Parameters:
  • run_id – Unique identifier for run
  • key – Metric name within the run
Returns:

A list of mlflow.entities.Metric entities if logged, else empty list

get_model_version_details(name, version)

Note

Experimental: This method may change or be removed in a future release without warning.

Parameters:
  • name – Name of the containing registered model.
  • version – Version number of the model version.
Returns:

A single mlflow.entities.model_registry.ModelVersionDetailed object.

get_model_version_download_uri(name, version)

Note

Experimental: This method may change or be removed in a future release without warning.

Get the download location in Model Registry for this model version.

Parameters:
  • name – Name of the containing registered model.
  • version – Version number of the model version.
Returns:

A single URI location that allows reads for downloading.

get_model_version_stages(name, version)

Note

Experimental: This method may change or be removed in a future release without warning.

Returns:A list of valid stages.
get_registered_model_details(name)
Parameters:name – Name of the registered model to update.
Returns:A single mlflow.entities.model_registry.RegisteredModelDetailed object.
get_run(run_id)

Fetch the run from backend store. The resulting Run contains a collection of run metadata – RunInfo, as well as a collection of run parameters, tags, and metrics – RunData. In the case where multiple metrics with the same key are logged for the run, the RunData contains the most recently logged value at the largest step for each metric.

Parameters:run_id – Unique identifier for the run.
Returns:A single mlflow.entities.Run object, if the run exists. Otherwise, raises an exception.
list_artifacts(run_id, path=None)

List the artifacts for a run.

Parameters:
  • run_id – The run to list artifacts from.
  • path – The run’s relative artifact path to list from. By default it is set to None or the root artifact path.
Returns:

List of mlflow.entities.FileInfo

list_experiments(view_type=None)
Returns:List of mlflow.entities.Experiment
list_registered_models()

Note

Experimental: This method may change or be removed in a future release without warning.

List of all registered models.

Returns:List of mlflow.entities.registry.RegisteredModel objects.
list_run_infos(experiment_id, run_view_type=1)
Returns:List of mlflow.entities.RunInfo
log_artifact(run_id, local_path, artifact_path=None)

Write a local file or directory to the remote artifact_uri.

Parameters:
  • local_path – Path to the file or directory to write.
  • artifact_path – If provided, the directory in artifact_uri to write to.
log_artifacts(run_id, local_dir, artifact_path=None)

Write a directory of files to the remote artifact_uri.

Parameters:
  • local_dir – Path to the directory of files to write.
  • artifact_path – If provided, the directory in artifact_uri to write to.
log_batch(run_id, metrics=(), params=(), tags=())

Log multiple metrics, params, and/or tags.

Parameters:
  • run_id – String ID of the run
  • metrics – If provided, List of Metric(key, value, timestamp) instances.
  • params – If provided, List of Param(key, value) instances.
  • tags – If provided, List of RunTag(key, value) instances.

Raises an MlflowException if any errors occur. :return: None

log_metric(run_id, key, value, timestamp=None, step=None)

Log a metric against the run ID.

Parameters:
  • run_id – The run id to which the metric should be logged.
  • key – Metric name.
  • value – Metric value (float). Note that some special values such as +/- Infinity may be replaced by other values depending on the store. For example, the SQLAlchemy store replaces +/- Inf with max / min float values.
  • timestamp – Time when this metric was calculated. Defaults to the current system time.
  • step – Training step (iteration) at which was the metric calculated. Defaults to 0.
log_param(run_id, key, value)

Log a parameter against the run ID. Value is converted to a string.

rename_experiment(experiment_id, new_name)

Update an experiment’s name. The new name must be unique.

Parameters:experiment_id – The experiment ID returned from create_experiment.
restore_experiment(experiment_id)

Restore a deleted experiment unless permanently deleted.

Parameters:experiment_id – The experiment ID returned from create_experiment.
restore_run(run_id)

Restores a deleted run with the given ID.

search_model_versions(filter_string)

Note

Experimental: This method may change or be removed in a future release without warning.

Search for model versions in backend that satisfy the filter criteria.

Parameters:filter_string – A filter string expression. Currently supports a single filter condition either name of model like name = 'model_name' or run_id = '...'.
Returns:PagedList of mlflow.entities.model_registry.ModelVersion objects.
search_runs(experiment_ids, filter_string='', run_view_type=1, max_results=1000, order_by=None, page_token=None)

Search experiments that fit the search criteria.

Parameters:
  • experiment_ids – List of experiment IDs, or a single int or string id.
  • filter_string – Filter query string, defaults to searching all runs.
  • run_view_type – one of enum values ACTIVE_ONLY, DELETED_ONLY, or ALL runs defined in mlflow.entities.ViewType.
  • max_results – Maximum number of runs desired.
  • order_by – List of columns to order by (e.g., “metrics.rmse”). The order_by column can contain an optional DESC or ASC value. The default is ASC. The default ordering is to sort by start_time DESC, then run_id.
  • page_token – Token specifying the next page of results. It should be obtained from a search_runs call.
Returns:

A list of mlflow.entities.Run objects that satisfy the search expressions. If the underlying tracking store supports pagination, the token for the next page may be obtained via the token attribute of the returned object.

set_experiment_tag(experiment_id, key, value)

Set a tag on the experiment with the specified ID. Value is converted to a string. :param experiment_id: String ID of the experiment. :param key: Name of the tag. :param value: Tag value (converted to a string).

set_tag(run_id, key, value)

Set a tag on the run with the specified ID. Value is converted to a string. :param run_id: String ID of the run. :param key: Name of the tag. :param value: Tag value (converted to a string)

set_terminated(run_id, status=None, end_time=None)

Set a run’s status to terminated.

Parameters:
  • status – A string value of mlflow.entities.RunStatus. Defaults to “FINISHED”.
  • end_time – If not provided, defaults to the current time.
update_model_version(name, version, stage=None, description=None)

Note

Experimental: This method may change or be removed in a future release without warning.

Update metadata associated with a model version in backend.

Parameters:
  • name – Name of the containing registered model.
  • version – Version number of the model version.
  • stage – New desired stage for this model version.
  • description – New description.
update_registered_model(name, new_name=None, description=None)

Note

Experimental: This method may change or be removed in a future release without warning.

Updates metadata for RegisteredModel entity. Either new_name or description should be non-None. Backend raises exception if a registered model with given name does not exist.

Parameters:
  • name – Name of the registered model to update.
  • new_name – (Optional) New proposed name for the registered model.
  • description – (Optional) New description.
Returns:

A single updated mlflow.entities.model_registry.RegisteredModel object.

mlflow.tracking.get_tracking_uri()

Get the current tracking URI. This may not correspond to the tracking URI of the currently active run, since the tracking URI can be updated via set_tracking_uri.

Returns:The tracking URI.
mlflow.tracking.set_tracking_uri(uri)

Set the tracking server URI. This does not affect the currently active run (if one exists), but takes effect for successive runs.

Parameters:uri
  • An empty string, or a local file path, prefixed with file:/. Data is stored locally at the provided file (or ./mlruns if empty).
  • An HTTP URI like https://my-tracking-server:5000.
  • A Databricks workspace, provided as the string “databricks” or, to use a Databricks CLI profile, “databricks://<profileName>”.