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Internal package providing a Python CRUD interface to MLflow experiments, runs, registered models,
and model versions. This is a lower level API than the :py:mod:`mlflow.tracking.fluent` module,
and is exposed in the :py:mod:`mlflow.tracking` module.
from mlflow.entities import ViewType
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import FEATURE_DISABLED
from mlflow.tracking._model_registry.client import ModelRegistryClient
from mlflow.tracking.registry import UnsupportedModelRegistryStoreURIException
from mlflow.tracking._tracking_service import utils
from mlflow.tracking._tracking_service.client import TrackingServiceClient
from mlflow.utils import experimental
class MlflowClient(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.
def __init__(self, tracking_uri=None, registry_uri=None):
:param tracking_uri: Address of local or remote tracking server. If not provided, defaults
to the service set by ``mlflow.tracking.set_tracking_uri``. See
`Where Runs Get Recorded <../tracking.html#where-runs-get-recorded>`_
for more info.
:param registry_uri: Address of local or remote model registry server. If not provided,
defaults to the service set by ``mlflow.tracking.set_tracking_uri``.
final_tracking_uri = tracking_uri or utils.get_tracking_uri()
self._registry_uri = registry_uri or final_tracking_uri
self._tracking_client = TrackingServiceClient(final_tracking_uri)
# `MlflowClient` also references a `ModelRegistryClient` instance that is provided by the
# `MlflowClient._get_registry_client()` method. This `ModelRegistryClient` is not explicitly
# defined as an instance variable in the `MlflowClient` constructor; an instance variable
# is assigned lazily by `MlflowClient._get_registry_client()` and should not be referenced
# outside of the `MlflowClient._get_registry_client()` method
def _get_registry_client(self):
Attempts to create a py:class:`ModelRegistryClient` if one does not already exist.
:raises: py:class:`mlflow.exceptions.MlflowException` if the py:class:`ModelRegistryClient`
cannot be created. This may occur, for example, when the registry URI refers
to an unsupported store type (e.g., the FileStore).
:return: A py:class:`ModelRegistryClient` instance
# Attempt to fetch a `ModelRegistryClient` that is lazily instantiated and defined as
# an instance variable on this `MlflowClient` instance. Because the instance variable
# is undefined until the first invocation of _get_registry_client(), the `getattr()`
# function is used to safely fetch the variable (if it is defined) or a NoneType
# (if it is not defined)
registry_client_attr = "_registry_client_lazy"
registry_client = getattr(self, registry_client_attr, None)
if registry_client is None:
registry_client = ModelRegistryClient(self._registry_uri)
# Define an instance variable on this `MlflowClient` instance to reference the
# `ModelRegistryClient` that was just constructed. `setattr()` is used to ensure
# that the variable name is consistent with the variable name specified in the
# preceding call to `getattr()`
setattr(self, registry_client_attr, registry_client)
except UnsupportedModelRegistryStoreURIException as exc:
raise MlflowException(
"Model Registry features are not supported by the store with URI:"
" '{uri}'. Stores with the following URI schemes are supported:"
" {schemes}.".format(uri=self._registry_uri, schemes=exc.supported_uri_schemes),
return registry_client
# Tracking API
def get_run(self, run_id):
Fetch the run from backend store. The resulting :py:class:`Run <mlflow.entities.Run>`
contains a collection of run metadata -- :py:class:`RunInfo <mlflow.entities.RunInfo>`,
as well as a collection of run parameters, tags, and metrics --
:py:class:`RunData <mlflow.entities.RunData>`. In the case where multiple metrics with the
same key are logged for the run, the :py:class:`RunData <mlflow.entities.RunData>` contains
the most recently logged value at the largest step for each metric.
:param run_id: Unique identifier for the run.
:return: A single :py:class:`mlflow.entities.Run` object, if the run exists. Otherwise,
raises an exception.
return self._tracking_client.get_run(run_id)
def get_metric_history(self, run_id, key):
Return a list of metric objects corresponding to all values logged for a given metric.
:param run_id: Unique identifier for run
:param key: Metric name within the run
:return: A list of :py:class:`mlflow.entities.Metric` entities if logged, else empty list
return self._tracking_client.get_metric_history(run_id, key)
def create_run(self, experiment_id, start_time=None, tags=None):
Create a :py:class:`mlflow.entities.Run` object that can be associated with
metrics, parameters, artifacts, etc.
Unlike :py:func:``, creates objects but does not run code.
Unlike :py:func:`mlflow.start_run`, does not change the "active run" used by
:param experiment_id: The ID of then experiment to create a run in.
:param start_time: If not provided, use the current timestamp.
:param tags: A dictionary of key-value pairs that are converted into
:py:class:`mlflow.entities.RunTag` objects.
:return: :py:class:`mlflow.entities.Run` that was created.
return self._tracking_client.create_run(experiment_id, start_time, tags)
def list_run_infos(self, experiment_id, run_view_type=ViewType.ACTIVE_ONLY):
""":return: List of :py:class:`mlflow.entities.RunInfo`"""
return self._tracking_client.list_run_infos(experiment_id, run_view_type)
def list_experiments(self, view_type=None):
:return: List of :py:class:`mlflow.entities.Experiment`
return self._tracking_client.list_experiments(view_type)
def get_experiment(self, experiment_id):
Retrieve an experiment by experiment_id from the backend store
:param experiment_id: The experiment ID returned from ``create_experiment``.
:return: :py:class:`mlflow.entities.Experiment`
return self._tracking_client.get_experiment(experiment_id)
def get_experiment_by_name(self, name):
Retrieve an experiment by experiment name from the backend store
:param name: The experiment name.
:return: :py:class:`mlflow.entities.Experiment`
return self._tracking_client.get_experiment_by_name(name)
def create_experiment(self, name, artifact_location=None):
"""Create an experiment.
:param name: The experiment name. Must be unique.
:param artifact_location: The location to store run artifacts.
If not provided, the server picks an appropriate default.
:return: Integer ID of the created experiment.
return self._tracking_client.create_experiment(name, artifact_location)
def delete_experiment(self, experiment_id):
Delete an experiment from the backend store.
:param experiment_id: The experiment ID returned from ``create_experiment``.
def restore_experiment(self, experiment_id):
Restore a deleted experiment unless permanently deleted.
:param experiment_id: The experiment ID returned from ``create_experiment``.
def rename_experiment(self, experiment_id, new_name):
Update an experiment's name. The new name must be unique.
:param experiment_id: The experiment ID returned from ``create_experiment``.
self._tracking_client.rename_experiment(experiment_id, new_name)
def log_metric(self, run_id, key, value, timestamp=None, step=None):
Log a metric against the run ID.
:param run_id: The run id to which the metric should be logged.
:param key: Metric name.
:param 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.
:param timestamp: Time when this metric was calculated. Defaults to the current system time.
:param step: Training step (iteration) at which was the metric calculated. Defaults to 0.
self._tracking_client.log_metric(run_id, key, value, timestamp, step)
def log_param(self, run_id, key, value):
Log a parameter against the run ID. Value is converted to a string.
self._tracking_client.log_param(run_id, key, value)
def set_experiment_tag(self, 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).
self._tracking_client.set_experiment_tag(experiment_id, key, value)
def set_tag(self, 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)
self._tracking_client.set_tag(run_id, key, value)
def delete_tag(self, run_id, key):
Delete a tag from a run. This is irreversible.
:param run_id: String ID of the run
:param key: Name of the tag
self._tracking_client.delete_tag(run_id, key)
def log_batch(self, run_id, metrics=(), params=(), tags=()):
Log multiple metrics, params, and/or tags.
:param run_id: String ID of the run
:param metrics: If provided, List of Metric(key, value, timestamp) instances.
:param params: If provided, List of Param(key, value) instances.
:param tags: If provided, List of RunTag(key, value) instances.
Raises an MlflowException if any errors occur.
:return: None
self._tracking_client.log_batch(run_id, metrics, params, tags)
def log_artifact(self, run_id, local_path, artifact_path=None):
Write a local file or directory to the remote ``artifact_uri``.
:param local_path: Path to the file or directory to write.
:param artifact_path: If provided, the directory in ``artifact_uri`` to write to.
self._tracking_client.log_artifact(run_id, local_path, artifact_path)
def log_artifacts(self, run_id, local_dir, artifact_path=None):
Write a directory of files to the remote ``artifact_uri``.
:param local_dir: Path to the directory of files to write.
:param artifact_path: If provided, the directory in ``artifact_uri`` to write to.
self._tracking_client.log_artifacts(run_id, local_dir, artifact_path)
def _record_logged_model(self, run_id, mlflow_model):
Record logged model info with the tracking server.
:param run_id: run_id under which the model has been logged.
:param mlflow_model: Model info to be recorded.
self._tracking_client._record_logged_model(run_id, mlflow_model)
def list_artifacts(self, run_id, path=None):
List the artifacts for a run.
:param run_id: The run to list artifacts from.
:param path: The run's relative artifact path to list from. By default it is set to None
or the root artifact path.
:return: List of :py:class:`mlflow.entities.FileInfo`
return self._tracking_client.list_artifacts(run_id, path)
def download_artifacts(self, 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.
:param run_id: The run to download artifacts from.
:param path: Relative source path to the desired artifact.
:param 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.
:return: Local path of desired artifact.
return self._tracking_client.download_artifacts(run_id, path, dst_path)
def set_terminated(self, run_id, status=None, end_time=None):
"""Set a run's status to terminated.
:param status: A string value of :py:class:`mlflow.entities.RunStatus`.
Defaults to "FINISHED".
:param end_time: If not provided, defaults to the current time."""
self._tracking_client.set_terminated(run_id, status, end_time)
def delete_run(self, run_id):
Deletes a run with the given ID.
def restore_run(self, run_id):
Restores a deleted run with the given ID.
def search_runs(self, experiment_ids, filter_string="", run_view_type=ViewType.ACTIVE_ONLY,
max_results=SEARCH_MAX_RESULTS_DEFAULT, order_by=None, page_token=None):
Search experiments that fit the search criteria.
:param experiment_ids: List of experiment IDs, or a single int or string id.
:param filter_string: Filter query string, defaults to searching all runs.
:param run_view_type: one of enum values ACTIVE_ONLY, DELETED_ONLY, or ALL runs
defined in :py:class:`mlflow.entities.ViewType`.
:param max_results: Maximum number of runs desired.
:param 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``.
:param page_token: Token specifying the next page of results. It should be obtained from
a ``search_runs`` call.
:return: A list of :py:class:`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.
return self._tracking_client.search_runs(experiment_ids, filter_string, run_view_type,
max_results, order_by, page_token)
# Registry API
# Registered Model Methods
def create_registered_model(self, name):
Create a new registered model in backend store.
:param name: Name of the new model. This is expected to be unique in the backend store.
:return: A single object of :py:class:`mlflow.entities.model_registry.RegisteredModel`
created by backend.
return self._get_registry_client().create_registered_model(name)
def update_registered_model(self, name, new_name=None, description=None):
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.
:param name: Name of the registered model to update.
:param new_name: (Optional) New proposed name for the registered model.
:param description: (Optional) New description.
:return: A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
return self._get_registry_client().update_registered_model(name, new_name, description)
def delete_registered_model(self, name):
Delete registered model.
Backend raises exception if a registered model with given name does not exist.
:param name: Name of the registered model to update.
def list_registered_models(self):
List of all registered models.
:return: List of :py:class:`mlflow.entities.model_registry.RegisteredModel` objects.
return self._get_registry_client().list_registered_models()
def get_registered_model_details(self, name):
:param name: Name of the registered model to update.
:return: A single :py:class:`mlflow.entities.model_registry.RegisteredModelDetailed` object.
return self._get_registry_client().get_registered_model_details(name)
def get_latest_versions(self, name, stages=None):
Latest version models for each requests stage. If no ``stages`` provided, returns the
latest version for each stage.
:param name: Name of the registered model to update.
:param stages: List of desired stages. If input list is None, return latest versions for
:return: List of `:py:class:`mlflow.entities.model_registry.ModelVersionDetailed` objects.
return self._get_registry_client().get_latest_versions(name, stages)
# Model Version Methods
def create_model_version(self, name, source, run_id):
Create a new model version from given source or run ID.
:param name: Name ID for containing registered model.
:param source: Source path where the MLflow model is stored.
:param run_id: Run ID from MLflow tracking server that generated the model
:return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by
return self._get_registry_client().create_model_version(name, source, run_id)
def update_model_version(self, name, version, stage=None, description=None):
Update metadata associated with a model version in backend.
:param name: Name of the containing registered model.
:param version: Version number of the model version.
:param stage: New desired stage for this model version.
:param description: New description.
self._get_registry_client().update_model_version(name, version, stage, description)
def delete_model_version(self, name, version):
Delete model version in backend.
:param name: Name of the containing registered model.
:param version: Version number of the model version.
self._get_registry_client().delete_model_version(name, version)
def get_model_version_details(self, name, version):
:param name: Name of the containing registered model.
:param version: Version number of the model version.
:return: A single :py:class:`mlflow.entities.model_registry.ModelVersionDetailed` object.
return self._get_registry_client().get_model_version_details(name, version)
def get_model_version_download_uri(self, name, version):
Get the download location in Model Registry for this model version.
:param name: Name of the containing registered model.
:param version: Version number of the model version.
:return: A single URI location that allows reads for downloading.
return self._get_registry_client().get_model_version_download_uri(name, version)
def search_model_versions(self, filter_string):
Search for model versions in backend that satisfy the filter criteria.
:param filter_string: A filter string expression. Currently supports a single filter
condition either name of model like ``name = 'model_name'`` or
``run_id = '...'``.
:return: PagedList of :py:class:`mlflow.entities.model_registry.ModelVersion` objects.
return self._get_registry_client().search_model_versions(filter_string)
def get_model_version_stages(self, name, version):
:return: A list of valid stages.
return self._get_registry_client().get_model_version_stages(name, version)
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