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)
def test_get_artifact_repo(artifact_uri, databricks_uri, uri_for_repo):
with mock.patch(
"mlflow.tracking._tracking_service.client.TrackingServiceClient.get_run",
return_value=Run(
RunInfo("uuid",
"expr_id",
"userid",
"status",
0,
10,
"active",
artifact_uri=artifact_uri),
None,
),
), mock.patch(
"mlflow.tracking._tracking_service.client.get_artifact_repository",
return_value=None) as get_repo_mock:
client = TrackingServiceClient(databricks_uri)
client._get_artifact_repo("some-run-id")
get_repo_mock.assert_called_once_with(uri_for_repo)
def test_artifact_repo_is_cached_per_run_id():
uri = "ftp://*****:*****@host/path"
with mock.patch(
"mlflow.tracking._tracking_service.client.TrackingServiceClient.get_run",
return_value=Run(
RunInfo("uuid",
"expr_id",
"userid",
"status",
0,
10,
"active",
artifact_uri=uri),
None,
),
):
artifact_repo = TrackingServiceClient(
"some_tracking_uri")._get_artifact_repo("some_run_id")
another_artifact_repo = TrackingServiceClient(
"some_tracking_uri")._get_artifact_repo("some_run_id")
assert artifact_repo is another_artifact_repo
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_registry_uri``. If
no such service was set, defaults to the tracking uri of the client.
"""
final_tracking_uri = utils._resolve_tracking_uri(tracking_uri)
self._registry_uri = registry_utils._resolve_registry_uri(
registry_uri, 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:
try:
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),
FEATURE_DISABLED,
)
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:`mlflow.projects.run`, creates objects but does not run code.
Unlike :py:func:`mlflow.start_run`, does not change the "active run" used by
:py:func:`mlflow.log_param`.
: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,
max_results=SEARCH_MAX_RESULTS_DEFAULT,
order_by=None,
page_token=None,
):
""":return: List of :py:class:`mlflow.entities.RunInfo`"""
return self._tracking_client.list_run_infos(experiment_id,
run_view_type, max_results,
order_by, page_token)
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``.
"""
self._tracking_client.delete_experiment(experiment_id)
def restore_experiment(self, experiment_id):
"""
Restore a deleted experiment unless permanently deleted.
:param experiment_id: The experiment ID returned from ``create_experiment``.
"""
self._tracking_client.restore_experiment(experiment_id)
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: Integer 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.
"""
self._tracking_client.delete_run(run_id)
def restore_run(self, run_id):
"""
Restores a deleted run with the given ID.
"""
self._tracking_client.restore_run(run_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
@experimental
def create_registered_model(self, name, tags=None, description=None):
"""
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.
:param tags: A dictionary of key-value pairs that are converted into
:py:class:`mlflow.entities.model_registry.RegisteredModelTag` objects.
:param description: Description of the model.
:return: A single object of :py:class:`mlflow.entities.model_registry.RegisteredModel`
created by backend.
"""
return self._get_registry_client().create_registered_model(
name, tags, description)
@experimental
def rename_registered_model(self, name, new_name):
"""
Update registered model name.
:param name: Name of the registered model to update.
:param new_name: New proposed name for the registered model.
:return: A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
self._get_registry_client().rename_registered_model(name, new_name)
@experimental
def update_registered_model(self, name, description=None):
"""
Updates metadata for RegisteredModel entity. Input field ``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 description: (Optional) New description.
:return: A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
if description is None:
raise MlflowException(
"Attempting to update registered model with no new field values."
)
return self._get_registry_client().update_registered_model(
name=name, description=description)
@experimental
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.
"""
self._get_registry_client().delete_registered_model(name)
@experimental
def list_registered_models(
self,
max_results=SEARCH_REGISTERED_MODEL_MAX_RESULTS_DEFAULT,
page_token=None):
"""
List of all registered models
:param max_results: Maximum number of registered models desired.
:param page_token: Token specifying the next page of results. It should be obtained from
a ``list_registered_models`` call.
:return: A PagedList of :py:class:`mlflow.entities.model_registry.RegisteredModel` objects
that can satisfy the search expressions. The pagination token for the next page
can be obtained via the ``token`` attribute of the object.
"""
return self._get_registry_client().list_registered_models(
max_results, page_token)
@experimental
def search_registered_models(
self,
filter_string=None,
max_results=SEARCH_REGISTERED_MODEL_MAX_RESULTS_DEFAULT,
order_by=None,
page_token=None,
):
"""
Search for registered models in backend that satisfy the filter criteria.
:param filter_string: Filter query string, defaults to searching all registered
models. Currently, it supports only a single filter condition as the name
of the model, for example, ``name = 'model_name'`` or a search expression
to match a pattern in the registered model name.
For example, ``name LIKE 'Boston%'`` (case sensitive) or
``name ILIKE '%boston%'`` (case insensitive).
:param max_results: Maximum number of registered models desired.
:param order_by: List of column names with ASC|DESC annotation, to be used for ordering
matching search results.
:param page_token: Token specifying the next page of results. It should be obtained from
a ``search_registered_models`` call.
:return: A PagedList of :py:class:`mlflow.entities.model_registry.RegisteredModel` objects
that satisfy the search expressions. The pagination token for the next page can be
obtained via the ``token`` attribute of the object.
.. code-block:: python
:caption: Example
import mlflow
client = mlflow.tracking.MlflowClient()
# Get search results filtered by the registered model name
model_name="CordobaWeatherForecastModel"
filter_string = "name='{}'".format(model_name)
results = client.search_registered_models(filter_string=filter_string)
print("-" * 80)
for res in results:
for mv in res.latest_versions:
print("name={}; run_id={}; version={}".format(mv.name, mv.run_id, mv.version))
# Get search results filtered by the registered model name that matches
# prefix pattern
filter_string = "name LIKE 'Boston%'"
results = client.search_registered_models(filter_string=filter_string)
for res in results:
for mv in res.latest_versions:
print("name={}; run_id={}; version={}".format(mv.name, mv.run_id, mv.version))
# Get all registered models and order them by ascending order of the names
results = client.search_registered_models(order_by=["name ASC"])
print("-" * 80)
for res in results:
for mv in res.latest_versions:
print("name={}; run_id={}; version={}".format(mv.name, mv.run_id, mv.version))
.. code-block:: text
:caption: Output
------------------------------------------------------------------------------------
name=CordobaWeatherForecastModel; run_id=eaef868ee3d14d10b4299c4c81ba8814; version=1
name=CordobaWeatherForecastModel; run_id=e14afa2f47a040728060c1699968fd43; version=2
------------------------------------------------------------------------------------
name=BostonWeatherForecastModel; run_id=ddc51b9407a54b2bb795c8d680e63ff6; version=1
name=BostonWeatherForecastModel; run_id=48ac94350fba40639a993e1b3d4c185d; version=2
-----------------------------------------------------------------------------------
name=AzureWeatherForecastModel; run_id=5fcec6c4f1c947fc9295fef3fa21e52d; version=1
name=AzureWeatherForecastModel; run_id=8198cb997692417abcdeb62e99052260; version=3
name=BostonWeatherForecastModel; run_id=ddc51b9407a54b2bb795c8d680e63ff6; version=1
name=BostonWeatherForecastModel; run_id=48ac94350fba40639a993e1b3d4c185d; version=2
name=CordobaWeatherForecastModel; run_id=eaef868ee3d14d10b4299c4c81ba8814; version=1
name=CordobaWeatherForecastModel; run_id=e14afa2f47a040728060c1699968fd43; version=2
"""
return self._get_registry_client().search_registered_models(
filter_string, max_results, order_by, page_token)
@experimental
def get_registered_model(self, name):
"""
:param name: Name of the registered model to update.
:return: A single :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
return self._get_registry_client().get_registered_model(name)
@experimental
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
for ALL_STAGES.
:return: List of :py:class:`mlflow.entities.model_registry.ModelVersion` objects.
"""
return self._get_registry_client().get_latest_versions(name, stages)
@experimental
def set_registered_model_tag(self, name, key, value):
"""
Set a tag for the registered model.
:param name: Registered model name.
:param key: Tag key to log.
:param value: Tag value log.
:return: None
"""
self._get_registry_client().set_registered_model_tag(name, key, value)
@experimental
def delete_registered_model_tag(self, name, key):
"""
Delete a tag associated with the registered model.
:param name: Registered model name.
:param key: Registered model tag key.
:return: None
"""
self._get_registry_client().delete_registered_model_tag(name, key)
# Model Version Methods
@experimental
def create_model_version(
self,
name,
source,
run_id,
tags=None,
run_link=None,
description=None,
await_creation_for=DEFAULT_AWAIT_MAX_SLEEP_SECONDS,
):
"""
Create a new model version from given source (artifact URI).
:param name: Name for the 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
:param tags: A dictionary of key-value pairs that are converted into
:py:class:`mlflow.entities.model_registry.ModelVersionTag` objects.
:param run_link: Link to the run from an MLflow tracking server that generated this model.
:param description: Description of the version.
:param await_creation_for: Number of seconds to wait for the model version to finish being
created and is in ``READY`` status. By default, the function
waits for five minutes. Specify 0 or None to skip waiting.
:return: Single :py:class:`mlflow.entities.model_registry.ModelVersion` object created by
backend.
"""
tracking_uri = self._tracking_client.tracking_uri
if not run_link and is_databricks_uri(
tracking_uri) and tracking_uri != self._registry_uri:
run_link = self._get_run_link(tracking_uri, run_id)
new_source = source
if is_databricks_uri(
self._registry_uri) and tracking_uri != self._registry_uri:
# Print out some info for user since the copy may take a while for large models.
eprint(
"=== Copying model files from the source location to the model"
+ " registry workspace ===")
new_source = _upload_artifacts_to_databricks(
source, run_id, tracking_uri, self._registry_uri)
# NOTE: we can't easily delete the target temp location due to the async nature
# of the model version creation - printing to let the user know.
eprint(
"=== Source model files were copied to %s" % new_source +
" in the model registry workspace. You may want to delete the files once the"
+
" model version is in 'READY' status. You can also find this location in the"
+ " `source` field of the created model version. ===")
return self._get_registry_client().create_model_version(
name=name,
source=new_source,
run_id=run_id,
tags=tags,
run_link=run_link,
description=description,
await_creation_for=await_creation_for,
)
def _get_run_link(self, tracking_uri, run_id):
# if using the default Databricks tracking URI and in a notebook, we can automatically
# figure out the run-link.
if is_databricks_default_tracking_uri(tracking_uri) and (
is_in_databricks_notebook() or is_in_databricks_job()):
# use DBUtils to determine workspace information.
workspace_host, workspace_id = get_workspace_info_from_dbutils()
else:
# in this scenario, we're not able to automatically extract the workspace ID
# to proceed, and users will need to pass in a databricks profile with the scheme:
# databricks://scope:prefix and store the host and workspace-ID as a secret in the
# Databricks Secret Manager with scope=<scope> and key=<prefix>-workspaceid.
workspace_host, workspace_id = get_workspace_info_from_databricks_secrets(
tracking_uri)
if not workspace_id:
print(
"No workspace ID specified; if your Databricks workspaces share the same"
" host URL, you may want to specify the workspace ID (along with the host"
" information in the secret manager) for run lineage tracking. For more"
" details on how to specify this information in the secret manager,"
" please refer to the model registry documentation.")
# retrieve experiment ID of the run for the URL
experiment_id = self.get_run(run_id).info.experiment_id
if workspace_host and run_id and experiment_id:
return construct_run_url(workspace_host, experiment_id, run_id,
workspace_id)
@experimental
def update_model_version(self, name, version, 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 description: New description.
:return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
"""
if description is None:
raise MlflowException(
"Attempting to update model version with no new field values.")
return self._get_registry_client().update_model_version(
name=name, version=version, description=description)
@experimental
def transition_model_version_stage(self,
name,
version,
stage,
archive_existing_versions=False):
"""
Update model version stage.
:param name: Registered model name.
:param version: Registered model version.
:param stage: New desired stage for this model version.
:param archive_existing_versions: If this flag is set to ``True``, all existing model
versions in the stage will be automically moved to the "archived" stage. Only valid
when ``stage`` is ``"staging"`` or ``"production"`` otherwise an error will be raised.
:return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
"""
return self._get_registry_client().transition_model_version_stage(
name, version, stage, archive_existing_versions)
@experimental
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)
@experimental
def get_model_version(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.ModelVersion` object.
"""
return self._get_registry_client().get_model_version(name, version)
@experimental
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)
@experimental
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, it supports a single filter
condition either a name of model like ``name = 'model_name'`` or
``run_id = '...'``.
:return: PagedList of :py:class:`mlflow.entities.model_registry.ModelVersion` objects.
.. code-block:: python
:caption: Example
import mlflow
client = mlflow.tracking.MlflowClient()
# Get all versions of the model filtered by name
model_name = "CordobaWeatherForecastModel"
filter_string = "name='{}'".format(model_name)
results = client.search_model_versions(filter_string)
print("-" * 80)
for res in results:
print("name={}; run_id={}; version={}".format(res.name, res.run_id, res.version))
# Get the version of the model filtered by run_id
run_id = "e14afa2f47a040728060c1699968fd43"
filter_string = "run_id='{}'".format(run_id)
results = client.search_model_versions(filter_string)
print("-" * 80)
for res in results:
print("name={}; run_id={}; version={}".format(res.name, res.run_id, res.version))
.. code-block:: text
:caption: Output
------------------------------------------------------------------------------------
name=CordobaWeatherForecastModel; run_id=eaef868ee3d14d10b4299c4c81ba8814; version=1
name=CordobaWeatherForecastModel; run_id=e14afa2f47a040728060c1699968fd43; version=2
------------------------------------------------------------------------------------
name=CordobaWeatherForecastModel; run_id=e14afa2f47a040728060c1699968fd43; version=2
"""
return self._get_registry_client().search_model_versions(filter_string)
@experimental
def get_model_version_stages(self, name, version): # pylint: disable=unused-argument
"""
:return: A list of valid stages.
"""
return ALL_STAGES
@experimental
def set_model_version_tag(self, name, version, key, value):
"""
Set a tag for the model version.
:param name: Registered model name.
:param version: Registered model version.
:param key: Tag key to log.
:param value: Tag value to log.
:return: None
"""
self._get_registry_client().set_model_version_tag(
name, version, key, value)
@experimental
def delete_model_version_tag(self, name, version, key):
"""
Delete a tag associated with the model version.
:param name: Registered model name.
:param version: Registered model version.
:param key: Tag key.
:return: None
"""
self._get_registry_client().delete_model_version_tag(
name, version, key)
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:
try:
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),
FEATURE_DISABLED)
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:`mlflow.projects.run`, creates objects but does not run code.
Unlike :py:func:`mlflow.start_run`, does not change the "active run" used by
:py:func:`mlflow.log_param`.
: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``.
"""
self._tracking_client.delete_experiment(experiment_id)
def restore_experiment(self, experiment_id):
"""
Restore a deleted experiment unless permanently deleted.
:param experiment_id: The experiment ID returned from ``create_experiment``.
"""
self._tracking_client.restore_experiment(experiment_id)
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.
"""
self._tracking_client.delete_run(run_id)
def restore_run(self, run_id):
"""
Restores a deleted run with the given ID.
"""
self._tracking_client.restore_run(run_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
@experimental
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)
@experimental
def rename_registered_model(self, name, new_name):
"""
Update registered model name.
:param name: Name of the registered model to update.
:param new_name: New proposed name for the registered model.
:return: A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
self._get_registry_client().rename_registered_model(name, new_name)
@experimental
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: (Deprecated) New proposed name for the registered model.
This argument is deprecated, use rename_registered_model instead..
:param description: (Optional) New description.
:return: A single updated :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
if new_name is None and description is None:
raise MlflowException("Attempting to update registered model with no new field values.")
if new_name is not None and new_name.strip() == "":
raise MlflowException("The new name must not be an empty string.")
res = None
if new_name is not None:
_logger.warning("'new_name' argument in update_registered_model is deprecated, "
"please use renamed_registered_model instead.")
res = self._get_registry_client().rename_registered_model(name=name, new_name=new_name)
name = new_name
if description is not None:
res = self._get_registry_client().update_registered_model(name=name,
description=description)
return res
@experimental
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.
"""
self._get_registry_client().delete_registered_model(name)
@experimental
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()
@deprecated(alternative="mlflow.tracking.client.get_registered_model", since="1.7")
def get_registered_model_details(self, name):
return self.get_registered_model(name)
@experimental
def get_registered_model(self, name):
"""
:param name: Name of the registered model to update.
:return: A single :py:class:`mlflow.entities.model_registry.RegisteredModel` object.
"""
return self._get_registry_client().get_registered_model(name)
@experimental
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
for ALL_STAGES.
:return: List of :py:class:`mlflow.entities.model_registry.ModelVersion` objects.
"""
return self._get_registry_client().get_latest_versions(name, stages)
# Model Version Methods
@experimental
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
backend.
"""
return self._get_registry_client().create_model_version(name, source, run_id)
@experimental
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: (Deprecated) New desired stage forthis model version. This field is deprecated
as of mlflow 1.7. Use transition_model_version_stage instead to update stage.
:param description: New description.
:return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
"""
if stage is None and description is None:
raise MlflowException("Attempting to update model version with no new field values.")
if stage is not None and stage.strip() == "":
raise MlflowException("The stage must not be an empty string.")
res = None
if stage is not None:
_logger.warning("'stage' field in update_model_version is deprecated. "
"Use transition_model_stage instead.")
res = self._get_registry_client().transition_model_version_stage(name=name,
version=version,
stage=stage)
if description is not None:
res = self._get_registry_client().update_model_version(name=name, version=version,
description=description)
return res
@experimental
def transition_model_version_stage(self, name, version, stage):
"""
Update model version stage.
:param name: Registered model name.
:param version: Registered model version.
:param new_stage: New desired stage for this model version.
:param archive_existing_versions: If this flag is set, all existing model
versions in the stage will be atomically moved to the "archived" stage.
:return: A single :py:class:`mlflow.entities.model_registry.ModelVersion` object.
"""
return self._get_registry_client().transition_model_version_stage(name, version, stage)
@experimental
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)
@deprecated("mlflow.tracking.client.get_model_version", "1.7")
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.ModelVersion` object.
"""
return self._get_registry_client().get_model_version(name, version)
@experimental
def get_model_version(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.ModelVersion` object.
"""
return self._get_registry_client().get_model_version(name, version)
@experimental
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)
@experimental
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)
@experimental
def get_model_version_stages(self, name, version): # pylint: disable=unused-argument
"""
:return: A list of valid stages.
"""
return ALL_STAGES
def newTrackingServiceClient():
return TrackingServiceClient("databricks://scope:key")
