Source code for mlflow.tracking.client

"""
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.
"""
import logging

from mlflow.entities import ViewType
from mlflow.entities.model_registry.model_version_stages import ALL_STAGES
from mlflow.exceptions import MlflowException
from mlflow.protos.databricks_pb2 import FEATURE_DISABLED
from mlflow.store.model_registry import SEARCH_REGISTERED_MODEL_MAX_RESULTS_DEFAULT
from mlflow.store.tracking import SEARCH_MAX_RESULTS_DEFAULT
from mlflow.tracking._model_registry.client import ModelRegistryClient
from mlflow.tracking._model_registry import utils as registry_utils
from mlflow.tracking._tracking_service import utils
from mlflow.tracking._tracking_service.client import TrackingServiceClient
from mlflow.tracking.artifact_utils import _upload_artifacts_to_databricks
from mlflow.tracking.registry import UnsupportedModelRegistryStoreURIException
from mlflow.utils.annotations import experimental
from mlflow.utils.databricks_utils import (
    is_databricks_default_tracking_uri,
    is_in_databricks_job,
    is_in_databricks_notebook,
    get_workspace_info_from_dbutils,
    get_workspace_info_from_databricks_secrets,
)
from mlflow.utils.logging_utils import eprint
from mlflow.utils.uri import is_databricks_uri, construct_run_url

_logger = logging.getLogger(__name__)


[docs]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
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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 )
[docs] def list_experiments(self, view_type=None): """ :return: List of :py:class:`mlflow.entities.Experiment` """ return self._tracking_client.list_experiments(view_type)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] 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)
[docs] def delete_run(self, run_id): """ Deletes a run with the given ID. """ self._tracking_client.delete_run(run_id)
[docs] def restore_run(self, run_id): """ Restores a deleted run with the given ID. """ self._tracking_client.restore_run(run_id)
[docs] 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
[docs] @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)
[docs] @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)
[docs] @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 )
[docs] @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)
[docs] @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)
[docs] @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 )
[docs] @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)
[docs] @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)
[docs] @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)
[docs] @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
[docs] @experimental def create_model_version( self, name, source, run_id, tags=None, run_link=None, description=None ): """ 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. :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, )
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)
[docs] @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 )
[docs] @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 )
[docs] @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)
[docs] @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)
[docs] @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)
[docs] @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)
[docs] @experimental def get_model_version_stages(self, name, version): # pylint: disable=unused-argument """ :return: A list of valid stages. """ return ALL_STAGES
[docs] @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)
[docs] @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)