def test_plugin_doesnot_have_required_attrib():
class DummyPlugin:
... # pylint: disable=pointless-statement
dummy_plugin = DummyPlugin()
plugin_manager = DeploymentPlugins()
plugin_manager.registry["dummy"] = dummy_plugin
with pytest.raises(MlflowException):
plugin_manager["dummy"] # pylint: disable=pointless-statement
import inspect
from mlflow.deployments.plugin_manager import DeploymentPlugins
from mlflow.deployments.base import BaseDeploymentClient
from mlflow.utils.annotations import experimental
from mlflow.deployments.utils import parse_target_uri
plugin_store = DeploymentPlugins()
def get_deploy_client(target_uri):
"""
Returns a subclass of :py:class:`mlflow.deployments.BaseDeploymentClient` exposing standard
APIs for deploying models to the specified target. See available deployment APIs
by calling ``help()`` on the returned object or viewing docs for
:py:class:`mlflow.deployments.BaseDeploymentClient`. You can also run
``mlflow deployments help -t <target-uri>`` via the CLI for more details on target-specific
configuration options.
:param target_uri: URI of target to deploy to.
.. code-block:: python
:caption: Example
from mlflow.deployments import get_deploy_client
import pandas as pd
client = get_deploy_client('redisai')
# Deploy the model stored at artifact path 'myModel' under run with ID 'someRunId'. The
# model artifacts are fetched from the current tracking server and then used for deployment.
client.create_deployment("spamDetector", "runs:/someRunId/myModel")
# Load a CSV of emails and score it against our deployment
import inspect
from mlflow.deployments.plugin_manager import DeploymentPlugins
from mlflow.deployments.base import BaseDeploymentClient
from mlflow.deployments.utils import parse_target_uri
plugin_store = DeploymentPlugins()
plugin_store.register("sagemaker", "mlflow.sagemaker")
def get_deploy_client(target_uri):
"""
Returns a subclass of :py:class:`mlflow.deployments.BaseDeploymentClient` exposing standard
APIs for deploying models to the specified target. See available deployment APIs
by calling ``help()`` on the returned object or viewing docs for
:py:class:`mlflow.deployments.BaseDeploymentClient`. You can also run
``mlflow deployments help -t <target-uri>`` via the CLI for more details on target-specific
configuration options.
:param target_uri: URI of target to deploy to.
.. code-block:: python
:caption: Example
from mlflow.deployments import get_deploy_client
import pandas as pd
client = get_deploy_client('redisai')
# Deploy the model stored at artifact path 'myModel' under run with ID 'someRunId'. The
# model artifacts are fetched from the current tracking server and then used for deployment.
client.create_deployment("spamDetector", "runs:/someRunId/myModel")
# Load a CSV of emails and score it against our deployment