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()
[docs]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
emails_df = pd.read_csv("...")
prediction_df = client.predict_deployment("spamDetector", emails_df)
# List all deployments, get details of our particular deployment
print(client.list_deployments())
print(client.get_deployment("spamDetector"))
# Update our deployment to serve a different model
client.update_deployment("spamDetector", "runs:/anotherRunId/myModel")
# Delete our deployment
client.delete_deployment("spamDetector")
"""
target = parse_target_uri(target_uri)
plugin = plugin_store[target]
for _, obj in inspect.getmembers(plugin):
if inspect.isclass(obj):
if issubclass(obj, BaseDeploymentClient) and not obj == BaseDeploymentClient:
return obj(target_uri)
[docs]def run_local(target, name, model_uri, flavor=None, config=None):
"""
Deploys the specified model locally, for testing. Note that models deployed locally cannot
be managed by other deployment APIs (e.g. ``update_deployment``, ``delete_deployment``, etc).
:param target: Target to deploy to.
:param name: Name to use for deployment
:param model_uri: URI of model to deploy
:param flavor: (optional) Model flavor to deploy. If unspecified, a default flavor
will be chosen.
:param config: (optional) Dict containing updated target-specific configuration for
the deployment
:return: None
"""
return plugin_store[target].run_local(name, model_uri, flavor, config)
def _target_help(target):
"""
Return a string containing detailed documentation on the current deployment target,
to be displayed when users invoke the ``mlflow deployments help -t <target-name>`` CLI.
This method should be defined within the module specified by the plugin author.
The string should contain:
* An explanation of target-specific fields in the ``config`` passed to ``create_deployment``,
``update_deployment``
* How to specify a ``target_uri`` (e.g. for AWS SageMaker, ``target_uri``s have a scheme of
"sagemaker:/<aws-cli-profile-name>", where aws-cli-profile-name is the name of an AWS
CLI profile https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-profiles.html)
* Any other target-specific details.
:param target: Which target to use. This information is used to call the appropriate plugin
"""
return plugin_store[target].target_help()