Source code for mlflow.deployments.interface

import inspect

from mlflow.deployments.base import BaseDeploymentClient
from mlflow.deployments.plugin_manager import DeploymentPlugins
from mlflow.deployments.utils import parse_target_uri

plugin_store = DeploymentPlugins()
plugin_store.register("sagemaker", "mlflow.sagemaker")

[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 * 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()