"""
This module contains the base interface implemented by MLflow model deployment plugins.
In particular, a valid deployment plugin module must implement:
1. Exactly one client class subclassed from :py:class:`BaseDeploymentClient`, exposing the primary
user-facing APIs used to manage deployments.
2. :py:func:`run_local`, for testing deployment by deploying a model locally
3. :py:func:`target_help`, which returns a help message describing target-specific URI format
and deployment config
"""
import abc
from mlflow.utils.annotations import experimental
from mlflow.exceptions import MlflowException
def run_local(target, name, model_uri, flavor=None, config=None): # pylint: disable=W0613
"""
.. Note::
This function is kept here only for documentation purpose and not implementing the
actual feature. It should be implemented in the plugin's top level namescope and should
be callable with ``plugin_module.run_local``
Deploys the specified model locally, for testing. This function should be defined
within the plugin module. Also note that this function has a signature which is very
similar to :py:meth:`BaseDeploymentClient.create_deployment` since both does logically
similar operation.
:param target: Which target to use. This information is used to call the appropriate plugin
:param name: Unique name to use for deployment. If another deployment exists with the same
name, create_deployment will raise a
:py:class:`mlflow.exceptions.MlflowException`
:param model_uri: URI of model to deploy
:param flavor: (optional) Model flavor to deploy. If unspecified, default flavor is chosen.
:param config: (optional) Dict containing updated target-specific config for the deployment
:return: None
"""
raise NotImplementedError(
"This function should be implemented in the deployment plugin. It is"
"kept here only for documentation purpose and shouldn't be used in"
"your application"
)
def target_help():
"""
.. Note::
This function is kept here only for documentation purpose and not implementing the
actual feature. It should be implemented in the plugin's top level namescope and should
be callable with ``plugin_module.target_help``
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`` 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.
"""
raise NotImplementedError(
"This function should be implemented in the deployment plugin. It is"
"kept here only for documentation purpose and shouldn't be used in"
"your application"
)
[docs]class BaseDeploymentClient(abc.ABC):
"""
Base class exposing Python model deployment APIs.
Plugin implementors should define target-specific deployment logic via a subclass of
``BaseDeploymentClient`` within the plugin module, and customize method docstrings with
target-specific information.
.. Note::
Subclasses should raise :py:class:`mlflow.exceptions.MlflowException` in error cases (e.g.
on failure to deploy a model).
"""
def __init__(self, target_uri):
self.target_uri = target_uri
[docs] @abc.abstractmethod
def create_deployment(self, name, model_uri, flavor=None, config=None):
"""
Deploy a model to the specified target. By default, this method should block until
deployment completes (i.e. until it's possible to perform inference with the deployment).
In the case of conflicts (e.g. if it's not possible to create the specified deployment
without due to conflict with an existing deployment), raises a
:py:class:`mlflow.exceptions.MlflowException`. See target-specific plugin documentation
for additional detail on support for asynchronous deployment and other configuration.
:param name: Unique name to use for deployment. If another deployment exists with the same
name, raises a
:py:class:`mlflow.exceptions.MlflowException`
: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: Dict corresponding to created deployment, which must contain the 'name' key.
"""
pass
[docs] @abc.abstractmethod
def update_deployment(self, name, model_uri=None, flavor=None, config=None):
"""
Update the deployment with the specified name. You can update the URI of the model, the
flavor of the deployed model (in which case the model URI must also be specified), and/or
any target-specific attributes of the deployment (via `config`). By default, this method
should block until deployment completes (i.e. until it's possible to perform inference
with the updated deployment). See target-specific plugin documentation for additional
detail on support for asynchronous deployment and other configuration.
:param name: Unique name of deployment to update
:param model_uri: URI of a new model to deploy.
:param flavor: (optional) new model flavor to use for deployment. If provided,
``model_uri`` must also be specified. If ``flavor`` is unspecified but
``model_uri`` is specified, a default flavor will be chosen and the
deployment will be updated using that flavor.
:param config: (optional) dict containing updated target-specific configuration for the
deployment
:return: None
"""
pass
[docs] @abc.abstractmethod
def delete_deployment(self, name):
"""
Delete the deployment with name ``name`` from the specified target. Deletion should be
idempotent (i.e. deletion should not fail if retried on a non-existent deployment).
:param name: Name of deployment to delete
:return: None
"""
pass
[docs] @abc.abstractmethod
def list_deployments(self):
"""
List deployments. This method is expected to return an unpaginated list of all
deployments (an alternative would be to return a dict with a 'deployments' field
containing the actual deployments, with plugins able to specify other fields, e.g.
a next_page_token field, in the returned dictionary for pagination, and to accept
a `pagination_args` argument to this method for passing pagination-related args).
:return: A list of dicts corresponding to deployments. Each dict is guaranteed to
contain a 'name' key containing the deployment name. The other fields of
the returned dictionary and their types may vary across deployment targets.
"""
pass
[docs] @abc.abstractmethod
def get_deployment(self, name):
"""
Returns a dictionary describing the specified deployment, throwing a
py:class:`mlflow.exception.MlflowException` if no deployment exists with the provided
ID.
The dict is guaranteed to contain an 'name' key containing the deployment name.
The other fields of the returned dictionary and their types may vary across
deployment targets.
:param name: ID of deployment to fetch
"""
pass
[docs] @abc.abstractmethod
def predict(self, deployment_name, df):
"""
Compute predictions on the pandas DataFrame ``df`` using the specified deployment.
Note that the input/output types of this method matches that of `mlflow pyfunc predict`
(we accept a pandas DataFrame as input and return either a pandas DataFrame,
pandas Series, or numpy array as output).
:param deployment_name: Name of deployment to predict against
:param df: Pandas DataFrame to use for inference
:return: A pandas DataFrame, pandas Series, or numpy array
"""
pass
[docs] @experimental
def explain(self, deployment_name, df): # pylint: disable=unused-argument
"""
Generate explanations of model predictions on the specified input pandas Dataframe
``df`` for the deployed model. Explanation output formats vary by deployment target,
and can include details like feature importance for understanding/debugging predictions.
:param deployment_name: Name of deployment to predict against
:param df: Pandas DataFrame to use for explaining feature importance in model prediction
:return: A JSON-able object (pandas dataframe, numpy array, dictionary), or
an exception if the implementation is not available in deployment target's class
"""
raise MlflowException(
"Computing model explanations is not yet supported for this deployment target"
)