# -----------------------------------------------------------------------------------------
# (C) Copyright IBM Corp. 2020- 2023.
# https://opensource.org/licenses/BSD-3-Clause
# -----------------------------------------------------------------------------------------
from typing import TYPE_CHECKING, Any, Dict, Union, List, Optional
import pandas as pd
from pandas import DataFrame
from .base_deployment import BaseDeployment
from ..wml_client_error import WMLClientError
if TYPE_CHECKING:
from sklearn.pipeline import Pipeline
from pandas import DataFrame
from numpy import ndarray
from ..workspace import WorkSpace
__all__ = [
"WebService"
]
[docs]class WebService(BaseDeployment):
"""An Online Deployment class aka. WebService.
With this class object you can manage any online (WebService) deployment.
:param source_wml_credentials: credentials to Watson Machine Learning instance where training was performed
:type source_wml_credentials: dict
:param source_project_id: ID of the Watson Studio project where training was performed
:type source_project_id: str, optional
:param source_space_id: ID of the Watson Studio Space where training was performed
:type source_space_id: str, optional
:param target_wml_credentials: credentials to Watson Machine Learning instance where you want to deploy
:type target_wml_credentials: dict
:param target_project_id: ID of the Watson Studio project where you want to deploy
:type target_project_id: str, optional
:param target_space_id: ID of the Watson Studio Space where you want to deploy
:type target_space_id: str, optional
"""
def __init__(self,
source_wml_credentials: Union[dict, 'WorkSpace'] = None,
source_project_id: str = None,
source_space_id: str = None,
target_wml_credentials: Union[dict, 'WorkSpace'] = None,
target_project_id: str = None,
target_space_id: str = None,
wml_credentials: Union[dict, 'WorkSpace'] = None,
project_id: str = None,
space_id: str = None):
super().__init__(
deployment_type='online',
source_wml_credentials=source_wml_credentials,
source_project_id=source_project_id,
source_space_id=source_space_id,
target_wml_credentials=target_wml_credentials,
target_project_id=target_project_id,
target_space_id=target_space_id,
wml_credentials=wml_credentials,
project_id=project_id,
space_id=space_id
)
self.name = None
self.scoring_url = None
self.id = None
self.asset_id = None
def __repr__(self):
return f"name: {self.name}, id: {self.id}, scoring_url: {self.scoring_url}, asset_id: {self.asset_id}"
def __str__(self):
return f"name: {self.name}, id: {self.id}, scoring_url: {self.scoring_url}, asset_id: {self.asset_id}"
[docs] def create(self,
model: str,
deployment_name: str,
serving_name: str = None,
metadata: Optional[Dict] = None,
training_data: Optional[Union['DataFrame', 'ndarray']] = None,
training_target: Optional[Union['DataFrame', 'ndarray']] = None,
experiment_run_id: Optional[str] = None,
hardware_spec: Optional[dict] = None) -> None:
"""Create deployment from a model.
:param model: AutoAI model name
:type model: str
:param deployment_name: name of the deployment
:type deployment_name: str
:param training_data: training data for the model
:type training_data: pandas.DataFrame or numpy.ndarray, optional
:param training_target: target/label data for the model
:type training_target: pandas.DataFrame or numpy.ndarray, optional
:param serving_name: serving name of the deployment
:type serving_name: str, optional
:param metadata: model meta properties
:type metadata: dict, optional
:param experiment_run_id: ID of a training/experiment (only applicable for AutoAI deployments)
:type experiment_run_id: str, optional
:param hardware_spec: hardware specification for deployment
:type hardware_spec: dict, optional
**Example**
.. code-block:: python
from ibm_watson_machine_learning.deployment import WebService
deployment = WebService(
wml_credentials={
"apikey": "...",
"iam_apikey_description": "...",
"iam_apikey_name": "...",
"iam_role_crn": "...",
"iam_serviceid_crn": "...",
"instance_id": "...",
"url": "https://us-south.ml.cloud.ibm.com"
},
project_id="...",
space_id="...")
deployment.create(
experiment_run_id="...",
model=model,
deployment_name='My new deployment',
serving_name='my_new_deployment'
)
"""
return super().create(model=model,
deployment_name=deployment_name,
metadata=metadata,
serving_name=serving_name,
training_data=training_data,
training_target=training_target,
experiment_run_id=experiment_run_id,
deployment_type='online',
hardware_spec=hardware_spec)
[docs] @BaseDeployment._project_to_space_to_project
def get_params(self) -> Dict:
"""Get deployment parameters."""
return super().get_params()
[docs] @BaseDeployment._project_to_space_to_project
def score(self, payload: Union[dict, 'DataFrame'] = pd.DataFrame(), transaction_id: str = None) -> Dict[str, List]:
"""Online scoring on WML. Payload is passed to the WML scoring endpoint where model have been deployed.
:param payload: DataFrame with data to test the model or dictionary with keys `observations`
and `supporting_features` and DataFrames with data for `observations` and `supporting_features`
to score forecasting models
:type payload: pandas.DataFrame or dict
:param transaction_id: can be used to indicate under which id the records will be saved into payload table
in IBM OpenScale
:type transaction_id: str, optional
:return: dictionary with list od model output/predicted targets
:rtype: dict
**Examples**
.. code-block:: python
predictions = web_service.score(payload=test_data)
print(predictions)
# Result:
# {'predictions':
# [{
# 'fields': ['prediction', 'probability'],
# 'values': [['no', [0.9221385608558003, 0.07786143914419975]],
# ['no', [0.9798324002736079, 0.020167599726392187]]
# }]}
predictions = web_service.score(payload={'observations': new_observations_df})
predictions = web_service.score(payload={'observations': new_observations_df, 'supporting_features': supporting_features_df}) # supporting features time series forecasting sceanrio
"""
return super().score(payload=payload, transaction_id=transaction_id)
[docs] @BaseDeployment._project_to_space_to_project
def delete(self, deployment_id: str = None) -> None:
"""Delete deployment on WML.
:param deployment_id: ID of the deployment to delete, if empty, current deployment will be deleted
:type deployment_id: str, optional
**Example**
.. code-block:: python
deployment = WebService(workspace=...)
# Delete current deployment
deployment.delete()
# Or delete a specific deployment
deployment.delete(deployment_id='...')
"""
super().delete(deployment_id=deployment_id, deployment_type='online')
[docs] @BaseDeployment._project_to_space_to_project
def list(self, limit=None) -> 'DataFrame':
"""List WML deployments.
:param limit: set the limit of how many deployments to list,
default is `None` (all deployments should be fetched)
:type limit: int, optional
:return: Pandas DataFrame with information about deployments
:rtype: pandas.DataFrame
**Example**
.. code-block:: python
deployment = WebService(workspace=...)
deployments_list = deployment.list()
print(deployments_list)
# Result:
# created_at ... status
# 0 2020-03-06T10:50:49.401Z ... ready
# 1 2020-03-06T13:16:09.789Z ... ready
# 4 2020-03-11T14:46:36.035Z ... failed
# 3 2020-03-11T14:49:55.052Z ... failed
# 2 2020-03-11T15:13:53.708Z ... ready
"""
return super().list(limit=limit, deployment_type='online')
[docs] @BaseDeployment._project_to_space_to_project
def get(self, deployment_id: str) -> None:
"""Get WML deployment.
:param deployment_id: ID of the deployment to work with
:type deployment_id: str
**Example**
.. code-block:: python
deployment = WebService(workspace=...)
deployment.get(deployment_id="...")
"""
super().get(deployment_id=deployment_id, deployment_type='online')
@BaseDeployment._project_to_space_to_project
def _deploy(self,
pipeline_model: 'Pipeline',
deployment_name: str,
meta_props: Dict,
serving_name=None,
result_client=None,
hardware_spec=None) -> Dict:
"""Deploy model into WML.
:param pipeline_model: model of the pipeline to deploy
:type pipeline_model: Pipeline or str
:param deployment_name: name of the deployment
:type deployment_name: str
:param meta_props: model meta properties
:type meta_props: dict
:param serving_name: serving name of the deployment
:type serving_name: str
:param result_client: tuple with Result DataConnection object and initialized COS client
:rtype: tuple[DataConnection, resource]
:return: deployment details
:rtype: dict
"""
asset_uid = self._publish_model(pipeline_model=pipeline_model,
meta_props=meta_props)
self.asset_id = asset_uid
conf_names = self._target_workspace.wml_client.deployments.ConfigurationMetaNames
deployment_props = {
conf_names.NAME: deployment_name,
conf_names.ONLINE: {}
}
if hardware_spec:
deployment_props[conf_names.HARDWARE_SPEC] = hardware_spec
if serving_name:
deployment_props[conf_names.ONLINE]["parameters"] = {conf_names.SERVING_NAME: serving_name}
print("Deploying model {} using V4 client.".format(asset_uid))
try:
deployment_details = self._target_workspace.wml_client.deployments.create(
artifact_uid=asset_uid,
meta_props=deployment_props)
self.deployment_id = self._target_workspace.wml_client.deployments.get_uid(deployment_details)
except WMLClientError as e:
raise e
return deployment_details
