Source code for ibm_watson_machine_learning.deployment.web_service

# (C) Copyright IBM Corp. 2020.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from typing import TYPE_CHECKING, Any, Dict, Union, List, Optional

from pandas import DataFrame

from .base_deployment import BaseDeployment
from ..wml_client_error import WMLClientError

    from sklearn.pipeline import Pipeline
    from pandas import DataFrame
    from numpy import ndarray
    from ..workspace import WorkSpace

__all__ = [

[docs]class WebService(BaseDeployment): """ An Online Deployment class aka. WebService. With this class object you can manage any online (WebService) deployment. Parameters ---------- source_wml_credentials: dictionary, required Credentials to Watson Machine Learning instance where training was performed. source_project_id: str, optional ID of the Watson Studio project where training was performed. source_space_id: str, optional ID of the Watson Studio Space where training was performed. target_wml_credentials: dictionary, required Credentials to Watson Machine Learning instance where you want to deploy. target_project_id: str, optional ID of the Watson Studio project where you want to deploy. target_space_id: str, optional ID of the Watson Studio Space where you want to deploy. """ 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 ) = None self.scoring_url = None = None self.asset_id = None def __repr__(self): return f"name: {}, id: {}, scoring_url: {self.scoring_url}, asset_id: {self.asset_id}" def __str__(self): return f"name: {}, id: {}, scoring_url: {self.scoring_url}, asset_id: {self.asset_id}"
[docs] def create(self, model: str, deployment_name: str, metadata: Optional[Dict] = None, training_data: Optional[Union['DataFrame', 'ndarray']] = None, training_target: Optional[Union['DataFrame', 'ndarray']] = None, experiment_run_id: Optional[str] = None) -> None: """ Create deployment from a model. Parameters ---------- model: str, required AutoAI model name. deployment_name: str, required Name of the deployment training_data: Union['pandas.DataFrame', 'numpy.ndarray'], optional Training data for the model training_target: Union['pandas.DataFrame', 'numpy.ndarray'], optional Target/label data for the model metadata: dictionary, optional Model meta properties. experiment_run_id: str, optional ID of a training/experiment (only applicable for AutoAI deployments) Example ------- >>> 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": "" >>> }, >>> project_id="...", >>> space_id="...") >>> >>> deployment.create( >>> experiment_run_id="...", >>> model=model, >>> deployment_name='My new deployment' >>> ) """ return super().create(model=model, deployment_name=deployment_name, metadata=metadata, training_data=training_data, training_target=training_target, experiment_run_id=experiment_run_id, deployment_type='online')
[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: '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. Parameters ---------- payload: pandas.DataFrame, required DataFrame with data to test the model. transaction_id: str, optional Can be used to indicate under which id the records will be saved into payload table in IBM OpenScale. Returns ------- Dictionary with list od model output/predicted targets. Example ------- >>> predictions = deployment.score(payload=test_data) >>> print(predictions) {'predictions': [{ 'fields': ['prediction', 'probability'], 'values': [['no', [0.9221385608558003, 0.07786143914419975]], ['no', [0.9798324002736079, 0.020167599726392187]] }]} """ 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. Parameters ---------- deployment_id: str, optional ID of the deployment to delete. If empty, current deployment will be deleted. Example ------- >>> 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. Parameters ---------- limit: int, optional Set the limit of how many deployments to list. Default is None (all deployments should be fetched) Returns ------- Pandas DataFrame with information about deployments. Example ------- >>> deployment = WebService(workspace=...) >>> deployments_list = deployment.list() >>> print(deployments_list) 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. Parameters ---------- deployment_id: str, required ID of the deployment to work with. Returns ------- WebService deployment object Example ------- >>> 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, result_client=None) -> Dict: """ Deploy model into WML. Parameters ---------- pipeline_model: Union['Pipeline', str], required Model of the pipeline to deploy deployment_name: str, required Name of the deployment meta_props: dictionary, required Model meta properties. result_client: Tuple['DataConnection', 'resource'] required Tuple with Result DataConnection object and initialized COS client. """ deployment_details = {} asset_uid = self._publish_model(pipeline_model=pipeline_model, meta_props=meta_props) self.asset_id = asset_uid deployment_props = { self._target_workspace.wml_client.deployments.ConfigurationMetaNames.NAME: deployment_name, self._target_workspace.wml_client.deployments.ConfigurationMetaNames.ONLINE: {}, "hardware_spec": { "name": "M", } } 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