Source code for ibm_watson_machine_learning.deployments

# (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
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function
import requests
import json
from ibm_watson_machine_learning.utils import DEPLOYMENT_DETAILS_TYPE, INSTANCE_DETAILS_TYPE, print_text_header_h1, print_text_header_h2, STR_TYPE, STR_TYPE_NAME, docstring_parameter, str_type_conv, \
    StatusLogger, meta_props_str_conv, convert_metadata_to_parameters
from ibm_watson_machine_learning.wml_client_error import WMLClientError, MissingValue, NoVirtualDeploymentSupportedForICP
from ibm_watson_machine_learning.href_definitions import is_uid
from ibm_watson_machine_learning.wml_resource import WMLResource
from ibm_watson_machine_learning.metanames import DeploymentMetaNames, ScoringMetaNames, DecisionOptimizationMetaNames, DeploymentNewMetaNames
from ibm_watson_machine_learning.libs.repo.util.library_imports import LibraryChecker
import numpy as np
#from ibm_watson_machine_learning.metanames import DeploymentMetaNames, ScoreMetaNames, EnvironmentMetaNames

lib_checker = LibraryChecker()

[docs]class Deployments(WMLResource): """ Deploy and score published artifacts (models and functions). """ cloud_platform_spaces = False icp_platform_spaces = False def __init__(self, client): WMLResource.__init__(self, __name__, client) if not client.ICP and not client.CLOUD_PLATFORM_SPACES and not client.ICP_PLATFORM_SPACES: Deployments._validate_type(client.service_instance.details, u'instance_details', dict, True) Deployments._validate_type_of_details(client.service_instance.details, INSTANCE_DETAILS_TYPE) self.session = requests.Session() self._ICP = client.ICP if client.CLOUD_PLATFORM_SPACES or client.ICP_PLATFORM_SPACES: self.ConfigurationMetaNames = DeploymentNewMetaNames() else: self.ConfigurationMetaNames = DeploymentMetaNames() if client.CLOUD_PLATFORM_SPACES: Deployments.cloud_platform_spaces = True if client.ICP_PLATFORM_SPACES: Deployments.icp_platform_spaces = True self.ScoringMetaNames = ScoringMetaNames() self.DecisionOptimizationMetaNames = DecisionOptimizationMetaNames() def _deployment_status_errors_handling(self, deployment_details, operation_name, deployment_id): try: if 'failure' in deployment_details['entity']['status']: errors = deployment_details[u'entity'][u'status'][u'failure'][u'errors'] for error in errors: if type(error) == str: try: error_obj = json.loads(error) print(error_obj[u'message']) except: print(error) elif type(error) == dict: print(error['message']) else: print(error) raise WMLClientError('Deployment ' + operation_name + ' failed for deployment id: ' + deployment_id +'. Errors: ' + str(errors)) else: print(deployment_details['entity']['status']) raise WMLClientError('Deployment ' + operation_name + ' failed for deployment id: ' + deployment_id +'. Error: ' + str(deployment_details['entity']['status']['state'])) except WMLClientError as e: raise e except Exception as e: self._logger.debug('Deployment ' + operation_name + ' failed: ' + str(e)) print(deployment_details['entity']['status']['failure']) raise WMLClientError('Deployment ' + operation_name + ' failed for deployment id: ' + deployment_id + '.')
[docs] @docstring_parameter({'str_type': STR_TYPE_NAME}) # TODO model_uid and artifact_uid should be changed to artifact_uid only def create(self, artifact_uid=None, meta_props=None, rev_id=None, **kwargs): """ Create a deployment from an artifact. As artifact we understand model or function which may be deployed. **Parameters** .. important:: #. **artifact_uid**: Published artifact UID (model or function uid)\n **type**: str\n #. **meta_props**: metaprops. To see the available list of metanames use:\n >>> client.deployments.ConfigurationMetaNames.get() **type**: dict\n **Output** .. important:: **returns**: metadata of the created deployment\n **return type**: dict\n **Example** >>> meta_props = { >>> wml_client.deployments.ConfigurationMetaNames.NAME: "SAMPLE DEPLOYMENT NAME", >>> wml_client.deployments.ConfigurationMetaNames.ONLINE: {}, >>> wml_client.deployments.ConfigurationMetaNames.HARDWARE_SPEC : { "id": "e7ed1d6c-2e89-42d7-aed5-8sb972c1d2b"} >>> } >>> deployment_details = client.deployments.create(artifact_uid, meta_props) """ #artifact_uid = str_type_conv(artifact_uid) if artifact_uid is not None else (str_type_conv(kwargs['model_uid'] if 'model_uid' in kwargs else None)) ##To be removed once deployments adds support for projects WMLResource._chk_and_block_create_update_for_python36(self) self._client._check_if_space_is_set() Deployments._validate_type(artifact_uid, u'artifact_uid', STR_TYPE, True) if self._ICP: predictionUrl = self._wml_credentials[u'url'] if meta_props is None: raise WMLClientError("Invalid input. meta_props can not be empty.") if self._client.CLOUD_PLATFORM_SPACES and 'r_shiny' in meta_props: raise WMLClientError('Shiny is not supported in this release') metaProps = self.ConfigurationMetaNames._generate_resource_metadata(meta_props) if (self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES) and 'wml_instance_id' in meta_props: metaProps.update({'wml_instance_id': meta_props['wml_instance_id']}) if not self._client.ICP_30 and not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES: ## Remove new meataprop from older one, just in case if user provides. if self.ConfigurationMetaNames.HARDWARE_SPEC in metaProps: metaProps.pop(self.ConfigurationMetaNames.HARDWARE_SPEC) if self.ConfigurationMetaNames.HYBRID_PIPELINE_HARDWARE_SPECS in metaProps: metaProps.pop(self.ConfigurationMetaNames.HYBRID_PIPELINE_HARDWARE_SPECS) if self.ConfigurationMetaNames.R_SHINY in metaProps: metaProps.pop(self.ConfigurationMetaNames.R_SHINY) artifact_details = self._client.repository.get_details(artifact_uid) artifact_href = artifact_details['metadata']['href'] artifact_revision_id = artifact_href.split('=')[1] if 'model' in artifact_href: if self._client.CAMS: details = "/v4/models/"+artifact_uid + "?space_id=" + self._client.default_space_id else: details = "/v4/models/" + artifact_uid + "?rev=" + artifact_revision_id elif 'function' in artifact_href: if self._client.CAMS: details = "/v4/functions/" + artifact_uid + "?space_id=" + self._client.default_space_id else: details = "/v4/functions/" + artifact_uid + "?rev=" + artifact_revision_id else: raise WMLClientError('Unexpected artifact type: \'{}\'.'.format(artifact_uid)) if 'model' in artifact_href: if "tensorflow_1.11" in artifact_details["entity"]["type"] or "tensorflow_1.5" in \ artifact_details["entity"]["type"]: print("Note: Model of framework tensorflow and versions 1.5/1.11 has been deprecated. These versions will not be supported after 26th Nov 2019.") metaProps['asset'] = {'href': details} if self._client.CAMS: if self._client.default_space_id is not None: metaProps['space'] = {'href': "/v4/spaces/" + self._client.default_space_id} else: raise WMLClientError( "It is mandatory is set the space. Use client.set.default_space(<SPACE_GUID>) to set the space.") ##Check if default space is set else: metaProps['asset'] = metaProps.get('asset') if metaProps.get('asset') else {'id': artifact_uid} if rev_id is not None: metaProps['asset'].update({'rev': rev_id}) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: metaProps['space_id'] = self._client.default_space_id else: if 'space' not in metaProps: if self._client.default_space_id is not None: metaProps['space_id'] = self._client.default_space_id else: raise WMLClientError( "It is mandatory is set the space. Use client.set.default_space(<SPACE_GUID>) to set the space.") url = self._client.service_instance._href_definitions.get_deployments_href() if not self._ICP: if self._client.CLOUD_PLATFORM_SPACES: response = requests.post( url, json=metaProps, params=self._client._params(), # version is mandatory headers=self._client._get_headers()) else: response = requests.post( url, json=metaProps, headers=self._client._get_headers()) else: if self._client.ICP_PLATFORM_SPACES: response = requests.post( url, json=metaProps, params=self._client._params(), # version is mandatory headers=self._client._get_headers(), verify=False) else: response = requests.post( url, json=metaProps, headers=self._client._get_headers(), verify=False) ## Post Deployment call executed if response.status_code == 202: deployment_details = response.json() #print(deployment_details) if self._ICP: if 'online_url' in deployment_details["entity"]["status"]: scoringUrl = deployment_details.get(u'entity').get(u'status').get('online_url').get('url').replace('https://ibm-nginx-svc:443', predictionUrl) deployment_details[u'entity'][u'status']['online_url']['url'] = scoringUrl deployment_uid = self.get_uid(deployment_details) import time print_text_header_h1(u'Synchronous deployment creation for uid: \'{}\' started'.format(artifact_uid)) status = deployment_details[u'entity'][u'status']['state'] notifications = [] with StatusLogger(status) as status_logger: while True: time.sleep(5) deployment_details = self._client.deployments.get_details(deployment_uid) #this is wrong , needs to update for ICP if "system" in deployment_details: notification = deployment_details['system']['warnings'][0]['message'] if notification not in notifications: print("\nNote: " + notification) notifications.append(notification) if self._ICP and not self._client.ICP_PLATFORM_SPACES: scoringUrl = deployment_details.get(u'entity').get(u'asset').get('href').replace('https://wml-os-envoy:16600', predictionUrl) deployment_details[u'entity'][u'asset']['href'] = scoringUrl status = deployment_details[u'entity'][u'status'][u'state'] status_logger.log_state(status) if status != u'DEPLOY_IN_PROGRESS' and status != "initializing": break if status == u'DEPLOY_SUCCESS' or status == u'ready': print(u'') print_text_header_h2( u'Successfully finished deployment creation, deployment_uid=\'{}\''.format(deployment_uid)) return deployment_details else: print_text_header_h2(u'Deployment creation failed') self._deployment_status_errors_handling(deployment_details, 'creation', deployment_uid) else: error_msg = u'Deployment creation failed' reason = response.text print(reason) print_text_header_h2(error_msg) raise WMLClientError(error_msg + '. Error: ' + str(response.status_code) + '. ' + reason)
#return self._handle_response(202, u'created deployment', response)
[docs] @staticmethod @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_uid(deployment_details): """ Get deployment_uid from deployment details. Deprecated!! Use get_id(deployment_details) instead **Parameters** .. important:: #. **deployment_details**: Metadata of the deployment\n **type**: dict\n **Output** .. important:: **returns**: deployment UID that is used to manage the deployment\n **return type**: str **Example** >>> deployment_uid = client.deployments.get_uid(deployment) """ Deployments._validate_type(deployment_details, u'deployment_details', dict, True) if not Deployments.cloud_platform_spaces and not Deployments.icp_platform_spaces: Deployments._validate_type_of_details(deployment_details, DEPLOYMENT_DETAILS_TYPE) try: if 'id' in deployment_details[u'metadata']: uid = deployment_details.get(u'metadata').get(u'id') else: uid = deployment_details.get(u'metadata').get(u'guid') except Exception as e: raise WMLClientError(u'Getting deployment UID from deployment details failed.', e) if uid is None: raise MissingValue(u'deployment_details.metadata.guid') return uid
[docs] @staticmethod @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_id(deployment_details): """ Get deployment id from deployment details. **Parameters** .. important:: #. **deployment_details**: Metadata of the deployment\n **type**: dict\n **Output** .. important:: **returns**: deployment ID that is used to manage the deployment\n **return type**: str **Example** >>> deployment_id = client.deployments.get_id(deployment) """ return Deployments.get_uid(deployment_details)
[docs] @staticmethod @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_href(deployment_details): """ Get deployment_href from deployment details. **Parameters** .. important:: #. **deployment_details**: Metadata of the deployment.\n **type**: dict\n **Output** .. important:: **returns**: Deployment href that is used to manage the deployment.\n **return type**: str **Example** >>> deployment_href = client.deployments.get_href(deployment) """ Deployments._validate_type(deployment_details, u'deployment_details', dict, True) if not Deployments.cloud_platform_spaces and not Deployments.icp_platform_spaces: Deployments._validate_type_of_details(deployment_details, DEPLOYMENT_DETAILS_TYPE) try: if 'href' in deployment_details[u'metadata']: url = deployment_details.get(u'metadata').get(u'href') else: url = "/ml/v4/deployments/{}".format(deployment_details[u'metadata'][u'id']) except Exception as e: raise WMLClientError(u'Getting deployment url from deployment details failed.', e) if url is None: raise MissingValue(u'deployment_details.metadata.href') return url
[docs] @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_details(self, deployment_uid=None, limit=None): """ Get information about your deployment(s). If deployment_uid is not passed, all deployment details are fetched. **Parameters** .. important:: #. **deployment_uid**: Unique Id of Deployment (optional)\n **type**: str\n #. **limit**: limit number of fetched records (optional)\n **type**: int\n **Output** .. important:: **returns**: metadata of deployment(s)\n **return type**: dict\n dict (if deployment_uid is not None) or {"resources": [dict]} (if deployment_uid is None)\n .. note:: If deployment_uid is not specified, all deployments metadata is fetched\n **Example** >>> deployment_details = client.deployments.get_details(deployment_uid) >>> deployment_details = client.deployments.get_details(deployment_uid=deployment_uid) >>> deployments_details = client.deployments.get_details() """ ##To be removed once deployments adds support for deployments self._client._check_if_space_is_set() deployment_uid = str_type_conv(deployment_uid) Deployments._validate_type(deployment_uid, u'deployment_uid', STR_TYPE, False) if deployment_uid is not None and not is_uid(deployment_uid): raise WMLClientError(u'\'deployment_uid\' is not an uid: \'{}\''.format(deployment_uid)) url = self._client.service_instance._href_definitions.get_deployments_href() ## TODO: Adding a work around till v4/deployment accepts space_id query parameter for GET if self._ICP and not self._client.ICP_PLATFORM_SPACES: if deployment_uid is not None: url = url + '/' + deployment_uid if self._client.ICP_35 or self._client.ICP_40: response = requests.get( url, params = self._client._params(), headers=self._client._get_headers(), verify=False ) else: response = requests.get( url, headers=self._client._get_headers(), verify=False ) return self._handle_response(200, "deployment", response) else: deployment_details = self._get_artifact_details(url, deployment_uid, limit, 'deployments') if "system" in deployment_details: print("Note: " + deployment_details['system']['warnings'][0]['message']) return deployment_details else: details = self._get_artifact_details(url, deployment_uid, limit, 'deployments') return details
[docs] @staticmethod @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_scoring_href(deployment_details): """ Get scoring url from deployment details. **Parameters** .. important:: #. **deployment_details**: Metadata of the deployment\n **type**: dict\n **Output** .. important:: **returns**: scoring endpoint url that is used for making scoring requests\n **return type**: str\n **Example** >>> scoring_href = client.deployments.get_scoring_href(deployment) """ Deployments._validate_type(deployment_details, u'deployment', dict, True) if not Deployments.cloud_platform_spaces and not Deployments.icp_platform_spaces: Deployments._validate_type_of_details(deployment_details, DEPLOYMENT_DETAILS_TYPE) scoring_url = None try: url = deployment_details['entity']['status'].get('online_url') if url is not None: scoring_url = deployment_details['entity']['status']['online_url']['url'] else: raise MissingValue(u'Getting scoring url for deployment failed. This functionality is available only for sync deployments') except Exception as e: raise WMLClientError(u'Getting scoring url for deployment failed. This functionality is available only for sync deployments', e) if scoring_url is None: raise MissingValue(u'scoring_url missing in online_predictions') return scoring_url
[docs] @docstring_parameter({'str_type': STR_TYPE_NAME}) def delete(self, deployment_uid): """ Delete deployment. **Parameters** .. important:: #. **deployment uid**: Unique Id of Deployment\n **type**: str\n **Output** .. important:: **returns**: status ("SUCCESS" or "FAILED")\n **return type**: str\n **Example** >>> client.deployments.delete(deployment_uid) """ ##To be removed once deployments adds support for deployments self._client._check_if_space_is_set() deployment_uid = str_type_conv(deployment_uid) Deployments._validate_type(deployment_uid, u'deployment_uid', STR_TYPE, True) if deployment_uid is not None and not is_uid(deployment_uid): raise WMLClientError(u'\'deployment_uid\' is not an uid: \'{}\''.format(deployment_uid)) deployment_url = self._client.service_instance._href_definitions.get_deployment_href(deployment_uid) if not self._ICP: response_delete = requests.delete( deployment_url, params=self._client._params(), headers=self._client._get_headers()) else: if self._client.ICP_PLATFORM_SPACES: response_delete = requests.delete( deployment_url, params=self._client._params(), headers=self._client._get_headers(), verify=False) else: response_delete = requests.delete( deployment_url, #TODO: The below line needs to uncommeted after /v4/deployment accespts query param # params=self._client._params(), headers=self._client._get_headers(), verify=False) return self._handle_response(204, u'deployment deletion', response_delete, False)
[docs] @docstring_parameter({'str_type': STR_TYPE_NAME}) def score(self,deployment_id, meta_props, transaction_id=None): """ Make scoring requests against deployed artifact. **Parameters** .. important:: #. **deployment_id**: Unique Id of the deployment to be scored\n **type**: str\n #. **meta_props**: Meta props for scoring\n >>> Use client.deployments.ScoringMetaNames.show() to view the list of ScoringMetaNames. **type**: dict\n #. **transaction_id**: transaction id to be passed with records during payload logging (optional)\n **type**: str\n **Output** .. important:: **returns**: scoring result containing prediction and probability\n **return type**: dict\n .. note:: * client.deployments.ScoringMetaNames.INPUT_DATA is the only metaname valid for sync scoring.\n * The valid payloads for scoring input are either list of values, pandas or numpy dataframes. **Example** >>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA: >>> [{'fields': >>> ['GENDER','AGE','MARITAL_STATUS','PROFESSION'], >>> 'values': [ >>> ['M',23,'Single','Student'], >>> ['M',55,'Single','Executive'] >>> ] >>> } >>> ]} >>> predictions = client.deployments.score(deployment_id, scoring_payload) >>> predictions = client.deployments.score(deployment_id, scoring_payload,async=True) """ ##To be removed once deployments adds support for deployments self._client._check_if_space_is_set() Deployments._validate_type(deployment_id, u'deployment_id', STR_TYPE, True) Deployments._validate_type(meta_props, u'meta_props', dict, True) if meta_props.get(self.ScoringMetaNames.INPUT_DATA) is None: raise WMLClientError("Scoring data input 'ScoringMetaNames.INPUT_DATA' is mandatory for synchronous " "scoring") scoring_data = meta_props[self.ScoringMetaNames.INPUT_DATA] if scoring_data is not None: score_payload = [] for each_score_request in scoring_data: lib_checker.check_lib(lib_name='pandas') import pandas as pd scoring_values = each_score_request["values"] # Check feature types, currently supporting pandas df, numpy.ndarray, python lists and Dmatrix if (isinstance(scoring_values, pd.DataFrame)): fields_names = scoring_values.columns.values.tolist() values = scoring_values.values.tolist() each_score_request["values"] = values if fields_names is not None: each_score_request["fields"] = fields_names ## If payload is a numpy dataframe elif (isinstance(scoring_values, np.ndarray)): values = scoring_values.tolist() each_score_request["values"] = values # else: # payload_score["values"] = each_score_request["values"] # if "fields" in each_score_request: # payload_score["fields"] = each_score_request["fields"] score_payload.append(each_score_request) ##See if it is scoring or DecisionOptimizationJob payload = {} payload["input_data"] = score_payload headers = self._client._get_headers() if transaction_id is not None: headers.update({'x-global-transaction-id': transaction_id}) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: scoring_url = self._wml_credentials["url"] + "/ml/v4/deployments/" + deployment_id + "/predictions" else: scoring_url = self._wml_credentials["url"] + "/v4/deployments/" + deployment_id + "/predictions" if not self._ICP: if self._client.CLOUD_PLATFORM_SPACES: params = self._client._params() del params[u'space_id'] response_scoring = self.session.post( scoring_url, json=payload, params=params, # version parameter is mandatory headers=headers) else: response_scoring = self.session.post( scoring_url, json=payload, headers=headers) else: if self._client.ICP_PLATFORM_SPACES: params = self._client._params() del params[u'space_id'] response_scoring = self.session.post( scoring_url, json=payload, params=params, # version parameter is mandatory headers=headers, verify=False) else: response_scoring = self.session.post( scoring_url, json=payload, headers=headers, verify=False) return self._handle_response(200, u'scoring', response_scoring)
#########################################
[docs] @staticmethod @docstring_parameter({'str_type': STR_TYPE_NAME}) def get_download_url(deployment_details): """ Get deployment_download_url from deployment details. **Parameters** .. important:: #. **deployment_details**: Created deployment details.\n **type**: dict\n **Output** .. important:: **returns**: Deployment download URL that is used to get file deployment (for example: Core ML).\n **return type**: str\n **Example** >>> deployment_url = client.deployments.get_download_url(deployment) """ Deployments._validate_type(deployment_details, u'deployment_details', dict, True) try: virtual_deployment_detaails = deployment_details.get(u'entity').get(u'status').get(u'virtual_deployment_downloads') if virtual_deployment_detaails is not None: url = virtual_deployment_detaails[0].get(u'url') else: url = None except Exception as e: raise WMLClientError(u'Getting download url from deployment details failed.', e) if url is None: raise MissingValue(u'deployment_details.entity.virtual_deployment_downloads.url') return url
[docs] @docstring_parameter({'str_type': STR_TYPE_NAME}) def download(self, virtual_deployment_uid, filename=None): """ Downloads file deployment of specified deployment Id. Currently supported format is Core ML. **Parameters** .. important:: #. **virtual_deployment_uid**: Unique Id of virtual deployment.\n **type**: str\n #. **filename**: filename of downloaded archive (optional).\n **type**: str\n **Output** .. important:: **returns**: Path to downloaded file.\n **return type**: str\n **Example** >>> client.deployments.download(virtual_deployment_uid) """ self._client._check_if_space_is_set() virtual_deployment_uid = str_type_conv(virtual_deployment_uid) Deployments._validate_type(virtual_deployment_uid, u'deployment_uid', STR_TYPE, False) if virtual_deployment_uid is not None and not is_uid(virtual_deployment_uid): raise WMLClientError(u'\'deployment_uid\' is not an uid: \'{}\''.format(virtual_deployment_uid)) params = self._client._params() details = self.get_details(virtual_deployment_uid) download_url = self.get_download_url(details) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_35 or self._client.ICP_40: download_url = download_url + "&version=2020-08-01" params = None if not self._ICP: # if self._client.CLOUD_PLATFORM_SPACES: # download_url = download_url + "&version=2020-08-01" # response = requests.get( # download_url, # headers=self._client._get_headers() # ) # else: response = requests.get( download_url, params=params, headers=self._client._get_headers() ) else: response = requests.get( download_url, params=params, headers=self._client._get_headers(), verify=False ) if filename is None: filename = 'mlartifact.tar.gz' if response.status_code == 200: with open(filename, "wb") as new_file: new_file.write(response.content) new_file.close() print_text_header_h2( u'Successfully downloaded deployment file: ' + str(filename)) return filename else: raise WMLClientError(u'Unable to download deployment content: ' + response.text)
# @docstring_parameter({'str_type': STR_TYPE_NAME}) # def score(self, deployment_id, meta_props, transaction_id=None): # """ # Make scoring requests against deployed artifact. # # **Parameters** # # .. important:: # #. **deployment_id**: UID of the deployment to be scored\n # **type**: str\n # #. **meta_props**: Meta props for scoring\n # >>> Use client.deployments.ScoringMetaNames.show() to view the list of ScoringMetaNames. # **type**: dict\n # #. **transaction_id**: transaction id to be passed with records during payload logging (optional)\n # **type**: str\n # # **Output** # # .. important:: # **returns**: scoring result containing prediction and probability\n # **return type**: dict\n # # .. note:: # # * client.deployments.ScoringMetaNames.SCORING_INPUT_DATA is the only metaname valid for sync scoring.\n # # **Example** # # >>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA: [{'fields': ['GENDER','AGE','MARITAL_STATUS','PROFESSION'], 'values': [['M',23,'Single','Student'],['M',55,'Single','Executive']]}]} # >>> predictions = client.deployments.score(deployment_id, scoring_payload) # >>> predictions = client.deployments.score(deployment_id, scoring_payload,async=True) # """ # Deployments._validate_type(deployment_id, u'scoring_url', STR_TYPE, True) # Deployments._validate_type(meta_props, u'meta_props', dict, True) # # # score_payload = {} # if meta_props[self.ScoringMetaNames.INPUT_DATA] is None: # raise WMLClientError("Scoring data input is mandatory for synchronous scoring") # # score_payload["input_data"] = meta_props[self.ScoringMetaNames.INPUT_DATA] # payload = score_payload # Deployments._validate_type(deployment_id, u'scoring_url', STR_TYPE, True) # Deployments._validate_type(payload, u'payload', dict, True) # # headers = self._client._get_headers() # # if transaction_id is not None: # headers.update({'x-global-transaction-id': transaction_id}) # # making change - connection keep alive # scoring_url = self._wml_credentials["url"] + "/v4/deployments/"+deployment_id+"/predictions" # if not self._ICP: # response_scoring = self.session.post( # scoring_url, # json=payload, # headers=headers) # else: # response_scoring = self.session.post( # scoring_url, # json=payload, # headers=headers, # verify=False) # # return self._handle_response(200, u'scoring', response_scoring)
[docs] def list(self, limit=None): """ List deployments. If limit is set to None there will be only first 50 records shown. **Parameters** .. important:: #. **limit**: limit number of fetched records\n **type**: int\n **Output** .. important:: This method only prints the list of all deployments in a table format.\n **return type**: None\n **Example** >>> client.deployments.list() """ ##To be removed once deployments adds support for deployments self._client._check_if_space_is_set() details = self.get_details(limit=limit) resources = details[u'resources'] values = [] index = 0 for m in resources: # Deployment service currently doesn't support limit querying # As a workaround, its filtered in python client # Ideally this needs to be on the server side if limit is not None and index == limit: break if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES: if 'guid' in m[u'metadata']: values.append((m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'entity'][u'status']['state'], m[u'metadata'][u'created_at'], self._get_deployable_asset_type(m))) else: values.append((m[u'metadata'][u'id'], m[u'entity'][u'name'], m[u'entity'][u'status']['state'], m[u'metadata'][u'created_at'], self._get_deployable_asset_type(m))) else: if 'guid' in m[u'metadata']: values.append((m[u'metadata'][u'guid'], m[u'entity'][u'name'], m[u'entity'][u'status']['state'], m[u'metadata'][u'created_at'])) else: values.append((m[u'metadata'][u'id'], m[u'entity'][u'name'], m[u'entity'][u'status']['state'], m[u'metadata'][u'created_at'])) index = index + 1 if not self._client.CLOUD_PLATFORM_SPACES and not self._client.ICP_PLATFORM_SPACES: self._list(values, [u'GUID', u'NAME', u'STATE', u'CREATED', u'ARTIFACT_TYPE'], limit, 50) else: self._list(values, [u'GUID', u'NAME', u'STATE', u'CREATED'], limit, 50)
[docs] def list_jobs(self,limit=None): """ List the async deployment jobs. If limit is set to None there will be only first 50 records shown. **Parameters** .. important:: #. **limit**: limit number of fetched records\n **type**: int\n **Output** .. important:: This method only prints the list of all async jobs in a table format.\n **return type**: None\n .. note:: * This method list only async deployment jobs created for WML deployment. **Example** >>> client.deployments.list_jobs() """ details = self.get_job_details(limit=limit) resources = details[u'resources'] values = [] index = 0 for m in resources: # Deployment service currently doesn't support limit querying # As a workaround, its filtered in python client if limit is not None and index == limit: break if 'scoring' in m['entity']: state = m['entity']['scoring']['status']['state'] else: state = m['entity']['decision_optimization']['status']['state'] if self._client.ICP_30 or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: deploy_id = m['entity']['deployment']['id'] values.append((m[u'metadata'][u'id'], state, m[u'metadata'][u'created_at'], deploy_id)) else: deploy_id = m['entity']['deployment']['href'].split("/")[3] values.append((m[u'metadata'][u'guid'], state, m[u'metadata'][u'created_at'], deploy_id)) index = index + 1 self._list(values, [u'JOB-UID', u'STATE', u'CREATED',u'DEPLOYMENT-ID'], limit, 50)
def _get_deployable_asset_type(self, details): url = details[u'entity'][u'asset']['id'] if 'model' in url: return 'model' elif 'function' in url: return 'function' else: return 'unknown'
[docs] def update(self, deployment_uid, changes): """ Updates existing deployment metadata. If ASSET is patched, then 'id' field is mandatory and it starts a deployment with the provided asset id/rev. Deployment id remains same **Parameters** .. important:: #. **deployment_uid**: Unique Id of deployment which should be updated\n **type**: str\n #. **changes**: elements which should be changed, where keys are ConfigurationMetaNames\n **type**: dict\n **Output** .. important:: **returns**: metadata of updated deployment\n **return type**: dict\n **Example** >>> metadata = { >>> client.deployments.ConfigurationMetaNames.NAME:"updated_Deployment", >>> client.deployments.ConfigurationMetaNames.ASSET: { "id": "ca0cd864-4582-4732-b365-3165598dc945", "rev":"2" } >>> } >>> >>> deployment_details = client.deployments.update(deployment_uid, changes=metadata) """ WMLResource._chk_and_block_create_update_for_python36(self) ##To be removed once deployments adds support for deployments self._client._check_if_space_is_set() ret202 = False ## In case of passing 'AUTO_ONLINE_DEPLOYMENT' as true, we need to poll for deployment to be either 'deploy_success' or 'update_success'. deployment_uid = str_type_conv(deployment_uid) Deployments._validate_type(deployment_uid, 'deployment_uid', STR_TYPE, True) if 'asset' in changes and not changes[u'asset']: msg = "ASSETS cannot be empty. 'id' and 'rev' fields are supported. 'id' is mandatory" print(msg) raise WMLClientError(msg) # if changes.get('asset') is not None and (changes.get('name') is not None or changes.get('description') is not None): if changes.get('asset') is not None and (len(changes) > 1): msg = "When ASSET is being updated/patched, other fields cannot be updated. If other fields are to be " \ "updated, try without ASSET update. ASSET update triggers deployment with the new asset retaining " \ "the same deployment_id" print(msg) raise WMLClientError(msg) deployment_details = self.get_details(deployment_uid) patch_payload = self.ConfigurationMetaNames._generate_patch_payload(deployment_details['entity'], changes,with_validation=True) ## As auto_online_deployment and auto_redeploy values are passed as 'bool' but service needs them in 'str' format to patch. for ele in patch_payload: if('auto_online_deployment' in ele['path'] or 'auto_redeploy' in ele['path']): if(ele['value']==True): ele['value']='true' else: ele['value']='false' url = self._client.service_instance._href_definitions.get_deployment_href(deployment_uid) if not self._ICP: response = requests.patch(url, json=patch_payload, params=self._client._params(),headers=self._client._get_headers()) else: response = requests.patch(url, json=patch_payload, params=self._client._params(),headers=self._client._get_headers(), verify=False) if 'asset' in changes and response.status_code == 202: updated_details = self._handle_response(202, u'deployment asset patch', response) ret202 = True print("Since ASSET is patched, deployment with new asset id/rev is being started. " \ "Monitor the status using deployments.get_details(deployment_uid) api") elif 'hardware_spec' in changes: updated_details = self._handle_response(202, u'deployment scaling', response) ret202 = True else: updated_details = self._handle_response(200, u'deployment patch', response) if('auto_online_deployment' in changes): if response is not None: if response.status_code == 200: deployment_details = self.get_details(deployment_uid) import time print_text_header_h1(u' deployment update for uid: \'{}\' started'.format(deployment_uid)) status = deployment_details[u'entity'][u'status'][u'state'] with StatusLogger(status) as status_logger: while True: time.sleep(5) deployment_details = self.get_details(deployment_uid) status = deployment_details[u'entity'][u'status'][u'state'] status_logger.log_state(status) if status != u'DEPLOY_IN_PROGRESS' and status != u'UPDATE_IN_PROGRESS': break if status == u'DEPLOY_SUCCESS' or status == u'UPDATE_SUCCESS': print(u'') print_text_header_h2( u'Successfully finished deployment update, deployment_uid=\'{}\''.format(deployment_uid)) return deployment_details else: print_text_header_h2(u'Deployment update failed') self._deployment_status_errors_handling(deployment_details, 'update', deployment_uid) else: error_msg = u'Deployment update failed' reason = response.text print(reason) print_text_header_h2(error_msg) raise WMLClientError(error_msg + u'. Error: ' + str(response.status_code) + '. ' + reason) if not ret202: return updated_details
## Below functions are for async scoring. They are just dummy functions. def _score_async(self,deployment_uid, scoring_payload, transaction_id=None, retention=None): Deployments._validate_type(deployment_uid, u'deployment_id', STR_TYPE, True) Deployments._validate_type(scoring_payload, u'scoring_payload', dict, True) headers = self._client._get_headers() if transaction_id is not None: headers.update({'x-global-transaction-id': transaction_id}) # making change - connection keep alive scoring_url = self._client.service_instance._href_definitions.get_async_deployment_job_href() if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: params = self._client._params() else: params = None if not self._client.ICP and retention is not None: if not isinstance(retention, int) or retention < -1: raise TypeError("`retention` takes integer values greater or equal than -1.") params.update({'retention': retention}) if not self._ICP: response_scoring = self.session.post( scoring_url, params=params, json=scoring_payload, headers=headers) else: response_scoring = self.session.post( scoring_url, params = params, json=scoring_payload, headers=headers, verify=False) return self._handle_response(202, u'scoring asynchronously', response_scoring)
[docs] def create_job(self, deployment_id, meta_props, retention=None, transaction_id=None): """ Create an asynchronous deployment job. **Parameters** .. important:: #. **deployment_id**: Unique Id of Deployment\n **type**: str\n #. **meta_props**: metaprops. To see the available list of metanames use:\n >>> client.deployments.ScoringMetaNames.get() or client.deployments.DecisionOptimizationmetaNames.get() **type**: dict\n #. **retention**: How many job days job meta should be retained (optional). Takes integer values >= -1. Supported only on Cloud.\n **type**:: int\n **Output** .. important:: **returns**: metadata of the created async deployment job\n **return type**: dict\n .. note:: * The valid payloads for scoring input are either list of values, pandas or numpy dataframes. **Example** >>> scoring_payload = {wml_client.deployments.ScoringMetaNames.INPUT_DATA: [{'fields': ['GENDER','AGE','MARITAL_STATUS','PROFESSION'], 'values': [['M',23,'Single','Student'],['M',55,'Single','Executive']]}]} >>> async_job = client.deployments.create_job(deployment_id, scoring_payload) """ WMLResource._chk_and_block_create_update_for_python36(self) Deployments._validate_type(deployment_id, u'deployment_uid', STR_TYPE, True) Deployments._validate_type(meta_props, u'meta_props', dict, True) scoring_data = None flag = 0 ## To see if it is async scoring or DecisionOptimization Job deployment_details = self.get_details(deployment_id) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: asset = deployment_details["entity"]["asset"]['id'] else: asset = deployment_details["entity"]["asset"]["href"].split("/")[-1] do_model = False if self._client.ICP_30 or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: asset_details = self._client.data_assets.get_details(asset) if 'wml_model' in asset_details["entity"] and 'type' in asset_details['entity']['wml_model']: if 'do' in asset_details['entity']['wml_model']['type']: do_model = True else: asset_details = self._client.repository.get_details(asset) if "type" in asset_details["entity"]: if "do" in asset_details["entity"]["type"]: do_model = True if do_model: payload = self.DecisionOptimizationMetaNames._generate_resource_metadata(meta_props, with_validation=True, client=self._client) flag = 1 else: payload = self.ScoringMetaNames._generate_resource_metadata(meta_props, with_validation=True, client=self._client) if "scoring" in payload and "input_data" in payload["scoring"]: scoring_data = payload["scoring"]["input_data"] if "decision_optimization" in payload and "input_data" in payload["decision_optimization"]: scoring_data = payload["decision_optimization"]["input_data"] if scoring_data is not None: score_payload = [] for each_score_request in scoring_data: lib_checker.check_lib(lib_name='pandas') import pandas as pd if "values" in each_score_request: scoring_values = each_score_request["values"] # Check feature types, currently supporting pandas df, numpy.ndarray, python lists and Dmatrix if (isinstance(scoring_values, pd.DataFrame)): fields_names = scoring_values.columns.values.tolist() values = scoring_values.where(pd.notnull(scoring_values), None).values.tolist() #replace nan with None each_score_request["values"] = values if fields_names is not None: each_score_request["fields"] = fields_names ## If payload is a numpy dataframe elif (isinstance(scoring_values, np.ndarray)): values = np.where(pd.notnull(scoring_values), scoring_values, None).tolist() #replace nan with None each_score_request["values"] = values # else: # payload_score["values"] = each_score_request["values"] # if "fields" in each_score_request: # payload_score["fields"] = each_score_request["fields"] score_payload.append(each_score_request) ##See if it is scoring or DecisionOptimizationJob if flag == 0: payload["scoring"]["input_data"] = score_payload if flag == 1: payload["decision_optimization"]["input_data"] = score_payload if self._client.ICP_30 or self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: import copy if 'input_data_references' in meta_props: Deployments._validate_type(meta_props.get('input_data_references'), u'input_data_references', list, True) modified_input_data_references=False input_data = copy.deepcopy(meta_props.get('input_data_references')) for i, input_data_fields in enumerate(input_data): if 'connection' not in input_data_fields: modified_input_data_references=True input_data_fields.update({'connection': {}}) if modified_input_data_references: if 'scoring' in payload: payload['scoring'].update({'input_data_references': input_data}) else: payload['decision_optimization'].update({'input_data_references': input_data}) if 'output_data_reference' in meta_props: Deployments._validate_type(meta_props.get('output_data_reference'), u'output_data_reference', dict, True) output_data = copy.deepcopy(meta_props.get('output_data_reference')) if 'connection' not in output_data: # and output_data.get('connection', None) is not None: output_data.update({'connection': {}}) payload['scoring'].update({'output_data_reference': output_data}) if 'output_data_references' in meta_props: Deployments._validate_type(meta_props.get('output_data_references'), u'output_data_references', list, True) output_data = copy.deepcopy(meta_props.get('output_data_references')) modified_output_data_references = False for i, output_data_fields in enumerate(output_data): if 'connection' not in output_data_fields: modified_output_data_references = True output_data_fields.update({'connection': {}}) if modified_output_data_references and 'decision_optimization' in payload: payload['decision_optimization'].update({'output_data_references': output_data}) payload.update({"deployment": {"id": deployment_id}}) if 'hardware_spec' in meta_props: payload.update({"hardware_spec": meta_props[self.ConfigurationMetaNames.HARDWARE_SPEC]}) if 'hybrid_pipeline_hardware_specs' in meta_props: payload.update({"hybrid_pipeline_hardware_specs": meta_props[self.ConfigurationMetaNames.HYBRID_PIPELINE_HARDWARE_SPECS]}) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: payload.update({'space_id': self._client.default_space_id}) if 'name' not in payload: import uuid payload.update({'name': 'name_' + str(uuid.uuid4())}) else: payload.update({"deployment": {"href": "/v4/deployments/" + deployment_id}}) return self._score_async(deployment_id, payload, transaction_id=transaction_id, retention=retention)
[docs] def get_job_details(self, job_uid=None, limit=None): """ Get information about your deployment job(s). If deployment job_uid is not passed, all deployment jobs details are fetched. **Parameters** .. important:: #. **job_uid**: Unique Job ID (optional)\n **type**: str\n #. **limit**: limit number of fetched records (optional)\n **type**: int\n **Output** .. important:: **returns**: metadata of deployment job(s)\n **return type**: dict\n dict (if job_uid is not None) or {"resources": [dict]} (if job_uid is None)\n .. note:: If job_uid is not specified, all deployment jobs metadata associated with the deployment Id is fetched\n **Example** >>> deployment_details = client.deployments.get_job_details() >>> deployments_details = client.deployments.get_job_details(job_uid=job_uid) """ if job_uid is not None: Deployments._validate_type(job_uid, u'job_uid', STR_TYPE, True) url = self._client.service_instance._href_definitions.get_async_deployment_job_href() if self._ICP: if job_uid is not None: url = url + '/' + job_uid response = requests.get( url, headers=self._client._get_headers(), params=self._client._params(), verify=False ) return self._handle_response(200, "async deployment job", response) else: return self._get_artifact_details(url, job_uid, limit, 'async deployment jobs') else: return self._get_artifact_details(url, job_uid, limit, 'async deployment jobs')
[docs] def get_job_status(self, job_id): """ Get the status of the deployment job. **Parameters** .. important:: #. **job_id**: Unique Id of the deployment job\n **type**: str\n **Output** .. important:: **returns**: status of the deployment job\n **return type**: dict\n **Example** >>> job_status = client.deployments.get_job_status(job_uid) """ job_details = self.get_job_details(job_id) if 'scoring' not in job_details['entity']: return job_details['entity']['decision_optimization']['status'] return job_details['entity']['scoring']['status']
[docs] def get_job_uid(self, job_details): """ Get the Unique Id of the deployment job. **Parameters** .. important:: #. **job_details**: metadata of the deployment job\n **type**: dict\n **Output** .. important:: **returns**: Unique Id of the deployment job\n **return type**: str\n **Example** >>> job_details = client.deployments.get_job_details(job_uid=job_uid) >>> job_status = client.deployments.get_job_uid(job_details) """ if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: return job_details['metadata']['id'] else: return job_details['metadata']['guid']
[docs] def get_job_href(self, job_details): """ Get the href of the deployment job. **Parameters** .. important:: #. **job_details**: metadata of the deployment job\n **type**: dict\n **Output** .. important:: **returns**: href of the deployment job\n **return type**: str\n **Example** >>> job_details = client.deployments.get_job_details(job_uid=job_uid) >>> job_status = client.deployments.get_job_href(job_details) """ if self._client.ICP_30 and not self._client.ICP_PLATFORM_SPACES: job_href = "/v4/deployment_jobs/" + job_details['metadata']['id'] return job_href else: if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: return "/ml/v4/deployment_jobs/{}".format(job_details[u'metadata'][u'id']) else: return job_details['metadata']['href']
[docs] def delete_job(self, job_uid, hard_delete=False): """ Cancels a deployment job that is currenlty running. This method is also be used to delete metadata details of the completed or canceled jobs when hard_delete parameter is set to True. **Parameters** .. important:: #. **job_uid**: Unique Id of deployment job which should be canceled\n **type**: str\n #. **hard_delete**: specify True or False. True - To delete the completed or canceled job. False - To cancel the currently running deployment job. Default value is False. **type**: Boolean\n **Output** .. important:: **returns**: status ("SUCCESS" or "FAILED")\n **return type**: str\n **Example** >>> client.deployments.delete_job(job_uid) """ job_uid = str_type_conv(job_uid) Deployments._validate_type(job_uid, u'job_uid', STR_TYPE, True) if job_uid is not None and not is_uid(job_uid): raise WMLClientError(u'\'job_uid\' is not an uid: \'{}\''.format(job_uid)) url = self._client.service_instance._href_definitions.get_async_deployment_jobs_href(job_uid) if self._client.CLOUD_PLATFORM_SPACES or self._client.ICP_PLATFORM_SPACES: params = self._client._params() else: params = {} if hard_delete is True: params.update({'hard_delete': u'true'}) if not self._ICP: response_delete = requests.delete( url, headers=self._client._get_headers(), params=params) else: response_delete = requests.delete( url, headers=self._client._get_headers(), params=params, verify=False) return self._handle_response(204, u'deployment async job deletion', response_delete, False)
# def create_environment(self, meta_props): # """ # Cancels a job. # # **Parameters** # # .. important:: # # #. **job_uid**: UID of job which should be canceled\n # **type**: str\n # # **Output** # # .. important:: # # **returns**: status ("SUCCESS" or "FAILED")\n # **return type**: str\n # # **Example** # # >>> client.deployments.delete_job(job_uid) # """ # # Deployments._validate_type(meta_props, u'metaprops', dict, True) # # # url = self._client.service_instance._href_definitions.get_envs_href() # metadata = self.EnvironmentMetanames._generate_resource_metadata(meta_props) # # if not self._ICP: # response_post = requests.post(url, json=metadata, # headers=self._client._get_headers()) # # # else: # response_post = requests.post(url, json=metadata, # headers=self._client._get_headers(), verify=False) # # return self._handle_response(201, u'deployment job deletion', response_post, False) # # def delete_environment(self, env_id): # """ # Cancels a job. # # **Parameters** # # .. important:: # # #. **job_uid**: UID of job which should be canceled\n # **type**: str\n # # **Output** # # .. important:: # # **returns**: status ("SUCCESS" or "FAILED")\n # **return type**: str\n # # **Example** # # >>> client.deployments.delete_job(job_uid) # """ # # env_id = str_type_conv(env_id) # # Deployments._validate_type(env_id, u'env_uid', STR_TYPE, True) # # # if env_id is not None and not is_uid(env_id): # raise WMLClientError(u'\'env_id\' is not an uid: \'{}\''.format(env_id)) # # # url = self._client.service_instance._href_definitions.get_env_href(env_id) # # if not self._ICP: # response_delete = requests.delete( # url, # headers=self._client._get_headers()) # else: # response_delete = requests.delete( # url, # headers=self._client._get_headers(), # verify=False) # # return self._handle_response(204, u'environment deletion', response_delete, False) # # def get_environment_uid(self, env_details): # """ # Get the UID of the environment. # # **Parameters** # # .. important:: # #. **job_details**: metadata of the job\n # **type**: dict\n # # **Output** # # .. important:: # **returns**: UID of the job\n # **return type**: str\n # # **Example** # # >>> job_details = client.deployments.get_job_details(deployment_uid=deployment_uid,job_uid=job_uid) # >>> job_status = client.deployments.get_job_uid(job_details) # """ # # return env_details['metadata']['guid'] # # def get_environment_href(self, env_details): # """ # Get the href of the job. # # **Parameters** # # .. important:: # #. **job_details**: metadata of the job\n # **type**: dict\n # # **Output** # # .. important:: # **returns**: href of the job\n # **return type**: str\n # # **Example** # # >>> job_details = client.deployments.get_job_details(deployment_uid=deployment_uid,job_uid=job_uid) # >>> job_status = client.deployments.get_job_href(job_details) # """ # # return env_details['metadata']['href'] # # def get_environment_details(self,env_id=None,limit=None): # """ # Get information about your job(s). If job_uid is not passed, all jobs details are fetched. # # **Parameters** # # .. important:: # # #. **job_uid**: Job UID (optional)\n # **type**: str\n # #. **limit**: limit number of fetched records (optional)\n # **type**: int\n # # **Output** # # .. important:: # **returns**: metadata of job(s)\n # **return type**: dict\n # dict (if job UID is not None) or {"resources": [dict]} (if job UID is None)\n # # .. note:: # If job UID is not specified, all jobs metadata associated with the deployment UID is fetched\n # # # **Example** # # >>> deployment_details = client.deployments.get_job_details() # >>> deployments_details = client.deployments.get_job_details(job_uid=job_uid) # """ # #Deployments._validate_type(job_uid, u'deployment_uid', STR_TYPE, True) # #Deployments._validate_type(job_uid, u'job_uid', STR_TYPE, True) # url = self._client.service_instance._href_definitions.get_envs_href() # return self._get_artifact_details(url, env_id, limit, 'get environments details') # # def list_environments(self,limit=None): # """ # List the async jobs. If limit is set to None there will be only first 50 records shown. # # **Parameters** # # .. important:: # #. **limit**: limit number of fetched records\n # **type**: int\n # # **Output** # # .. important:: # This method only prints the list of all async jobs in a table format.\n # **return type**: None\n # # **Example** # # >>> client.deployments.list_jobs() # """ # details = self.get_environment_details() # resources = details[u'resources'] # values = [(m[u'metadata'][u'guid'], m[u'entity'][u'name'], # m[u'metadata'][u'created_at']) for m in resources] # # self._list(values, [u'GUID', u'NAME', u'CREATED'], None, 50)