# Copyright 2019 Google LLC # # 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. import os import tarfile import utils import yamale import yaml from datetime import datetime from kfp import Client from constants import CONFIG_DIR, DEFAULT_CONFIG, SCHEMA_CONFIG class PySampleChecker(object): def __init__(self, testname, input, output, result, namespace='kubeflow'): """Util class for checking python sample test running results. :param testname: test name. :param input: The path of a pipeline file that will be submitted. :param output: The path of the test output. :param result: The path of the test result that will be exported. :param namespace: namespace of the deployed pipeline system. Default: kubeflow """ self._testname = testname self._input = input self._output = output self._result = result self._namespace = namespace self._run_pipeline = None self._test_timeout = None self._test_cases = [] self._test_name = self._testname + ' Sample Test' self._client = None self._experiment_id = None self._job_name = None self._test_args = None self._run_id = None def run(self): """Run compiled KFP pipeline.""" ###### Initialization ###### host = 'ml-pipeline.%s.svc.cluster.local:8888' % self._namespace self._client = Client(host=host) ###### Check Input File ###### utils.add_junit_test(self._test_cases, 'input generated yaml file', os.path.exists(self._input), 'yaml file is not generated') if not os.path.exists(self._input): utils.write_junit_xml(self._test_name, self._result, self._test_cases) print('Error: job not found.') exit(1) ###### Create Experiment ###### experiment_name = self._testname + ' sample experiment' response = self._client.create_experiment(experiment_name) self._experiment_id = response.id utils.add_junit_test(self._test_cases, 'create experiment', True) ###### Create Job ###### self._job_name = self._testname + '_sample' ###### Figure out arguments from associated config files. ####### self._test_args = {} config_schema = yamale.make_schema(SCHEMA_CONFIG) try: with open(DEFAULT_CONFIG, 'r') as f: raw_args = yaml.safe_load(f) default_config = yamale.make_data(DEFAULT_CONFIG) yamale.validate(config_schema, default_config) # If fails, a ValueError will be raised. except yaml.YAMLError as yamlerr: raise RuntimeError('Illegal default config:{}'.format(yamlerr)) except OSError as ose: raise FileExistsError('Default config not found:{}'.format(ose)) else: self._test_timeout = raw_args['test_timeout'] self._run_pipeline = raw_args['run_pipeline'] try: config_file = os.path.join(CONFIG_DIR, '%s.config.yaml' % self._testname) with open(config_file, 'r') as f: raw_args = yaml.safe_load(f) test_config = yamale.make_data(config_file) yamale.validate(config_schema, test_config) # If fails, a ValueError will be raised. except yaml.YAMLError as yamlerr: print('No legit yaml config file found, use default args:{}'.format(yamlerr)) except OSError as ose: print('Config file with the same name not found, use default args:{}'.format(ose)) else: if 'arguments' in raw_args.keys() and raw_args['arguments']: self._test_args.update(raw_args['arguments']) if 'output' in self._test_args.keys(): # output is a special param that has to be specified dynamically. self._test_args['output'] = self._output if 'test_timeout' in raw_args.keys(): self._test_timeout = raw_args['test_timeout'] if 'run_pipeline' in raw_args.keys(): self._run_pipeline = raw_args['run_pipeline'] # Submit for pipeline running. if self._run_pipeline: response = self._client.run_pipeline(self._experiment_id, self._job_name, self._input, self._test_args) self._run_id = response.id utils.add_junit_test(self._test_cases, 'create pipeline run', True) def check(self): """Check pipeline run results.""" if self._run_pipeline: ###### Monitor Job ###### try: start_time = datetime.now() response = self._client.wait_for_run_completion(self._run_id, self._test_timeout) succ = (response.run.status.lower() == 'succeeded') end_time = datetime.now() elapsed_time = (end_time - start_time).seconds utils.add_junit_test(self._test_cases, 'job completion', succ, 'waiting for job completion failure', elapsed_time) finally: ###### Output Argo Log for Debugging ###### workflow_json = self._client._get_workflow_json(self._run_id) workflow_id = workflow_json['metadata']['name'] argo_log, _ = utils.run_bash_command('argo logs -n {} -w {}'.format( self._namespace, workflow_id)) print('=========Argo Workflow Log=========') print(argo_log) if not succ: utils.write_junit_xml(self._test_name, self._result, self._test_cases) exit(1) ###### Validate the results for specific test cases ###### #TODO: Add result check for tfx-cab-classification after launch. if self._testname == 'xgboost_training_cm': # For xgboost sample, check its confusion matrix. cm_tar_path = './confusion_matrix.tar.gz' utils.get_artifact_in_minio(workflow_json, 'confusion-matrix', cm_tar_path, 'mlpipeline-ui-metadata') with tarfile.open(cm_tar_path) as tar_handle: file_handles = tar_handle.getmembers() assert len(file_handles) == 1 with tar_handle.extractfile(file_handles[0]) as f: cm_data = f.read() utils.add_junit_test(self._test_cases, 'confusion matrix format', (len(cm_data) > 0), 'the confusion matrix file is empty') ###### Delete Job ###### #TODO: add deletion when the backend API offers the interface. ###### Write out the test result in junit xml ###### utils.write_junit_xml(self._test_name, self._result, self._test_cases)