126 lines
4.5 KiB
Python
126 lines
4.5 KiB
Python
# Copyright 2018 The Kubeflow Authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import json
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import tarfile
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from datetime import datetime
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import utils
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from kfp import Client
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###### Input/Output Instruction ######
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# input: yaml
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# output: local file path
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# Parsing the input arguments
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def parse_arguments():
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"""Parse command line arguments."""
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parser = argparse.ArgumentParser()
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parser.add_argument('--input',
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type=str,
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required=True,
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help='The path of a pipeline package that will be submitted.')
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parser.add_argument('--result',
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type=str,
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required=True,
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help='The path of the test result that will be exported.')
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parser.add_argument('--output',
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type=str,
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required=True,
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help='The path of the test output')
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parser.add_argument('--namespace',
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type=str,
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default='kubeflow',
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help="namespace of the deployed pipeline system. Default: kubeflow")
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args = parser.parse_args()
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return args
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def main():
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args = parse_arguments()
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test_cases = []
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test_name = 'Resnet CMLE Test'
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###### Initialization ######
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host = 'ml-pipeline.%s.svc.cluster.local:8888' % args.namespace
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client = Client(host=host)
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###### Check Input File ######
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utils.add_junit_test(test_cases, 'input generated yaml file', os.path.exists(args.input), 'yaml file is not generated')
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if not os.path.exists(args.input):
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utils.write_junit_xml(test_name, args.result, test_cases)
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print('Error: job not found.')
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exit(1)
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###### Create Experiment ######
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experiment_name = 'resnet cmle sample experiment'
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response = client.create_experiment(experiment_name)
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experiment_id = response.id
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utils.add_junit_test(test_cases, 'create experiment', True)
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###### Create Job ######
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job_name = 'cmle_sample'
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params = {'output': args.output,
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'project_id': 'ml-pipeline-test',
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'region': 'us-central1',
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'model': 'bolts',
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'version': 'beta1',
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'tf_version': '1.9', # Watch out! If 1.9 is no longer supported we need to set it to a newer version.
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'train_csv': 'gs://ml-pipeline-dataset/sample-test/bolts/bolt_images_train_sample1000.csv',
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'validation_csv': 'gs://ml-pipeline-dataset/sample-test/bolts/bolt_images_validate_sample200.csv',
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'labels': 'gs://bolts_image_dataset/labels.txt',
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'depth': 50,
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'train_batch_size': 32,
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'eval_batch_size': 32,
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'steps_per_eval': 128,
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'train_steps': 128,
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'num_train_images': 1000,
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'num_eval_images': 200,
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'num_label_classes': 10}
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response = client.run_pipeline(experiment_id, job_name, args.input, params)
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run_id = response.id
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utils.add_junit_test(test_cases, 'create pipeline run', True)
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###### Monitor Job ######
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try:
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start_time = datetime.now()
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response = client.wait_for_run_completion(run_id, 1800)
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succ = (response.run.status.lower()=='succeeded')
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end_time = datetime.now()
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elapsed_time = (end_time - start_time).seconds
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utils.add_junit_test(test_cases, 'job completion', succ, 'waiting for job completion failure', elapsed_time)
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finally:
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###### Output Argo Log for Debugging ######
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workflow_json = client._get_workflow_json(run_id)
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workflow_id = workflow_json['metadata']['name']
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print("Argo Workflow Name: ", workflow_id)
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argo_log, _ = utils.run_bash_command('argo logs {} -n {}'.format(workflow_id, args.namespace))
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print("=========Argo Workflow Log=========")
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print(argo_log)
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if not succ:
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utils.write_junit_xml(test_name, args.result, test_cases)
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exit(1)
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###### Delete Job ######
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#TODO: add deletion when the backend API offers the interface.
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###### Write out the test result in junit xml ######
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utils.write_junit_xml(test_name, args.result, test_cases)
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if __name__ == "__main__":
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main()
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