126 lines
4.1 KiB
Python
126 lines
4.1 KiB
Python
# Copyright 2021 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 unittest
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from pprint import pprint
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import kfp_server_api
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import kfp.deprecated.dsl as dsl
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from .lightweight_python_functions_v2_pipeline import pipeline
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from kfp.samples.test.utils import run_pipeline_func, TestCase, KfpMlmdClient
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from ml_metadata.proto import Execution
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def verify(run: kfp_server_api.ApiRun, mlmd_connection_config, **kwargs):
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t = unittest.TestCase()
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t.maxDiff = None # we always want to see full diff
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t.assertEqual(run.status, 'Succeeded')
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client = KfpMlmdClient(mlmd_connection_config=mlmd_connection_config)
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tasks = client.get_tasks(run_id=run.id)
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task_names = [*tasks.keys()]
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t.assertCountEqual(task_names, ['preprocess', 'train'], 'task names')
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pprint(tasks)
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preprocess = tasks['preprocess']
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train = tasks['train']
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pprint(preprocess.get_dict())
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t.assertEqual(
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{
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'inputs': {
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'parameters': {
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'message': 'message',
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}
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},
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'name': 'preprocess',
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'outputs': {
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'artifacts': [{
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'metadata': {
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'display_name': 'output_dataset_one'
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},
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'name': 'output_dataset_one',
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'type': 'system.Dataset'
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}, {
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'metadata': {
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'display_name': 'output_dataset_two_path'
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},
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'name': 'output_dataset_two_path',
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'type': 'system.Dataset'
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}],
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'parameters': {
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'output_bool_parameter_path': True,
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'output_dict_parameter_path': {
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"A": 1.0,
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"B": 2.0
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},
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'output_list_parameter_path': ["a", "b", "c"],
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'output_parameter_path': 'message'
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}
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},
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'type': 'system.ContainerExecution',
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'state': Execution.State.COMPLETE,
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},
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preprocess.get_dict(),
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)
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t.assertEqual(
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{
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'inputs': {
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'artifacts': [{
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'metadata': {
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'display_name': 'output_dataset_one'
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},
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'name': 'dataset_one_path',
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'type': 'system.Dataset'
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}, {
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'metadata': {
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'display_name': 'output_dataset_two_path'
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},
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'name': 'dataset_two',
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'type': 'system.Dataset'
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}],
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'parameters': {
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'input_bool': True,
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'input_dict': {
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"A": 1.0,
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"B": 2.0,
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},
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'input_list': ["a", "b", "c"],
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'message': 'message'
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}
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},
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'name': 'train',
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'outputs': {
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'artifacts': [{
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'metadata': {
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'display_name': 'model',
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'accuracy': 0.9,
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},
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'name': 'model',
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'type': 'system.Model'
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}],
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},
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'type': 'system.ContainerExecution',
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'state': Execution.State.COMPLETE,
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},
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train.get_dict(),
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)
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run_pipeline_func([
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TestCase(
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pipeline_func=pipeline,
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verify_func=verify,
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mode=dsl.PipelineExecutionMode.V2_ENGINE),
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])
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