150 lines
5.0 KiB
Python
150 lines
5.0 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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"""Two step v2-compatible pipeline."""
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# %%
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from __future__ import annotations
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import unittest
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from pprint import pprint
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import kfp.deprecated as kfp
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import kfp_server_api
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from .two_step import two_step_pipeline
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from kfp.samples.test.utils import run_pipeline_func, TestCase, KfpMlmdClient, KfpTask
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from ml_metadata.proto import Execution
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def verify_tasks(t: unittest.TestCase, tasks: dict[str, KfpTask]):
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task_names = [*tasks.keys()]
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t.assertCountEqual(task_names, ['train-op', 'preprocess'], 'task names')
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preprocess = tasks['preprocess']
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train = tasks['train-op']
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pprint('======= preprocess task =======')
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pprint(preprocess.get_dict())
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pprint('======= train task =======')
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pprint(train.get_dict())
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pprint('==============')
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t.assertEqual(
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{
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'name': 'preprocess',
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'inputs': {
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'artifacts': [],
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'parameters': {
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'some_int': 1234,
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'uri': 'uri-to-import'
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}
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},
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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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'parameters': {
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'output_parameter_one': 1234
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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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}, preprocess.get_dict())
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t.assertEqual(
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{
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'name': 'train-op',
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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',
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'type': 'system.Dataset',
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}],
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'parameters': {
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'num_steps': 1234
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}
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},
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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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},
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'name': 'model',
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'type': 'system.Model',
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}],
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'parameters': {}
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},
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'type': 'system.ContainerExecution',
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'state': Execution.State.COMPLETE,
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}, train.get_dict())
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def verify_artifacts(t: unittest.TestCase, tasks: dict, artifact_uri_prefix):
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for task in tasks.values():
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for artifact in task.outputs.artifacts:
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t.assertTrue(artifact.uri.startswith(artifact_uri_prefix))
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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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verify_tasks(t, tasks)
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def verify_with_default_pipeline_root(run: kfp_server_api.ApiRun,
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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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verify_tasks(t, tasks)
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verify_artifacts(t, tasks, 'minio://mlpipeline/v2/artifacts')
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def verify_with_specific_pipeline_root(run: kfp_server_api.ApiRun,
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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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verify_tasks(t, tasks)
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verify_artifacts(t, tasks, 'minio://mlpipeline/override/artifacts')
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if __name__ == '__main__':
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run_pipeline_func([
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TestCase(
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pipeline_func=two_step_pipeline,
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mode=kfp.dsl.PipelineExecutionMode.V1_LEGACY),
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# Cannot test V2_ENGINE and V1_LEGACY using the same code.
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# V2_ENGINE requires importing everything from v2 namespace.
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# TestCase(
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# pipeline_func=two_step_pipeline,
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# verify_func=verify,
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# mode=kfp.dsl.PipelineExecutionMode.V2_ENGINE,
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# ),
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])
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# %%
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