pipelines/samples/test/two_step_test.py

150 lines
5.0 KiB
Python

# Copyright 2021 The Kubeflow Authors
#
# 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.
"""Two step v2-compatible pipeline."""
# %%
from __future__ import annotations
import unittest
from pprint import pprint
import kfp.deprecated as kfp
import kfp_server_api
from .two_step import two_step_pipeline
from kfp.samples.test.utils import run_pipeline_func, TestCase, KfpMlmdClient, KfpTask
from ml_metadata.proto import Execution
def verify_tasks(t: unittest.TestCase, tasks: dict[str, KfpTask]):
task_names = [*tasks.keys()]
t.assertCountEqual(task_names, ['train-op', 'preprocess'], 'task names')
preprocess = tasks['preprocess']
train = tasks['train-op']
pprint('======= preprocess task =======')
pprint(preprocess.get_dict())
pprint('======= train task =======')
pprint(train.get_dict())
pprint('==============')
t.assertEqual(
{
'name': 'preprocess',
'inputs': {
'artifacts': [],
'parameters': {
'some_int': 1234,
'uri': 'uri-to-import'
}
},
'outputs': {
'artifacts': [{
'metadata': {
'display_name': 'output_dataset_one',
},
'name': 'output_dataset_one',
'type': 'system.Dataset'
}],
'parameters': {
'output_parameter_one': 1234
}
},
'type': 'system.ContainerExecution',
'state': Execution.State.COMPLETE,
}, preprocess.get_dict())
t.assertEqual(
{
'name': 'train-op',
'inputs': {
'artifacts': [{
'metadata': {
'display_name': 'output_dataset_one',
},
'name': 'dataset',
'type': 'system.Dataset',
}],
'parameters': {
'num_steps': 1234
}
},
'outputs': {
'artifacts': [{
'metadata': {
'display_name': 'model',
},
'name': 'model',
'type': 'system.Model',
}],
'parameters': {}
},
'type': 'system.ContainerExecution',
'state': Execution.State.COMPLETE,
}, train.get_dict())
def verify_artifacts(t: unittest.TestCase, tasks: dict, artifact_uri_prefix):
for task in tasks.values():
for artifact in task.outputs.artifacts:
t.assertTrue(artifact.uri.startswith(artifact_uri_prefix))
def verify(run: kfp_server_api.ApiRun, mlmd_connection_config, **kwargs):
t = unittest.TestCase()
t.maxDiff = None # we always want to see full diff
t.assertEqual(run.status, 'Succeeded')
client = KfpMlmdClient(mlmd_connection_config=mlmd_connection_config)
tasks = client.get_tasks(run_id=run.id)
verify_tasks(t, tasks)
def verify_with_default_pipeline_root(run: kfp_server_api.ApiRun,
mlmd_connection_config, **kwargs):
t = unittest.TestCase()
t.maxDiff = None # we always want to see full diff
t.assertEqual(run.status, 'Succeeded')
client = KfpMlmdClient(mlmd_connection_config=mlmd_connection_config)
tasks = client.get_tasks(run_id=run.id)
verify_tasks(t, tasks)
verify_artifacts(t, tasks, 'minio://mlpipeline/v2/artifacts')
def verify_with_specific_pipeline_root(run: kfp_server_api.ApiRun,
mlmd_connection_config, **kwargs):
t = unittest.TestCase()
t.maxDiff = None # we always want to see full diff
t.assertEqual(run.status, 'Succeeded')
client = KfpMlmdClient(mlmd_connection_config=mlmd_connection_config)
tasks = client.get_tasks(run_id=run.id)
verify_tasks(t, tasks)
verify_artifacts(t, tasks, 'minio://mlpipeline/override/artifacts')
if __name__ == '__main__':
run_pipeline_func([
TestCase(
pipeline_func=two_step_pipeline,
mode=kfp.dsl.PipelineExecutionMode.V1_LEGACY),
# Cannot test V2_ENGINE and V1_LEGACY using the same code.
# V2_ENGINE requires importing everything from v2 namespace.
# TestCase(
# pipeline_func=two_step_pipeline,
# verify_func=verify,
# mode=kfp.dsl.PipelineExecutionMode.V2_ENGINE,
# ),
])
# %%