54 lines
2.0 KiB
Python
54 lines
2.0 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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import kfp.deprecated.dsl as dsl
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from kfp.deprecated.components import create_component_from_func
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# Advanced function
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# Demonstrates imports, helper functions and multiple outputs
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from typing import NamedTuple
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@create_component_from_func
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def table_visualization(train_file_path: str = 'https://raw.githubusercontent.com/zijianjoy/pipelines/5651f41071816594b2ed27c88367f5efb4c60b50/samples/core/visualization/table.csv') -> NamedTuple('VisualizationOutput', [('mlpipeline_ui_metadata', 'UI_metadata')]):
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"""Provide number to visualize as table metrics."""
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import json
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header = ['Average precision ', 'Precision', 'Recall']
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metadata = {
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'outputs' : [{
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'type': 'table',
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'storage': 'gcs',
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'format': 'csv',
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'header': header,
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'source': train_file_path
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}]
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}
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from collections import namedtuple
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visualization_output = namedtuple('VisualizationOutput', [
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'mlpipeline_ui_metadata'])
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return visualization_output(json.dumps(metadata))
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@dsl.pipeline(
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name='table-pipeline',
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description='A sample pipeline to generate scalar metrics for UI visualization.'
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)
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def table_pipeline():
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table_visualization_task = table_visualization()
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# You can also upload samples/core/visualization/table.csv to Google Cloud Storage.
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# And call the component function with gcs path parameter like below:
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# table_visualization_task = table_visualization('gs://<bucket-name>/<path>/table.csv')
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