61 lines
2.2 KiB
Python
61 lines
2.2 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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import kfp.deprecated.components as comp
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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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@comp.create_component_from_func
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def markdown_visualization() -> NamedTuple('VisualizationOutput', [('mlpipeline_ui_metadata', 'UI_metadata')]):
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import json
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# Exports a sample tensorboard:
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metadata = {
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'outputs': [
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{
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# Markdown that is hardcoded inline
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'storage': 'inline',
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'source': '''# Inline Markdown
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* [Kubeflow official doc](https://www.kubeflow.org/).
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''',
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'type': 'markdown',
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},
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{
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# Markdown that is read from a file
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'source': 'https://raw.githubusercontent.com/kubeflow/pipelines/master/README.md',
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# Alternatively, use Google Cloud Storage for sample.
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# 'source': 'gs://jamxl-kfp-bucket/v2-compatible/markdown/markdown_example.md',
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'type': 'markdown',
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}]
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}
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from collections import namedtuple
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divmod_output = namedtuple('VisualizationOutput', [
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'mlpipeline_ui_metadata'])
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return divmod_output(json.dumps(metadata))
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@dsl.pipeline(
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name='markdown-pipeline',
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description='A sample pipeline to generate markdown for UI visualization.'
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)
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def markdown_pipeline():
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# Passing a task output reference as operation arguments
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# For an operation with a single return value, the output reference can be accessed using `task.output` or `task.outputs['output_name']` syntax
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markdown_visualization_task = markdown_visualization()
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