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* add pipeline tuner component and dependencies. * clean code. * do not need force rerun. * replace the resources. * update metrics retrieving. * Update test/pipeline_tuning_example/requirements.txt * Update test/pipeline_tuning_example/train/env.yaml * Update test/pipeline_tuning_example/tuner/env.yaml * Update test/pipeline_tuning_example/tuner/tuner_func.py * Update test/pipeline_tuning_example/data_prep/env.yaml * fix issues found by lint with flake8. * add documentation * add data. * do not need AML resource for local run. * AML -> AzureML * clean code. * Update website/docs/Examples/Tune-AzureML pipeline.md * rename and add pip install. * update figure name. * align docs with code. * remove extra line.
27 lines
556 B
YAML
27 lines
556 B
YAML
$schema: https://componentsdk.azureedge.net/jsonschema/CommandComponent.json
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name: data_prep
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version: 0.0.1
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display_name: Data preparation for training
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type: CommandComponent
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inputs:
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data:
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type: path
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test_train_ratio:
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type: float
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outputs:
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train_data:
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type: path
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test_data:
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type: path
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environment:
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conda:
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conda_dependencies_file: env.yaml
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os: Linux
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command: >-
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python data_prep.py
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--data {inputs.data}
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--test_train_ratio {inputs.test_train_ratio}
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--train_data {outputs.train_data}
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--test_data {outputs.test_data}
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