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train_component.yaml
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train_component.yaml
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name: train xgboost
description: Trains xgboost model
inputs:
- { name: Data Path, type: Directory, default: 'ames_dataset', description: 'Path of the trainings data' }
- { name: Learning Rate, type: Float, default: '0.1', description: 'Learning rate for training' }
- { name: N Estimators, type: Integer, default: '100', description: 'Number of Estimators' }
outputs:
- { name: MLPipeline Metrics, type: Metrics }
implementation:
container:
image: us.gcr.io/gcp-bakdata-kubeflow-cluster/ames-xgboost-job-3.7.10-trained_ames_model.dat-v0.1:latest
command: [
python,
pipeline.py,
train_model,
--pipeline_run, 'True',
--data_path, { inputPath: Data Path },
--learning_rate, { inputValue: Learning Rate },
--n_estimators, { inputValue: N Estimators }
]
fileOutputs:
MLPipeline Metrics: /mlpipeline-metrics.json