wehub-resource-sync
60 lines
2.6 KiB
Plaintext
60 lines
2.6 KiB
Plaintext
name: HyperparameterSearch
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python_env: python_env.yaml
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entry_points:
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# train Keras DL model
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train:
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parameters:
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training_data: {type: string, default: "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-white.csv"}
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epochs: {type: int, default: 32}
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batch_size: {type: int, default: 16}
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learning_rate: {type: float, default: 1e-1}
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momentum: {type: float, default: .0}
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seed: {type: int, default: 97531}
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command: "python train.py {training_data}
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--batch-size {batch_size}
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--epochs {epochs}
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--learning-rate {learning_rate}
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--momentum {momentum}"
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# Use random search to optimize hyperparams of the train entry_point.
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random:
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parameters:
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training_data: {type: string, default: "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-white.csv"}
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max_runs: {type: int, default: 8}
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max_p: {type: int, default: 2}
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epochs: {type: int, default: 32}
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metric: {type: string, default: "rmse"}
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seed: {type: int, default: 97531}
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command: "python search_random.py {training_data}
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--max-runs {max_runs}
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--max-p {max_p}
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--epochs {epochs}
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--metric {metric}
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--seed {seed}"
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# Use Hyperopt to optimize hyperparams of the train entry_point.
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hyperopt:
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parameters:
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training_data: {type: string, default: "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-white.csv"}
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max_runs: {type: int, default: 12}
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epochs: {type: int, default: 32}
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metric: {type: string, default: "rmse"}
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algo: {type: string, default: "tpe.suggest"}
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seed: {type: int, default: 97531}
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command: "python -O search_hyperopt.py {training_data}
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--max-runs {max_runs}
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--epochs {epochs}
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--metric {metric}
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--algo {algo}
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--seed {seed}"
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main:
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parameters:
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training_data: {type: string, default: "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/winequality-white.csv"}
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command: "python search_random.py {training_data}"
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