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Richard Liaw
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
#!/usr/bin/env python
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"""Simple MLFLow Logger example.
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This uses a simple MLFlow logger. One limitation of this is that there is
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no artifact support; to save artifacts with Tune and MLFlow, you will need to
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start a MLFlow run inside the Trainable function/class.
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"""
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import mlflow
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from mlflow.tracking import MlflowClient
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import time
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import random
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from ray import tune
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from ray.tune.logger import MLFLowLogger, DEFAULT_LOGGERS
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def evaluation_fn(step, width, height):
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return (0.1 + width * step / 100)**(-1) + height * 0.1
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def easy_objective(config):
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# Hyperparameters
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width, height = config["width"], config["height"]
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for step in range(config.get("steps", 100)):
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# Iterative training function - can be any arbitrary training procedure
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intermediate_score = evaluation_fn(step, width, height)
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# Feed the score back back to Tune.
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tune.report(iterations=step, mean_loss=intermediate_score)
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time.sleep(0.1)
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if __name__ == "__main__":
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client = MlflowClient()
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experiment_id = client.create_experiment("test")
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trials = tune.run(
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easy_objective,
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name="mlflow",
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num_samples=5,
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loggers=DEFAULT_LOGGERS + (MLFLowLogger, ),
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config={
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"mlflow_experiment_id": experiment_id,
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"width": tune.sample_from(
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lambda spec: 10 + int(90 * random.random())),
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"height": tune.sample_from(lambda spec: int(100 * random.random()))
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})
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df = mlflow.search_runs([experiment_id])
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print(df)
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