wehub-resource-sync
129 lines
3.7 KiB
Python
129 lines
3.7 KiB
Python
#!/usr/bin/env python
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"""Examples using MLfowLoggerCallback and setup_mlflow.
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"""
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import os
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import tempfile
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import time
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import mlflow
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from ray import tune
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from ray.air.integrations.mlflow import MLflowLoggerCallback, setup_mlflow
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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 train_function(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 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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def tune_with_callback(mlflow_tracking_uri, finish_fast=False):
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tuner = tune.Tuner(
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train_function,
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run_config=tune.RunConfig(
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name="mlflow",
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callbacks=[
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MLflowLoggerCallback(
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tracking_uri=mlflow_tracking_uri,
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experiment_name="example",
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save_artifact=True,
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)
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],
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),
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tune_config=tune.TuneConfig(
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num_samples=5,
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),
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param_space={
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"width": tune.randint(10, 100),
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"height": tune.randint(0, 100),
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"steps": 5 if finish_fast else 100,
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},
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)
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tuner.fit()
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def train_function_mlflow(config):
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setup_mlflow(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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# Log the metrics to mlflow
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mlflow.log_metrics(dict(mean_loss=intermediate_score), step=step)
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# Feed the score 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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def tune_with_setup(mlflow_tracking_uri, finish_fast=False):
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# Set the experiment, or create a new one if does not exist yet.
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mlflow.set_tracking_uri(mlflow_tracking_uri)
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mlflow.set_experiment(experiment_name="mixin_example")
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tuner = tune.Tuner(
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train_function_mlflow,
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run_config=tune.RunConfig(
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name="mlflow",
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),
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tune_config=tune.TuneConfig(
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num_samples=5,
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),
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param_space={
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"width": tune.randint(10, 100),
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"height": tune.randint(0, 100),
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"steps": 5 if finish_fast else 100,
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"mlflow": {
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"experiment_name": "mixin_example",
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"tracking_uri": mlflow.get_tracking_uri(),
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},
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},
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)
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tuner.fit()
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if __name__ == "__main__":
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--smoke-test", action="store_true", help="Finish quickly for testing"
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)
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parser.add_argument(
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"--tracking-uri",
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type=str,
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help="The tracking URI for the MLflow tracking server.",
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)
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args, _ = parser.parse_known_args()
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if args.smoke_test:
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mlflow_tracking_uri = os.path.join(tempfile.gettempdir(), "mlruns")
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else:
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mlflow_tracking_uri = args.tracking_uri
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tune_with_callback(mlflow_tracking_uri, finish_fast=args.smoke_test)
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if not args.smoke_test:
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df = mlflow.search_runs(
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[mlflow.get_experiment_by_name("example").experiment_id]
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)
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print(df)
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tune_with_setup(mlflow_tracking_uri, finish_fast=args.smoke_test)
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if not args.smoke_test:
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df = mlflow.search_runs(
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[mlflow.get_experiment_by_name("mixin_example").experiment_id]
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)
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print(df)
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