cloudml-hypertune

import hypertune import argparse import os def train_model(learning_rate, num_epochs, metric_tag): # Simulate model training with hyperparameters # In a real scenario, this would be your ML training loop print(f"Training with learning_rate={learning_rate}, num_epochs={num_epochs}") # Simulate a metric, e.g., validation accuracy # In a real scenario, you'd get this from your model's evaluation metric_value = 0.5 + (learning_rate * 0.1) + (num_epochs * 0.01) # Report the metric to Cloud AI Platform / Vertex AI hpt = hypertune.HyperTune() hpt.report_hyperparameter_tuning_metric( hyperparameter_metric_tag=metric_tag, # Must match config.yaml objective metricTag metric_value=metric_value, global_step=num_epochs # Or current training step ) print(f"Reported metric '{metric_tag}': {metric_value} at step {num_epochs}") if __name__ == '__main__': parser = argparse.ArgumentParser() # Define hyperparameters as command-line arguments parser.add_argument( '--learning_rate', type=float, default=0.01, help='Learning rate for training.' ) parser.add_argument( '--num_epochs', type=int, default=10, help='Number of epochs for training.' ) parser.add_argument( '--metric_tag', type=str, default='accuracy', help='Tag for the metric reported to HyperTune.' ) args = parser.parse_args() train_model(args.learning_rate, args.num_epochs, args.metric_tag)