dora-search

import dora import dora.xp as xp import time import os from typing import Dict def train_model(config: Dict): """Simulates a training run with a given configuration.""" print(f"[Experiment {config.get('_xp_id', 'local')}] Running with config: {config}") # Simulate some work time.sleep(0.1) result = config["lr"] * config["batch_size"] print(f"[Experiment {config.get('_xp_id', 'local')}] Result: {result}") # Dora automatically saves results in the experiment directory xp_dir = config.get('_xp_dir') if xp_dir: os.makedirs(xp_dir, exist_ok=True) with open(os.path.join(xp_dir, 'metrics.json'), 'w') as f: import json json.dump({'loss': result, 'accuracy': 1 - result / 100}, f) return {"loss": result, "accuracy": 1 - result / 100} # Define the grid search parameters grid = [ xp.arg("lr", [0.01, 0.1, 1.0]), xp.arg("batch_size", [16, 32]) ] if __name__ == "__main__": print("Launching Dora experiments...") # dora.main is the recommended way to launch experiments # It will iterate through the grid, call train_model for each configuration, # and manage experiment directories and logs. dora.main(train_model, grid=grid) print("Dora experiments finished.")