An example for creating trial scheduler and search algorithm using Ray Tune

initialize_ray_tune.py

logger.info("Initializing ray")
ray.init(configure_logging=False)

logger.info("Initializing ray search space")
search_space, intial_best_config = create_search_space()

logger.info("Initializing scheduler and search algorithms")
# Use HyperBand scheduler to earlystop unpromising runs
scheduler = AsyncHyperBandScheduler(time_attr='training_iteration',
					metric="val_loss",
					mode="min",
					grace_period=10)

# Use bayesian optimisation with TPE implemented by hyperopt
search_alg = HyperOptSearch(search_space,
				metric="val_loss",
				mode="min",
				points_to_evaluate=intial_best_config)

# We limit concurrent trials to 2 since bayesian optimisation doesn't parallelize very well
search_alg = ConcurrencyLimiter(search_alg, max_concurrent=2)