def run_param_specs(param_specs):
'''Run the given param_specs in parallel trials using ray. Used for benchmarking.'''
ray.init()
ray_trials = tune.run(
ray_trainable,
name='param_specs',
config={
'spec': tune.grid_search(param_specs),
'trial_index': 0,
},
resources_per_trial=infer_trial_resources(param_specs[0]),
num_samples=1,
reuse_actors=False,
server_port=util.get_port(),
)
ray.shutdown()
def run_ray_search(spec):
'''
Method to run ray search from experiment. Uses RandomSearch now.
TODO support for other ray search algorithms: https://ray.readthedocs.io/en/latest/tune-searchalg.html
'''
logger.info(f'Running ray search for spec {spec["name"]}')
# generate trial index to pass into Lab Trial
global trial_index # make gen_trial_index passable into ray.run
trial_index = -1
def gen_trial_index():
global trial_index
trial_index += 1
return trial_index
ray.init()
ray_trials = tune.run(
ray_trainable,
name=spec['name'],
config={
'spec': spec,
'trial_index': tune.sample_from(lambda spec: gen_trial_index()),
**build_config_space(spec)
},
resources_per_trial=infer_trial_resources(spec),
num_samples=spec['meta']['max_trial'],
reuse_actors=False,
server_port=util.get_port(),
)
trial_data_dict = {} # data for Lab Experiment to analyze
for ray_trial in ray_trials:
ray_trial_data = ray_trial.last_result['trial_data']
trial_data_dict.update(ray_trial_data)
ray.shutdown()
return trial_data_dict