def testCustomResources2(self):
ray.init(num_cpus=4, num_gpus=2, resources={"a": 2})
runner = TrialRunner()
resource1 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 2})
self.assertTrue(runner.has_resources(resource1))
resource2 = Resources(cpu=1, gpu=0, custom_resources={"a": 2})
self.assertTrue(runner.has_resources(resource2))
resource3 = Resources(cpu=1, gpu=0, custom_resources={"a": 3})
self.assertFalse(runner.has_resources(resource3))
resource4 = Resources(cpu=1, gpu=0, extra_custom_resources={"a": 3})
self.assertFalse(runner.has_resources(resource4))
def choose_trial_to_run(self, trial_runner: TrialRunner):
"""
Ensures all trials get fair share of time (as defined by time_attr).
This enables the PBT scheduler to support a greater number of
concurrent trials than can fit in the cluster at any given time.
"""
candidates = []
for trial in trial_runner.get_trials():
if trial.status in [
Trial.PENDING, Trial.PAUSED
] and trial_runner.has_resources(trial.resources):
candidates.append(trial)
candidates.sort(key=lambda trial: self._trials_states_dict[trial].
last_perturbation_time)
return candidates[0] if candidates else None
