def testTrialErrorOnStart(self):
ray.init()
_default_registry.register(TRAINABLE_CLASS, "asdf", None)
trial = Trial("asdf")
try:
trial.start()
except Exception as e:
self.assertIn("a class", str(e))
def register_custom_model(model_name, model_class):
"""Register a custom model class by name.
The model can be later used by specifying {"custom_model": model_name}
in the model config.
Args:
model_name (str): Name to register the model under.
model_class (type): Python class of the model.
"""
_default_registry.register(RLLIB_MODEL, model_name, model_class)
def register_custom_model(model_name, model_class):
"""Register a custom model class by name.
The model can be later used by specifying {"custom_model": model_name}
in the model config.
Args:
model_name (str): Name to register the model under.
model_class (type): Python class of the model.
"""
_default_registry.register(RLLIB_MODEL, model_name, model_class)
def register_custom_preprocessor(preprocessor_name, preprocessor_class):
"""Register a custom preprocessor class by name.
The preprocessor can be later used by specifying
{"custom_preprocessor": preprocesor_name} in the model config.
Args:
preprocessor_name (str): Name to register the preprocessor under.
preprocessor_class (type): Python class of the preprocessor.
"""
_default_registry.register(RLLIB_PREPROCESSOR, preprocessor_name,
preprocessor_class)
def register_custom_preprocessor(preprocessor_name, preprocessor_class):
"""Register a custom preprocessor class by name.
The preprocessor can be later used by specifying
{"custom_preprocessor": preprocesor_name} in the model config.
Args:
preprocessor_name (str): Name to register the preprocessor under.
preprocessor_class (type): Python class of the preprocessor.
"""
_default_registry.register(
RLLIB_PREPROCESSOR, preprocessor_name, preprocessor_class)
def testErrorHandling(self):
ray.init(num_cpus=4, num_gpus=2)
runner = TrialRunner()
kwargs = {
"stopping_criterion": {
"training_iteration": 1
},
"resources": Resources(cpu=1, gpu=1),
}
_default_registry.register(TRAINABLE_CLASS, "asdf", None)
trials = [Trial("asdf", **kwargs), Trial("__fake", **kwargs)]
for t in trials:
runner.add_trial(t)
runner.step()
self.assertEqual(trials[0].status, Trial.ERROR)
self.assertEqual(trials[1].status, Trial.PENDING)
runner.step()
self.assertEqual(trials[0].status, Trial.ERROR)
self.assertEqual(trials[1].status, Trial.RUNNING)
def register_model(name, model):
_default_registry.register(RLLIB_MODEL, name, model)
