# __trainable_end__
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--smoke-test",
action="store_true",
help="Finish quickly for testing")
args, _ = parser.parse_known_args()
ray.init()
datasets.MNIST("~/data", train=True, download=True)
# check if PytorchTrainble will save/restore correctly before execution
validate_save_restore(PytorchTrainble)
validate_save_restore(PytorchTrainble, use_object_store=True)
# __pbt_begin__
scheduler = PopulationBasedTraining(
time_attr="training_iteration",
metric="mean_accuracy",
mode="max",
perturbation_interval=5,
hyperparam_mutations={
# distribution for resampling
"lr": lambda: np.random.uniform(0.0001, 1),
# allow perturbations within this set of categorical values
"momentum": [0.8, 0.9, 0.99],
})
def testAsyncHyperbandExample(self):
from ray.tune.examples.async_hyperband_example import MyTrainableClass
validate_save_restore(MyTrainableClass)
validate_save_restore(MyTrainableClass, use_object_store=True)
def testLogging(self):
from ray.tune.examples.logging_example import MyTrainableClass
validate_save_restore(MyTrainableClass)
validate_save_restore(MyTrainableClass, use_object_store=True)
def testPyTorchMNIST(self):
from ray.tune.examples.mnist_pytorch_trainable import TrainMNIST
from torchvision import datasets
datasets.MNIST("~/data", train=True, download=True)
validate_save_restore(TrainMNIST)
validate_save_restore(TrainMNIST, use_object_store=True)
def testPBTKeras(self):
from ray.tune.examples.pbt_tune_cifar10_with_keras import Cifar10Model
from tensorflow.python.keras.datasets import cifar10
cifar10.load_data()
validate_save_restore(Cifar10Model)
validate_save_restore(Cifar10Model, use_object_store=True)
def testTensorFlowMNIST(self):
from ray.tune.examples.tune_mnist_ray_hyperband import TrainMNIST
validate_save_restore(TrainMNIST)
validate_save_restore(TrainMNIST, use_object_store=True)
