def prepare_searcher(data,
search_space,
stop,
validation_data=None,
model_creator=linear_model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator,
feature_transformer=None,
metric="mse",
name="demo"):
modelBuilder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir="~/zoo_automl_logs",
resources_per_trial={"cpu": 2},
name=name)
searcher.compile(data=data,
validation_data=validation_data,
model_create_func=modelBuilder,
search_space=search_space,
n_sampling=2,
epochs=stop["training_iteration"],
metric_threshold=stop["reward_metric"],
feature_transformers=feature_transformer,
metric=metric)
return searcher
def from_keras(
*,
model_creator,
logs_dir,
resources_per_trial,
name,
):
"""
Create an AutoEstimator for tensorflow keras.
:param model_creator: Tensorflow keras model creator function.
:param logs_dir: Local directory to save logs and results.
:param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
:param name: Name of the auto estimator.
:return: an AutoEstimator object.
"""
from zoo.automl.model import ModelBuilder
from zoo.automl.search import SearchEngineFactory
model_builder = ModelBuilder.from_tfkeras(model_creator=model_creator)
searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
name=name)
return AutoEstimator(model_builder=model_builder, searcher=searcher)
def __init__(self,
model_builder,
logs_dir="/tmp/auto_estimator_logs",
resources_per_trial=None,
name=None):
self.model_builder = model_builder
self.searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
name=name)
self._fitted = False
def from_torch(
*,
model_creator,
optimizer,
loss,
logs_dir,
resources_per_trial,
name,
):
"""
Create an AutoEstimator for torch.
:param model_creator: PyTorch model creator function.
:param optimizer: PyTorch optimizer creator function or pytorch optimizer name (string).
Note that you should specify learning rate search space with key as "lr" or LR_NAME
(from zoo.orca.automl.pytorch_utils import LR_NAME) if input optimizer name.
Without learning rate search space specified, the default learning rate value of 1e-3
will be used for all estimators.
:param loss: PyTorch loss instance or PyTorch loss creator function
or pytorch loss name (string).
:param logs_dir: Local directory to save logs and results.
:param resources_per_trial: Dict. resources for each trial. e.g. {"cpu": 2}.
:param name: Name of the auto estimator.
:return: an AutoEstimator object.
"""
from zoo.orca.automl.pytorch_utils import validate_pytorch_loss, \
validate_pytorch_optim
from zoo.automl.model import ModelBuilder
from zoo.automl.search import SearchEngineFactory
loss = validate_pytorch_loss(loss)
optimizer = validate_pytorch_optim(optimizer)
model_builder = ModelBuilder.from_pytorch(model_creator=model_creator,
optimizer_creator=optimizer,
loss_creator=loss)
searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir=logs_dir,
resources_per_trial=resources_per_trial,
name=name)
return AutoEstimator(model_builder=model_builder, searcher=searcher)
def prepare_searcher(data,
model_creator=linear_model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator,
feature_transformer=None,
recipe=SimpleRecipe(),
name="demo"):
modelBuilder = ModelBuilder.from_pytorch(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
searcher = SearchEngineFactory.create_engine(backend="ray",
logs_dir="~/zoo_automl_logs",
resources_per_trial={"cpu": 2},
name=name)
search_space = recipe.search_space(feature_transformer.get_feature_list())\
if feature_transformer else None
searcher.compile(data=data,
model_create_func=modelBuilder,
recipe=recipe,
feature_transformers=feature_transformer,
search_space=search_space)
return searcher
def prepare_searcher(data,
model_creator=linear_model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator,
feature_transformer=None,
recipe=SimpleRecipe(),
metric="mse",
name="demo"):
modelBuilder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
searcher = SearchEngineFactory.create_engine(
backend="ray",
logs_dir="~/zoo_automl_logs",
resources_per_trial={"cpu": 2},
name=name)
searcher.compile(data=data,
model_create_func=modelBuilder,
recipe=recipe,
feature_transformers=feature_transformer,
metric=metric)
return searcher
train_x, train_y = get_linear_data(2, 5, 1000)
val_x, val_y = get_linear_data(2, 5, 400)
data = (train_x, train_y)
validation_data = (val_x, val_y)
return data, validation_data
if __name__ == "__main__":
# 1. the way to enable auto tuning model from creators.
init_orca_context(init_ray_on_spark=True)
modelBuilder = PytorchModelBuilder(model_creator=model_creator,
optimizer_creator=optimizer_creator,
loss_creator=loss_creator)
searcher = SearchEngineFactory.create_engine(backend="ray",
logs_dir="~/zoo_automl_logs",
resources_per_trial={"cpu": 2},
name="demo")
# pass input data, modelbuilder and recipe into searcher.compile. Note that if user doesn't pass
# feature transformer, the default identity feature transformer will be used.
data, validation_data = get_data()
searcher.compile(data=data,
validation_data=validation_data,
model_builder=modelBuilder,
search_space=create_simple_recipe(),
n_sampling=2,
epochs=20)
searcher.run()
best_trials = searcher.get_best_trials(k=1)
print(best_trials[0].config)
