def train(self,
data,
epochs=1,
batch_size=32,
profile=False,
reduce_results=True,
info=None,
feature_cols=None,
label_cols=None):
"""
See the documentation in
'zoo.orca.learn.pytorch.estimator.PyTorchRayEstimatorWrapper.fit'.
"""
from zoo.orca.data import SparkXShards
data, _ = maybe_dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=label_cols,
mode="fit")
if isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data = process_xshards_of_pandas_dataframe(
data, feature_cols, label_cols)
from zoo.orca.data.utils import process_spark_xshards
ray_xshards = process_spark_xshards(data, self.num_workers)
def transform_func(worker, partition_refs):
data_creator = partition_refs_to_creator(partition_refs)
# Should not wrap DistributedSampler on DataLoader for SparkXShards input.
return worker.train_epochs.remote(data_creator, epochs,
batch_size, profile, info,
False)
worker_stats = ray_xshards.reduce_partitions_for_actors(
self.remote_workers, transform_func)
else:
assert isinstance(data, types.FunctionType), \
"data should be either an instance of SparkXShards or a callable function, but " \
"got type: {}".format(type(data))
success, worker_stats = self._train_epochs(data,
epochs=epochs,
batch_size=batch_size,
profile=profile,
info=info)
epoch_stats = list(map(list, zip(*worker_stats)))
if reduce_results:
for i in range(len(epoch_stats)):
epoch_stats[i] = self._process_stats(epoch_stats[i])
return epoch_stats
else:
return epoch_stats
def validate(self,
data,
batch_size=32,
num_steps=None,
profile=False,
info=None,
feature_cols=None,
label_cols=None):
"""
See the documentation in
'zoo.orca.learn.pytorch.estimator.PyTorchRayEstimatorWrapper.evaluate'.
"""
from zoo.orca.data import SparkXShards
data, _ = maybe_dataframe_to_xshards(data,
validation_data=None,
feature_cols=feature_cols,
label_cols=label_cols,
mode="evaluate")
if isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data = process_xshards_of_pandas_dataframe(
data, feature_cols, label_cols)
from zoo.orca.data.utils import process_spark_xshards
ray_xshards = process_spark_xshards(data, self.num_workers)
def transform_func(worker, partition_refs):
data_creator = partition_refs_to_creator(partition_refs)
# Should not wrap DistributedSampler on DataLoader for SparkXShards input.
return worker.validate.remote(data_creator, batch_size,
num_steps, profile, info, False)
worker_stats = ray_xshards.reduce_partitions_for_actors(
self.remote_workers, transform_func)
else:
assert isinstance(data, types.FunctionType), \
"data should be either an instance of SparkXShards or a callable function, but " \
"got type: {}".format(type(data))
params = dict(data_creator=data,
batch_size=batch_size,
num_steps=num_steps,
profile=profile,
info=info)
worker_stats = ray.get(
[w.validate.remote(**params) for w in self.remote_workers])
return self._process_stats(worker_stats)
def train(self,
data,
epochs=1,
batch_size=32,
profile=False,
reduce_results=True,
info=None):
"""
See the documentation in
'zoo.orca.learn.pytorch.estimator.PyTorchRayEstimatorWrapper.fit'.
"""
from zoo.orca.data import SparkXShards
if isinstance(data, SparkXShards):
from zoo.orca.data.utils import process_spark_xshards
ray_xshards = process_spark_xshards(data, self.num_workers)
def transform_func(worker, shards_ref):
data_creator = shards_ref_to_creator(shards_ref)
# Should not wrap DistributedSampler on DataLoader for SparkXShards input.
return worker.train_epochs.remote(data_creator, epochs,
batch_size, profile, info,
False)
stats_shards = ray_xshards.transform_shards_with_actors(
self.remote_workers, transform_func, gang_scheduling=True)
worker_stats = stats_shards.collect_partitions()
else:
assert isinstance(data, types.FunctionType), \
"data should be either an instance of SparkXShards or a callable function, but " \
"got type: {}".format(type(data))
success, worker_stats = self._train_epochs(data,
epochs=epochs,
batch_size=batch_size,
profile=profile,
info=info)
epoch_stats = list(map(list, zip(*worker_stats)))
if reduce_results:
for i in range(len(epoch_stats)):
epoch_stats[i] = self._process_stats(epoch_stats[i])
return epoch_stats
else:
return epoch_stats
def validate(self,
data,
batch_size=32,
num_steps=None,
profile=False,
info=None):
"""
See the documentation in
'zoo.orca.learn.pytorch.estimator.PyTorchRayEstimatorWrapper.evaluate'.
"""
from zoo.orca.data import SparkXShards
if isinstance(data, SparkXShards):
from zoo.orca.data.utils import process_spark_xshards
ray_xshards = process_spark_xshards(data, self.num_workers)
def transform_func(worker, shards_ref):
data_creator = shards_ref_to_creator(shards_ref)
# Should not wrap DistributedSampler on DataLoader for SparkXShards input.
return worker.validate.remote(data_creator, batch_size,
num_steps, profile, info, False)
stats_shards = ray_xshards.transform_shards_with_actors(
self.remote_workers, transform_func, gang_scheduling=True)
worker_stats = stats_shards.collect_partitions()
else:
assert isinstance(data, types.FunctionType), \
"data should be either an instance of SparkXShards or a callable function, but " \
"got type: {}".format(type(data))
params = dict(data_creator=data,
batch_size=batch_size,
num_steps=num_steps,
profile=profile,
info=info)
worker_stats = ray.get(
[w.validate.remote(**params) for w in self.remote_workers])
return self._process_stats(worker_stats)
def fit(self,
data,
epochs=1,
batch_size=32,
validation_data=None,
train_resize_batch_num=None):
"""
Trains an MXNet model given train_data (with val_data) for several epochs.
:param data: An instance of SparkXShards or a function that takes config and kv as
arguments and returns an MXNet DataIter/DataLoader for training.
You can specify data related configurations for this function in the config argument above.
kv is an instance of MXNet distributed key-value store. kv.num_workers and kv.rank
can be used in this function to split data for different workers if necessary.
:param epochs: The number of epochs to train the MXNet model. Default is 1.
:param batch_size: The number of samples per batch for each worker. Default is 32.
:param validation_data: An instance of SparkXShards or a function that takes config and
kv as arguments and returns an MXNet DataIter/DataLoader for validation.
You can specify data related configurations for this function in the config argument above.
kv is an instance of MXNet distributed key-value store. kv.num_workers and kv.rank
can be used in this function to split data for different workers if necessary.
:param train_resize_batch_num: The number of batches per epoch to resize to.
Default is None. You might need to specify this if the size of train_data for each
worker varies. MXNet distributed training would crash when the first worker finishes
the training if the workers have unbalanced training data.
See this issue for more details: https://github.com/apache/incubator-mxnet/issues/17651
"""
if validation_data:
assert self.validation_metrics_creator,\
"Metrics not defined for validation, please specify validation_metrics_creator " \
"when creating the Estimator"
from zoo.orca.data import SparkXShards
if isinstance(data, SparkXShards):
ray_xshards = process_spark_xshards(data, self.num_workers)
if validation_data is None:
def transform_func(worker, shards_ref):
data_creator = shards_ref_to_creator(shards_ref,
shuffle=True)
return worker.train.remote(data_creator, epochs,
batch_size, None,
train_resize_batch_num)
stats_shards = ray_xshards.transform_shards_with_actors(
self.workers, transform_func, gang_scheduling=True)
else:
val_ray_xshards = process_spark_xshards(
validation_data, self.num_workers)
def zip_func(worker, this_shards_ref, that_shards_ref):
data_creator = shards_ref_to_creator(this_shards_ref,
shuffle=True)
validation_data_creator = shards_ref_to_creator(
that_shards_ref, shuffle=True)
return worker.train.remote(data_creator, epochs,
batch_size,
validation_data_creator,
train_resize_batch_num)
stats_shards = ray_xshards.zip_shards_with_actors(
val_ray_xshards,
self.workers,
zip_func,
gang_scheduling=True)
server_stats = [
server.train.remote(None, epochs, batch_size, None,
train_resize_batch_num)
for server in self.servers
]
worker_stats = stats_shards.collect()
server_stats = ray.get(server_stats)
server_stats = list(itertools.chain.from_iterable(server_stats))
stats = worker_stats + server_stats
else: # data_creator functions; should return Iter or DataLoader
assert isinstance(data, types.FunctionType),\
"train_data should be either an instance of SparkXShards or a callable function"
train_data_list = [data] * self.num_workers
if validation_data:
assert isinstance(validation_data, types.FunctionType),\
"val_data should be either an instance of SparkXShards or a callable function"
val_data_list = [validation_data] * self.num_workers
self.runners = self.workers + self.servers
# For servers, data is not used and thus just input a None value.
train_data_list += [None] * self.num_servers
val_data_list += [None] * self.num_servers
stats = ray.get([
runner.train.remote(train_data_list[i], epochs, batch_size,
val_data_list[i], train_resize_batch_num)
for i, runner in enumerate(self.runners)
])
stats = list(itertools.chain.from_iterable(stats))
return stats
def fit(self,
data,
epochs=1,
batch_size=32,
verbose=1,
callbacks=None,
validation_data=None,
class_weight=None,
steps_per_epoch=None,
validation_steps=None,
validation_freq=1,
data_config=None,
feature_cols=None,
label_cols=None):
"""
Train this tensorflow model with train data.
:param data: train data. It can be XShards, Spark DataFrame or creator function which
returns Iter or DataLoader.
If data is XShards, each partition can be a Pandas DataFrame or a dictionary of
{'x': feature, 'y': label}, where feature(label) is a numpy array or a tuple of
numpy arrays.
:param epochs: Number of epochs to train the model. Default: 1.
:param batch_size: Batch size used for training. Default: 32.
:param verbose: Prints output of one model if true.
:param callbacks: List of Keras compatible callbacks to apply during training.
:param validation_data: validation data. Validation data type should be the same
as train data.
:param class_weight: Optional dictionary mapping class indices (integers) to a weight
(float) value, used for weighting the loss function. This can be useful to tell
the model to "pay more attention" to samples from an under-represented class.
:param steps_per_epoch: Total number of steps (batches of samples) before declaring one
epoch finished and starting the next epoch. If `steps_pre_epoch` is `None`, the
epoch will run until the input dataset is exhausted. When passing an infinitely
repeating dataset, you must specify the `step_per_epoch` argument.
:param validation_steps: Total number of steps (batches of samples) to draw before stopping
when performing validation at the end of every epoch. Default: None.
:param validation_freq: Only relevant if validation data is provided. Integer of
`collections_abc.Container` instance (e.g. list, tuple, etc.). If an integer,
specifies how many training epochs to run before a new validation run is performed,
e.g. `validation_freq=2` runs validation every 2 epochs. If a Container, specifies
the epochs on which to run validation, e.g. `validation_freq=[1, 2, 10]` runs
validation at the end of the 1st, 2nd, and 10th epochs.
:param data_config: An optional dictionary that can be passed to data creator function.
:param feature_cols: Feature column name(s) of data. Only used when data is a Spark
DataFrame or an XShards of Pandas DataFrame. Default: None.
:param label_cols: Label column name(s) of data. Only used when data is a Spark DataFrame or
an XShards of Pandas DataFrame.
Default: None.
:return:
"""
params = dict(epochs=epochs,
batch_size=batch_size,
verbose=verbose,
callbacks=callbacks,
class_weight=class_weight,
steps_per_epoch=steps_per_epoch,
validation_steps=validation_steps,
validation_freq=validation_freq,
data_config=data_config)
from zoo.orca.data import SparkXShards
data, validation_data = maybe_dataframe_to_xshards(
data,
validation_data,
feature_cols,
label_cols,
mode="fit",
num_workers=self.num_workers,
accept_str_col=True)
if isinstance(data, SparkXShards):
if data._get_class_name() == 'pandas.core.frame.DataFrame':
data, validation_data = process_xshards_of_pandas_dataframe(
data, feature_cols, label_cols, validation_data, "fit")
ray_xshards = process_spark_xshards(data, self.num_workers)
if validation_data is None:
def transform_func(worker, partition_refs):
params["data_creator"] = make_data_creator(partition_refs)
return worker.step.remote(**params)
worker_stats = ray_xshards.reduce_partitions_for_actors(
self.remote_workers, transform_func)
else:
val_ray_xshards = process_spark_xshards(
validation_data, self.num_workers)
def zip_func(worker, this_partition_refs, that_partition_refs):
params["data_creator"] = make_data_creator(
this_partition_refs)
params["validation_data_creator"] = \
make_data_creator(that_partition_refs)
return worker.step.remote(**params)
worker_stats = ray_xshards.zip_reduce_shards_with_actors(
val_ray_xshards, self.remote_workers, zip_func)
else:
params["data_creator"] = data
params["validation_data_creator"] = validation_data
params_list = [params] * self.num_workers
worker_stats = ray.get([
self.remote_workers[i].step.remote(**params_list[i])
for i in range(self.num_workers)
])
worker_stats = list(itertools.chain.from_iterable(worker_stats))
stats = worker_stats[0].copy()
return stats
