cache=args.caching_dir,
preloading=args.data_preload,
features_name=args.features_name,
labels_name=args.labels_name)
# We initialize the Data object with the training data list
# so that we can use it to count the number of training examples
data.set_file_names(train_list)
validate_every = int(data.count_data() / args.batch)
# Some input arguments may be ignored depending on chosen algorithm
if args.mode == 'easgd':
algo = Algo(None,
loss=args.loss,
validate_every=validate_every,
mode='easgd',
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer,
worker_optimizer_params=args.worker_optimizer_params,
elastic_force=args.elastic_force / (comm.Get_size() - 1),
elastic_lr=args.elastic_lr,
elastic_momentum=args.elastic_momentum)
elif args.mode == 'gem':
algo = Algo('gem',
loss=args.loss,
validate_every=validate_every,
mode='gem',
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer,
worker_optimizer_params=args.worker_optimizer_params,
learning_rate=args.gem_lr,
momentum=args.gem_momentum,
kappa=args.gem_kappa)
cache=args.caching_dir,
preloading=args.data_preload,
features_name=args.features_name,
labels_name=args.labels_name)
# We initialize the Data object with the training data list
# so that we can use it to count the number of training examples
data.set_file_names(train_list)
validate_every = data.count_data() / args.batch
# Some input arguments may be ignored depending on chosen algorithm
if args.easgd:
algo = Algo(None,
loss=args.loss,
validate_every=validate_every,
mode='easgd',
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer,
elastic_force=args.elastic_force / (comm.Get_size() - 1),
elastic_lr=args.elastic_lr,
elastic_momentum=args.elastic_momentum)
else:
algo = Algo(args.optimizer,
loss=args.loss,
validate_every=validate_every,
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer)
# Creating the MPIManager object causes all needed worker and master nodes to be created
manager = MPIManager(comm=comm,
data=data,
algo=algo,
cache=args.caching_dir,
preloading=args.data_preload,
features_name=args.features_name,
labels_name=args.labels_name)
# We initialize the Data object with the training data list
# so that we can use it to count the number of training examples
data.set_file_names(train_list)
validate_every = int(data.count_data() / args.batch)
# Some input arguments may be ignored depending on chosen algorithm
if args.mode == 'easgd':
algo = Algo(None,
loss=args.loss,
validate_every=validate_every,
mode='easgd',
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer,
elastic_force=args.elastic_force / (comm.Get_size() - 1),
elastic_lr=args.elastic_lr,
elastic_momentum=args.elastic_momentum)
elif args.mode == 'gem':
algo = Algo('gem',
loss=args.loss,
validate_every=validate_every,
mode='gem',
sync_every=args.sync_every,
worker_optimizer=args.worker_optimizer,
learning_rate=args.gem_lr,
momentum=args.gem_momentum,
kappa=args.gem_kappa)
else:
def _execute_MPI(self,
comm=None,
# masters=1,
# easgd=False,
archiveTraining=True,
archiveValidation=True,
verbose=1):
from mpi4py import MPI
from mpi_learn.mpi.manager import MPIManager, get_device
from mpi_learn.train.algo import Algo
from mpi_learn.train.data import H5Data
from mpi_learn.train.model import ModelFromJson
#return prep_func
#print(self.custom_objects)
#print(custom_objects)
#print(Lorentz, Slice)
#raise ValueError()
load_weights = True
# synchronous = False
# sync_every = 1
# MPIoptimizer = "rmsprop"
# batch_size = 100
if(comm == None):
comm = MPI.COMM_WORLD.Dup()
# if(not isinstance(self.train_procedure,list)): self.train_procedure = [self.train_procedure]
# if(not isinstance(self.val_procedure,list)): self.val_procedure = [self.val_procedure]
if(not(isinstance(self.train_procedure,list))):
raise ValueError("Trial attribute train_procedure: expected list of DataProcedures or paths but got type %r" % type(self.train_procedure))
if(not(isinstance(self.val_procedure,list))):
raise ValueError("Trial attribute val_procedure: expected list of DataProcedures or paths but got type %r" % type(self.val_procedure))
train = [DataProcedure.from_json(self.archive_dir,x) if isinstance(x,DataProcedure) else str(x) for x in self.train_procedure]
val = [DataProcedure.from_json(self.archive_dir,x) if isinstance(x,DataProcedure) else str(x) for x in self.val_procedure]
# if(not isinstance(train, list) or not False in [isinstance(x,DataProcedure) or isinstance(x,string_types) for x in train]):
# raise ValueError("Train procedure must be list of DataProcedures")
# if(not isinstance(val, list) or not False in [isinstance(x, DataProcedure) or isinstance(x, string_types) for x in val]):
# raise ValueError("Validation procedure must be list of DataProcedures")
batchAssertArchived(train)
batchAssertArchived(val)
def assertStr(x):
if(isinstance(x,DataProcedure)):
return dp.get_path() + "archive.h5"
elif(os.path.isfile(x)):
return x
else:
raise IOError("Cannot find %r" % x)
train_list = [assertStr(x) for x in train]
val_list = [assertStr(x) for dp in val]
# print("Train List:", train_list)
# print("Val List:", val_list)
# There is an issue when multiple processes import Keras simultaneously --
# the file .keras/keras.json is sometimes not read correctly.
# as a workaround, just try several times to import keras.
# Note: importing keras imports theano --
# impossible to change GPU choice after this.
for try_num in range(10):
try:
from keras.models import model_from_json
import keras.callbacks as cbks
break
except ValueError:
print "Unable to import keras. Trying again: %d" % try_num
sleep(0.1)
custom_objects = {}
for name, module in self.custom_objects.items():
try:
#my_module = importlib.import_module('os.path')
custom_objects[name] = getattr(importlib.import_module(module), name)
#exec("from " + module + " import " + name)
except:
raise ValueError("Custom Object %r does not exist in %r. \
For best results Custom Objects should be importable and not locally defined." % (str(name), str(module)))
# We initialize the Data object with the training data list
# so that we can use it to count the number of training examples
data = H5Data(batch_size=self.batch_size,
features_name=self.features_name, labels_name=self.labels_name)
data.set_file_names(train_list)
num_train = data.count_data()
# if comm.Get_rank() == 0:
validate_every = num_train/self.batch_size
if self.easgd:
# raise NotImplementedError("Not implemented")
algo = Algo(None, loss=self.loss, validate_every=validate_every,
mode='easgd', elastic_lr=1.0, sync_every=self.sync_every,
worker_optimizer='sgd',
elastic_force=0.9/(comm.Get_size()-1))
else:
algo = Algo(self.master_optimizer, loss=self.loss, validate_every=validate_every,
sync_every=self.sync_every, worker_optimizer=self.optimizer)
#model = self.compile(custom_objects=custom_objects)
#model_arch = model.to_json()
#print(self.get_path()+"trial.json")
model_builder = ModelFromJson( comm,json_str=self.model,custom_objects=custom_objects )
callbacks = self._generateCallbacks(verbose=verbose)
# Creating the MPIManager object causes all needed worker and master nodes to be created
manager = MPIManager(comm=comm, data=data, num_epochs=self.epochs if hasattr(self,'epochs') else self.nb_epoch,
algo=algo, model_builder=model_builder,
train_list=train_list, val_list=val_list, num_masters=self.masters,
synchronous=self.synchronous, callbacks=callbacks, custom_objects=custom_objects)
# Process 0 defines the model and propagates it to the workers.
if comm.Get_rank() == 0:
record = self.read_record()
if(not "num_train" in record):
self.to_record({"num_train": num_train})
if(not "num_val" in record):
val_data = H5Data( val_list, batch_size=self.batch_size,
features_name=self.features_name, labels_name=self.labels_name)
self.to_record({"num_val": val_data.count_data()})
print(custom_objects)
print algo
#weights = model.get_weights()
#manager.process.set_model_info( model_arch, algo, weights )
t_0 = time()
histories = manager.process.train()
delta_t = time() - t_0
manager.free_comms()
print "Training finished in %.3f seconds" % delta_t
print(histories)
]
# MPI process 0 coordinates the Bayesian optimization procedure
if block_num == 0:
model_fn = lambda x, y, z: mpi.test_cnn(x, y, np.exp(-z))
opt_coordinator = coordinator.Coordinator(comm_world, num_blocks,
param_ranges, model_fn)
opt_coordinator.run(num_iterations=30)
else:
data = H5Data(batch_size=args.batch,
features_name='Images',
labels_name='Labels')
data.set_file_names(train_list)
validate_every = data.count_data() / args.batch
algo = Algo(args.optimizer,
loss=args.loss,
validate_every=validate_every,
sync_every=args.sync_every)
os.environ['KERAS_BACKEND'] = backend
import_keras()
import keras.callbacks as cbks
callbacks = []
if args.early_stopping is not None:
callbacks.append(
cbks.EarlyStopping(patience=args.early_stopping, verbose=1))
block = process_block.ProcessBlock(comm_world,
comm_block,
algo,
data,
device,
args.epochs,
train_list,