def run4(self):
set_rng_seed(100)
data_set, model = prepare_env()
train_set, dev_set = data_set.split(0.3)
model = NaiveClassifier(2, 1)
trainer = DistTrainer(
train_set,
model,
optimizer=SGD(lr=0.1),
loss=BCELoss(pred="predict", target="y"),
batch_size_per_gpu=32,
n_epochs=3,
print_every=50,
dev_data=dev_set,
metrics=AccuracyMetric(pred="predict", target="y"),
validate_every=-1,
save_path=self.save_path,
)
trainer.train()
"""
# 应该正确运行
"""
if trainer.is_master and os.path.exists(self.save_path):
shutil.rmtree(self.save_path)
def run1(self):
# test distributed training
print('local rank', get_local_rank())
set_rng_seed(100)
data_set = prepare_fake_dataset()
data_set.set_input("x", flag=True)
data_set.set_target("y", flag=True)
model = NaiveClassifier(2, 2)
trainer = DistTrainer(
model=model,
train_data=data_set,
optimizer=SGD(lr=0.1),
loss=CrossEntropyLoss(pred="predict", target="y"),
batch_size_per_gpu=8,
n_epochs=3,
print_every=50,
save_path=self.save_path,
)
trainer.train()
"""
# 应该正确运行
"""
if trainer.is_master and os.path.exists(self.save_path):
shutil.rmtree(self.save_path)
def run3(self):
set_rng_seed(100)
data_set, model = prepare_env()
trainer = DistTrainer(
data_set,
model,
optimizer=None,
loss=BCELoss(pred="predict", target="y"),
n_epochs=3,
print_every=50,
callbacks_all=[EchoCallback('callbacks_all')],
callbacks_master=[EchoCallback('callbacks_master')])
trainer.train()