def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
#if args.save_and_break:
# print("save model just after init and exit")
# snapshot.save("initial_snapshot")
# import sys
# sys.exit()
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(metric.metric_cb(step,
epoch=epoch))
#if 1: #step % args.loss_print_every_n_iter == 0:
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
summary = Summary(args.log_dir, args)
if args.do_train:
print('| Training Start')
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
if args.save_last_snapshot:
snapshot.save("last_snapshot")
if args.do_eval:
print('| Evaluation Start')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
if args.do_train or args.do_eval:
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
if args.do_train:
summary = Summary(args.log_dir, args)
for epoch in range(args.num_epochs):
metric = Metric(desc='train',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['total_loss'])
for step in range(epoch_size):
SquadFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
if args.do_eval:
assert os.path.isdir(args.eval_data_dir)
all_results = []
for step in range(num_eval_steps):
unique_ids, start_positions, end_positions = SquadDevJob().get()
unique_ids = unique_ids.numpy()
start_positions = start_positions.numpy()
end_positions = end_positions.numpy()
for unique_id, start_position, end_position in zip(
unique_ids, start_positions, end_positions):
all_results.append(
RawResult(
unique_id=int(unique_id[0]),
start_logits=start_position.flatten().tolist(),
end_logits=end_position.flatten().tolist(),
))
if step % args.loss_print_every_n_iter == 0:
print("{}/{}, num of results:{}".format(
step, num_eval_steps, len(all_results)))
print("last uid:", unique_id[0])
gen_eval_predict_json(args, all_results)
def main():
InitNodes(args)
assert args.model_load_dir, "Must have model load dir!"
flow.env.log_dir(args.log_dir)
# snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
print("Restoring model from {}.".format(args.model_load_dir))
flow.load_variables(flow.checkpoint.get(args.model_load_dir))
metric = Metric(desc="validation",
calculate_batches=num_val_steps,
batch_size=val_batch_size)
for i in range(args.num_epochs):
for j in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(0, j))
def main(self):
processed_list, alphabet, _, emb_dim = pkl.load(
open(self.config['res_path'].format(self.config['dataset']), 'rb'))
if isinstance(processed_list, dict):
processed_list = [processed_list]
scores = []
for data_list in processed_list:
train_data = MyDatasetLoader(self.config, data_list,
'train').get_data()
valid_data = MyDatasetLoader(self.config, data_list,
'valid').get_data()
test_data = MyDatasetLoader(self.config, data_list,
'test').get_data()
self.model = TextCNN(self.config, alphabet, emb_dim,
self.device).to(self.device)
for w in self.model.parameters():
print(w.shape, w.requires_grad)
self.optimizer = Adam(filter(lambda x: x.requires_grad,
self.model.parameters()),
lr=self.config['lr'],
weight_decay=float(self.config['l2']),
eps=float(self.config['esp']))
self.metircs = Metric()
score = self.forward(train_data, valid_data, test_data)
scores.append(score)
print('| valid best | global best|')
print('| --- | --- |')
for w in scores:
print("| {:.4f} | {:.4f} |".format(w[0], w[1]))
if len(scores) > 1:
print("valid Avg\tglobal Avg")
print("| {:.4f} | {:.4f} |".format(np.mean([w[0] for w in scores]),
np.mean([w[1]
for w in scores])))
def train(model, loader, criterion, optimizer, epoch, device, opt):
model.train()
train_loss = 0.0
losses = AverageMeter()
metric = Metric(opt.num_classes)
for i, (imgs, spatial_locations, word_vectors, targets_predicates,
targets_confidences) in enumerate(loader):
# compute outputs
imgs, spatial_locations, word_vectors, targets_confidences, targets_predicates = imgs.to(
device), spatial_locations.to(device), word_vectors.to(
device), targets_confidences.to(device), targets_predicates.to(
device)
confidences, predicates = model(imgs, spatial_locations, word_vectors)
# compute loss
loss1 = criterion(confidences, targets_confidences)
loss2 = criterion(predicates, targets_predicates)
tot_loss = loss1 + loss2
train_loss += tot_loss.item()
losses.update(tot_loss.item(), imgs.size(0))
predicates = torch.sigmoid(predicates)
metric.update(predicates, targets_predicates)
optimizer.zero_grad()
tot_loss.backward()
optimizer.step()
# show information
if (i + 1) % opt.log_interval == 0:
avg_loss = train_loss / opt.log_interval
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, losses.count, len(loader.dataset),
100. * (i + 1) / len(loader), avg_loss))
train_loss = 0.0
# show information
recall = metric.compute_metrics()
print('Train set ({:d} samples): Average loss: {:.4f}\tRecall: {:.4f}'.
format(losses.count, losses.avg, recall))
return losses.avg, recall
def main():
InitNodes(args)
assert args.model_load_dir, 'Must have model load dir!'
flow.env.log_dir(args.log_dir)
summary = Summary(args.log_dir, args)
# snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
print("Restoring model from {}.".format(args.model_load_dir))
checkpoint = flow.train.CheckPoint()
checkpoint.load(args.model_load_dir)
metric = Metric(desc='validation',
calculate_batches=num_val_steps,
summary=summary,
save_summary_steps=num_val_steps,
batch_size=val_batch_size)
for i in range(args.num_epochs):
for j in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(0, j))
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
metric = Metric(desc='train',
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size,
keys=['total_loss', 'mlm_loss', 'nsp_loss'])
for step in range(args.iter_num):
PretrainJob().async_get(metric.metric_cb(step))
#PretrainJob().async_get(metric.metric_cb(step, epoch=3))
if (step + 1) % args.model_save_every_n_iter == 0:
snapshot.save("snapshot_%d" % (step + 1))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
InitNodes(args)
flow.env.log_dir(args.log_dir)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir,
args.save_init)
print(" {} iter per epoch...".format(epoch_size))
for epoch in range(1, args.num_epochs + 1):
metric = Metric(
desc="train",
calculate_batches=args.loss_print_every_n_iter,
batch_size=train_batch_size,
loss_key="loss",
)
for i in range(epoch_size):
TrainNet().async_get(metric.metric_cb(epoch, i))
if args.val_data_dir:
metric = Metric(
desc="validation",
calculate_batches=num_val_steps,
batch_size=val_batch_size,
)
for i in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(epoch, i))
if epoch % args.save_epoch_interval == 0:
snapshot.save("epoch_{}".format(epoch))
if args.save_last:
snapshot.save("epoch_{}".format("last"))
def main():
InitNodes(args)
flow.env.grpc_use_no_signal()
flow.env.log_dir(args.log_dir)
summary = Summary(args.log_dir, args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
for epoch in range(args.num_epochs):
metric = Metric(desc='train',
calculate_batches=args.loss_print_every_n_iter,
summary=summary,
save_summary_steps=epoch_size,
batch_size=train_batch_size,
loss_key='loss')
for i in range(epoch_size):
TrainNet().async_get(metric.metric_cb(epoch, i))
if args.val_data_dir:
metric = Metric(desc='validation',
calculate_batches=num_val_steps,
summary=summary,
save_summary_steps=num_val_steps,
batch_size=val_batch_size)
for i in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(epoch, i))
snapshot.save('epoch_{}'.format(epoch))
def validate(model, loader, criterion, epoch, device, opt):
model.eval()
losses = AverageMeter()
metric = Metric(opt.num_classes)
with torch.no_grad():
for i, (imgs, spatial_locations, word_vectors, targets_predicates, targets_confidences) in enumerate(loader):
# compute outputs
imgs, spatial_locations, word_vectors, targets_confidences, targets_predicates = imgs.to(device), spatial_locations.to(
device), word_vectors.to(device), targets_confidences.to(device), targets_predicates.to(device)
confidences, predicates = model(imgs, spatial_locations, word_vectors)
# compute loss
loss = criterion(predicates, targets_predicates)
metric.update(predicates, targets_predicates)
losses.update(loss.item(), imgs.size(0))
# show information
recall = metric.compute_metrics()
print('Validation set ({:d} samples): Average loss: {:.4f}\tRecall: {:.4f}'.format(losses.count, losses.avg, recall))
return losses.avg, recall
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
snapshot = Snapshot(args.model_save_dir, args.model_load_dir, args.model_save_init)
print("num_accumulation_steps:", args.num_accumulation_steps)
metric = Metric(
desc="train",
print_steps=args.loss_print_every_n_iter,
batch_size=batch_size * args.num_accumulation_steps,
keys=["total_loss", "mlm_loss", "nsp_loss"],
)
for step in range(args.iter_num):
PretrainJob().async_get(metric.metric_cb(step))
# PretrainJob().async_get(metric.metric_cb(step, epoch=3))
if (step + 1) % args.model_save_every_n_iter == 0:
snapshot.save("snapshot_%d" % (step + 1))
if args.save_last_snapshot:
snapshot.save("last_snapshot")
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
if args.do_train:
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
summary = Summary(args.log_dir, args)
best_dev_acc = 0.0
best_result = {}
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
BertGlueFinetuneJob().async_get(
metric.metric_cb(step, epoch=epoch))
#if 1: #step % args.loss_print_every_n_iter == 0:
run_eval_job(BertGlueEvalTrainJob, epoch_size, desc='train')
result = run_eval_job(BertGlueEvalValJob,
num_eval_steps,
desc='eval')
save_model = False
if task_name in acc_tasks and result['accuracy'] > best_dev_acc:
best_dev_acc = result['accuracy']
best_result = result
save_model = True
print('Best result:', result)
# if task_name in corr_tasks and result['corr'] > best_dev_acc:
# best_dev_acc = result['corr']
# best_result = result
# save_model = True
#print('Best result:', result)
if task_name in mcc_tasks and result[
'matthews_corrcoef'] > best_dev_acc:
best_dev_acc = result['matthews_corrcoef']
best_result = result
save_model = True
print('Best result:', result)
if save_model:
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
# snapshot_save_path = os.path.join(args.model_save_dir)
# print("Saving best model to {}".format(snapshot_save_path))
snapshot.save('best')
flow.sync_default_session()
print('Best result:', best_result)
print("Saving best model to " +
os.path.join(args.model_save_dir, 'snapshot_best'))
if args.serve_for_online:
print('Deleting the optimizer parmas from model_save_dir...')
remove_optimizer_params(
os.path.join(args.model_save_dir, 'snapshot_best'))
# if args.save_last_snapshot:
# snapshot.save("last_snapshot")
if args.do_eval:
print('Loading model...')
print(args.model_save_dir)
if not args.do_train:
check_point = flow.train.CheckPoint()
check_point.load(args.model_save_dir)
print('Evaluation...')
run_eval_job(BertGlueEvalValJob, num_eval_steps, desc='eval')
parser = configs.get_parser()
args = parser.parse_args()
configs.print_args(args)
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.config.enable_debug_mode(True)
@flow.global_function(get_val_config(args))
def IOTest():
if args.train_data_dir:
assert os.path.exists(args.train_data_dir)
print("Loading data from {}".format(args.train_data_dir))
(labels, images) = load_imagenet_for_training(args)
else:
print("Loading synthetic data.")
(labels, images) = load_synthetic(args)
outputs = {"images": images, "labels": labels}
return outputs
total_device_num = args.num_nodes * args.gpu_num_per_node
train_batch_size = total_device_num * args.batch_size_per_device
summary = Summary(args.log_dir, args, filename='io_test.csv')
metric = Metric(desc='io_test',
calculate_batches=args.loss_print_every_n_iter,
summary=summary,
save_summary_steps=args.loss_print_every_n_iter,
batch_size=train_batch_size,
prediction_key=None)
for i in range(1000):
IOTest().async_get(metric.metric_cb(0, i))
from job_function_util import get_val_config
parser = configs.get_parser()
args = parser.parse_args()
configs.print_args(args)
flow.config.gpu_device_num(args.gpu_num_per_node)
# flow.config.enable_debug_mode(True)
@flow.global_function(get_val_config(args))
def IOTest():
if args.train_data_dir:
assert os.path.exists(args.train_data_dir)
print("Loading data from {}".format(args.train_data_dir))
(labels, images) = load_imagenet_for_training(args)
else:
print("Loading synthetic data.")
(labels, images) = load_synthetic(args)
outputs = {"images": images, "labels": labels}
return outputs
total_device_num = args.num_nodes * args.gpu_num_per_node
train_batch_size = total_device_num * args.batch_size_per_device
metric = Metric(
desc="io_test",
calculate_batches=args.loss_print_every_n_iter,
batch_size=train_batch_size,
prediction_key=None,
)
for i in range(1000):
IOTest().async_get(metric.metric_cb(0, i))
tmp = os.path.getsize(os.path.join(root, name))
size += tmp
# size += sum([os.path.getsize(os.path.join(root, name)) for name in files])
return size
def main():
InitNodes(args)
flow.env.log_dir(args.log_dir)
modelSize = getdirsize(args.model_load_dir)
summary = Summary(args.log_dir, args, modelSize)
>>>>>>> tianshu
snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
for epoch in range(args.num_epochs):
metric = Metric(desc='train', calculate_batches=args.loss_print_every_n_iter,
summary=summary, save_summary_steps=epoch_size,
batch_size=train_batch_size, loss_key='loss')
for i in range(epoch_size):
TrainNet().async_get(metric.metric_cb(epoch, i))
<<<<<<< HEAD
# flow.tensorrt.write_int8_calibration("./int8_calibration") # mkdir int8_calibration
=======
# flow.tensorrt.write_int8_calibration("./int8_calibration") # mkdir int8_calibration
>>>>>>> tianshu
if args.val_data_dir:
metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
save_summary_steps=num_val_steps, batch_size=val_batch_size)
for i in range(val_batch_size):
<<<<<<< HEAD
# if i<=10:
def main():
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
check_point = flow.train.CheckPoint()
summary = Summary(args.log_dir, args)
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
if args.do_train:
print('Combining two models into one dir')
if not os.path.exists('./tmp'):
os.makedirs('./tmp')
args.total_model = tempfile.mkdtemp(dir='./tmp')
CopyFile(args.student_model, args.total_model)
CopyFile(args.teacher_model, args.total_model)
print('Loading model...')
check_point.load(args.total_model)
# # check_point.load(args.teacher_model)
# # check_point.load(args.student_model)
#
print('Start training...')
global_step = 0
best_dev_acc = 0.0
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune', print_steps=args.loss_print_every_n_iter, summary=summary,
batch_size=batch_size, keys=['loss'])
for step in range(epoch_size):
DistilJob().async_get(metric.metric_cb(step, epoch=epoch))
global_step += 1
# if (global_step + 1) % args.model_save_every_n_iter == 0:
# if not os.path.exists(args.model_save_dir):
# os.makedirs(args.model_save_dir)
# snapshot_save_path = os.path.join(
# args.model_save_dir, "snapshot_%d" % (global_step + 1)
# )
# print("Saving model to {}.".format(snapshot_save_path))
# check_point.save(snapshot_save_path)
# if args.pred_distill:
print('EvalTrainJob...')
run_eval_job(StudentBertGlueEvalTrainJob, epoch_size, desc='train')
print('EvalValJob...')
result = run_eval_job(StudentBertGlueEvalValJob, num_eval_steps, desc='eval')
if not args.pred_distill:
save_model = True
else:
save_model = False
if task_name in acc_tasks and result['accuracy'] > best_dev_acc:
best_dev_acc = result['accuracy']
save_model = True
# if task_name in corr_tasks and result['corr'] > best_dev_acc:
# best_dev_acc = result['corr']
# save_model = True
if task_name in mcc_tasks and result['matthews_corrcoef'] > best_dev_acc:
best_dev_acc = result['matthews_corrcoef']
save_model = True
print('Best result:', result)
if save_model:
if os.path.exists(args.model_save_dir):
import shutil
shutil.rmtree(args.model_save_dir)
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
snapshot_save_path = os.path.join(args.model_save_dir)
print("Saving best model to {}".format(snapshot_save_path))
check_point.save(snapshot_save_path)
flow.sync_default_session()
if args.save_last_snapshot:
snapshot_save_path = args.model_save_dir
if os.path.exists(args.model_save_dir):
import shutil
shutil.rmtree(args.model_save_dir)
print("Saving model to {}".format(snapshot_save_path))
check_point.save(snapshot_save_path)
flow.sync_default_session()
if global_step >= 100:
# remove tmp total models
print('Removing the tmp models...')
import shutil
shutil.rmtree(args.total_model)
if args.serve_for_online:
print('Deleting the teacher params and the optimizer parmas from model_save_dir...')
remove_teacher_params(args.model_save_dir)
if args.do_eval:
print('Loading model...')
print(args.model_save_dir)
if not args.do_train:
check_point.load(args.model_save_dir)
print('Evaluation...')
run_eval_job(StudentBertGlueEvalValJob, num_eval_steps, desc='eval')
def main():
flow.config.enable_debug_mode(True)
flow.config.gpu_device_num(args.gpu_num_per_node)
flow.env.log_dir(args.log_dir)
InitNodes(args)
check_point = flow.train.CheckPoint()
check_point.init()
summary = Summary(args.log_dir, args)
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
if args.do_train:
print('Start training...')
global_step = 0
best_dev_acc = 0.0
print('epoch_size:', epoch_size)
print('args.iter_num:', args.iter_num)
for epoch in range(args.num_epochs):
metric = Metric(desc='finetune',
print_steps=args.loss_print_every_n_iter,
summary=summary,
batch_size=batch_size,
keys=['loss'])
for step in range(epoch_size):
loss = DistilJob().get()
if step % 10 == 0:
print('step/epoch_size:{}/{} epoch:{}'.format(
step, epoch_size, epoch))
print('loss:', loss['loss'].mean())
# global_step+=1
# DistilJob().async_get(metric.metric_cb(step, epoch=epoch))
print('EvalTrainJob...')
run_eval_job(StudentBertGlueEvalTrainJob, epoch_size, desc='train')
print('EvalValJob...')
result = run_eval_job(StudentBertGlueEvalValJob,
num_eval_steps,
desc='eval')
save_model = False
if task_name in acc_tasks and result['accuracy'] > best_dev_acc:
best_dev_acc = result['accuracy']
save_model = True
# if task_name in corr_tasks and result['corr'] > best_dev_acc:
# best_dev_acc = result['corr']
# save_model = True
if task_name in mcc_tasks and result[
'matthews_corrcoef'] > best_dev_acc:
best_dev_acc = result['matthews_corrcoef']
save_model = True
print('Best result:', result)
if save_model:
if not os.path.exists(args.model_save_dir):
os.makedirs(args.model_save_dir)
snapshot_save_path = os.path.join(args.model_save_dir)
print("Saving best model to {}".format(snapshot_save_path))
check_point.save(snapshot_save_path)
if args.save_last_snapshot:
snapshot_save_path = args.model_save_dir
print("Saving model to {}".format(snapshot_save_path))
check_point.save(snapshot_save_path)
if args.do_eval:
print('Loading model...')
print(args.model_save_dir)
if not args.do_train:
check_point.load(args.model_save_dir)
print('Evaluation...')
run_eval_job(StudentBertGlueEvalValJob, num_eval_steps, desc='eval')
summary = Summary(args.log_dir, args, modelSize)
# snapshot = Snapshot(args.model_save_dir, args.model_load_dir)
print("Restoring model from {}.".format(args.model_load_dir))
checkpoint = flow.train.CheckPoint()
checkpoint.load(args.model_load_dir)
if args.use_int8_online:
for j in range(10):
<<<<<<< HEAD
flow.tensorrt.cache_int8_calibration()
=======
InferenceNet().get()
flow.tensorrt.cache_int8_calibration()
flow.tensorrt.write_int8_calibration("./int8_calibration")
>>>>>>> tianshu
warmup = 2
for j in range(warmup):
InferenceNet().get()
metric = Metric(desc='validation', calculate_batches=num_val_steps, summary=summary,
save_summary_steps=num_val_steps, batch_size=val_batch_size)
for i in range(args.num_epochs):
for j in range(num_val_steps):
InferenceNet().async_get(metric.metric_cb(0, j))
if __name__ == "__main__":
main()
def compute_scores(self, gt, preds):
ret = {'ndcg': Metric.ndcg(gt, preds), 'auc': Metric.auc(gt, preds)}
return ret
import os
import config as configs
from util import Summary, InitNodes, Metric
from job_function_util import get_val_config
parser = configs.get_parser()
args = parser.parse_args()
configs.print_args(args)
flow.config.gpu_device_num(args.gpu_num_per_node)
#flow.config.enable_debug_mode(True)
@flow.global_function(get_val_config(args))
def IOTest():
if args.train_data_dir:
assert os.path.exists(args.train_data_dir)
print("Loading data from {}".format(args.train_data_dir))
(labels, images) = load_imagenet_for_training(args)
else:
print("Loading synthetic data.")
(labels, images) = load_synthetic(args)
outputs = {"images": images, "labels": labels}
return outputs
total_device_num = args.num_nodes * args.gpu_num_per_node
train_batch_size = total_device_num * args.batch_size_per_device
summary = Summary(args.log_dir, args, filename='io_test.csv')
metric = Metric(desc='io_test', calculate_batches=args.loss_print_every_n_iter,
summary=summary, save_summary_steps=args.loss_print_every_n_iter,
batch_size=train_batch_size, prediction_key=None)
for i in range(1000):
IOTest().async_get(metric.metric_cb(0, i))
