def main(_):
with tf.Graph().as_default() as single_gpu_graph:
model_config = configuration.model_config(
input_file_pattern=FLAGS.input_file_pattern,
batch_size=FLAGS.batch_size)
training_config = configuration.training_config()
model = skip_thoughts_model.SkipThoughtsModel(model_config,
mode="train")
model.build()
# Setup learning rate
if training_config.learning_rate_decay_factor > 0:
learning_rate = tf.train.exponential_decay(
learning_rate=float(training_config.learning_rate),
global_step=model.global_step,
decay_steps=training_config.learning_rate_decay_steps,
decay_rate=training_config.learning_rate_decay_factor,
staircase=False)
else:
learning_rate = tf.constant(training_config.learning_rate)
optimizer = tf.train.AdamOptimizer(learning_rate)
train_tensor = tf.contrib.slim.learning.create_train_op(
total_loss=model.total_loss,
optimizer=optimizer,
global_step=model.global_step,
clip_gradient_norm=training_config.clip_gradient_norm)
def run(sess, num_iters, tensor_or_op_name_to_replica_names, num_workers,
worker_id, num_replicas_per_worker):
fetches = {
'global_step':
tensor_or_op_name_to_replica_names[model.global_step.name][0],
'cost':
tensor_or_op_name_to_replica_names[model.total_loss.name][0],
'train_op':
tensor_or_op_name_to_replica_names[train_tensor.name][0],
}
start = time.time()
for i in range(num_iters):
results = sess.run(fetches)
if i % FLAGS.log_frequency == 0:
end = time.time()
throughput = float(FLAGS.log_frequency) / float(end - start)
parallax.log.info(
"global step: %d, loss: %f, throughput: %f steps/sec" %
(results['global_step'], results['cost'], throughput))
start = time.time()
parallax.parallel_run(single_gpu_graph,
run,
FLAGS.resource_info_file,
FLAGS.max_steps,
sync=FLAGS.sync,
parallax_config=parallax_config.build_config())
def main(_):
# Build benchmark_cnn model
params = benchmark_cnn.make_params_from_flags()
params, sess_config = benchmark_cnn.setup(params)
bench = benchmark_cnn.BenchmarkCNN(params)
# Print informaton
tfversion = cnn_util.tensorflow_version_tuple()
log_fn('TensorFlow: %i.%i' % (tfversion[0], tfversion[1]))
bench.print_info()
# Build single-GPU benchmark_cnn model
with tf.Graph().as_default() as single_gpu_graph:
bench.build_model()
def run(sess, num_iters, tensor_or_op_name_to_replica_names, num_workers,
worker_id, num_replicas_per_worker):
fetches = {
'global_step':
tensor_or_op_name_to_replica_names[bench.global_step.name][0],
'cost':
tensor_or_op_name_to_replica_names[bench.cost.name][0],
'train_op':
tensor_or_op_name_to_replica_names[bench.train_op.name][0],
}
if isinstance(bench.lr, tf.Tensor):
fetches['lr'] = tensor_or_op_name_to_replica_names[
bench.lr.name][0]
start = time.time()
for i in range(num_iters):
results = sess.run(fetches)
if i % FLAGS.log_frequency == 0:
end = time.time()
throughput = float(FLAGS.log_frequency) / float(end - start)
parallax.log.info(
"global step: %d, lr: %f, loss: %f, "
"throughput: %f steps/sec" %
(results['global_step'], results['lr'] if 'lr' in results
else bench.lr, results['cost'], throughput))
start = time.time()
config = parallax_config.build_config()
config.sess_config = sess_config
parallax.parallel_run(single_gpu_graph,
run,
FLAGS.resource_info_file,
FLAGS.max_steps,
sync=FLAGS.sync,
parallax_config=config)
def main(_):
with tf.Graph().as_default() as single_gpu_graph:
global_step = tf.train.get_or_create_global_step()
x = tf.placeholder(tf.float32, shape=(2))
y = tf.placeholder(tf.float32, shape=(1))
w = tf.get_variable(name='w', shape=(2, 1))
b = tf.get_variable(name='b', shape=(1))
pred = tf.nn.bias_add(tf.matmul(tf.expand_dims(x, axis=0), w), b)
loss = tf.reduce_sum(tf.pow(pred - tf.expand_dims(y, axis=0), 2)) / 2
optimizer = tf.train.GradientDescentOptimizer(args.learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
# init = tf.global_variables_initializer()
def run(sess, num_iters, op_name_to_replica_op_names, num_workers,
worker_id, num_replicas_per_worker):
cursor = 0
for i in range(num_iters):
feed_dict = {}
for replica in range(num_replicas_per_worker):
feed_dict[op_name_to_replica_op_names[x.name][replica]] = \
train_x[cursor % num_samples]
feed_dict[op_name_to_replica_op_names[y.name][replica]] = \
train_y[cursor % num_samples]
cursor += 1
fetches = {
'global_step':
op_name_to_replica_op_names[global_step.name][0],
'loss': loss.name,
'train_op': train_op.name
}
results = sess.run(fetches, feed_dict=feed_dict)
if i % 5 == 0:
print("global step: %d, loss: %f" %
(results['global_step'], results['loss']))
# with tf.Session() as sess:
# sess.run(init)
# _run(sess, 100, {x: [x], y: [y], loss: loss, train_op: train_op}, 1)
resource_info = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resource_info')
parallax.parallel_run(single_gpu_graph, run, resource_info, 1000)
def train(hps):
"""Training loop."""
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
images, labels = cifar_input.build_input('cifar10',
FLAGS.train_data_path,
hps.batch_size, 'train')
model = resnet_model.ResNet(hps, images, labels, 'train')
model.build_graph()
truth = tf.argmax(model.labels, axis=1)
predictions = tf.argmax(model.predictions, axis=1)
precision = tf.reduce_mean(tf.to_float(tf.equal(predictions, truth)))
########################################################################
#### FIXME: Get session for distributed environments using Parallax ####
#### Pass parallax_config as an argument ####
########################################################################
parallax_sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph,
FLAGS.resource_info_file,
sync=FLAGS.sync,
parallax_config=parallax_config.build_config())
for i in range(350000):
_, global_step, cost, precision_ = \
parallax_sess.run([model.train_op, model.global_step, model.cost, precision])
if i % 10 == 0:
print('step: %d, loss: %.3f, precision: %.3f' %
(global_step[0], cost[0], precision_[0]))
# Tuning learning rate
train_step = global_step[0]
if train_step < 10000:
lrn_rate = 0.1
elif train_step < 15000:
lrn_rate = 0.01
elif train_step < 20000:
lrn_rate = 0.001
else:
lrn_rate = 0.0001
feed_dict = {model.lrn_rate: []}
for worker in range(num_replicas_per_worker):
feed_dict[model.lrn_rate].append(lrn_rate)
parallax_sess.run(model.global_step, feed_dict=feed_dict)
def main(_):
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
global_step = tf.train.get_or_create_global_step()
x = tf.placeholder(tf.float32, shape=(2))
y = tf.placeholder(tf.float32, shape=(1))
w = tf.get_variable(name='w', shape=(2, 1))
b = tf.get_variable(name='b', shape=(1))
pred = tf.nn.bias_add(tf.matmul(tf.expand_dims(x, axis=0), w), b)
loss = tf.reduce_sum(tf.pow(pred - tf.expand_dims(y, axis=0), 2)) / 2
optimizer = tf.train.GradientDescentOptimizer(args.learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
# init = tf.global_variables_initializer()
def run(sess, num_workers, worker_id, num_replicas_per_worker):
cursor = 0
for i in range(1000):
feed_dict = {}
feed_dict[x] = [train_x[(cursor + j) % num_samples] for j in \
range(num_replicas_per_worker)]
feed_dict[y] = [train_y[(cursor + j) % num_samples] for j in \
range(num_replicas_per_worker)]
cursor += num_replicas_per_worker
fetches = {
'global_step': global_step,
'loss': loss,
'train_op': train_op
}
results = sess.run(fetches, feed_dict=feed_dict)
if i % 5 == 0:
print("global step: %d, loss: %f" %
(results['global_step'][0], results['loss'][0]))
resource_info = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'resource_info')
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph, resource_info)
run(sess, num_workers, worker_id, num_replicas_per_worker)
def main(_):
# Build benchmark_cnn model
params = benchmark_cnn.make_params_from_flags()
params, sess_config = benchmark_cnn.setup(params)
bench = benchmark_cnn.BenchmarkCNN(params)
# Print informaton
tfversion = cnn_util.tensorflow_version_tuple()
log_fn('TensorFlow: %i.%i' % (tfversion[0], tfversion[1]))
bench.print_info()
# Build single-GPU benchmark_cnn model
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
bench.build_model()
config = parallax_config.build_config()
config.sess_config = sess_config
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph,
FLAGS.resource_info_file,
sync=FLAGS.sync,
parallax_config=config)
fetches = {
'global_step': bench.global_step,
'cost': bench.cost,
'train_op': bench.train_op,
}
start = time.time()
for i in range(FLAGS.max_steps):
results = sess.run(fetches)
if (i + 1) % FLAGS.log_frequency == 0:
end = time.time()
throughput = float(FLAGS.log_frequency) / float(end - start)
parallax.log.info(
"global step: %d, loss: %f, throughput: %f steps/sec" %
(results['global_step'][0] + 1, results['cost'][0],
throughput))
start = time.time()
ops = rnn()
train_op = ops['train_op']
loss = ops['loss']
acc = ops['acc']
x = ops['images']
y = ops['labels']
is_training = ops['is_training']
parallax_config = parallax.Config()
ckpt_config = parallax.CheckPointConfig(ckpt_dir='parallax_ckpt',
save_ckpt_steps=1)
parallax_config.ckpt_config = ckpt_config
sess, num_workers, worker_id, num_replicas_per_worker = parallax.parallel_run(
single_gpu_graph,
FLAGS.resource_info_file,
sync=FLAGS.sync,
parallax_config=parallax_config)
start = time.time()
for i in range(FLAGS.max_steps):
batch = mnist.train.next_batch(FLAGS.batch_size, shuffle=False)
_, loss_ = sess.run([train_op, loss], feed_dict={x: [batch[0]],
y: [batch[1]],
is_training: [True]})
if i % FLAGS.log_frequency == 0:
end = time.time()
throughput = float(FLAGS.log_frequency) / float(end - start)
acc_ = sess.run(acc, feed_dict={x: [mnist.test.images],
y: [mnist.test.labels],
is_training: [False]})[0]
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
processors = {
"cola": ColaProcessor,
"mnli": MnliProcessor,
"mrpc": MrpcProcessor,
"xnli": XnliProcessor,
}
tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case,
FLAGS.init_checkpoint)
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval` or `do_predict' must be True."
)
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
if FLAGS.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
"Cannot use sequence length %d because the BERT model "
"was only trained up to sequence length %d" %
(FLAGS.max_seq_length, bert_config.max_position_embeddings))
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels()
tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file,
do_lower_case=FLAGS.do_lower_case)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if FLAGS.do_train:
train_examples = processor.get_train_examples(FLAGS.data_dir)
num_train_steps = int(
len(train_examples) / FLAGS.train_batch_size *
FLAGS.num_train_epochs)
num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
"""
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
"""
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
"""
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
"""
train_file = os.path.join(FLAGS.output_dir, "train.tf_record")
file_based_convert_examples_to_features(train_examples, label_list,
FLAGS.max_seq_length, tokenizer,
train_file)
tf.logging.info("***** Running training *****")
tf.logging.info(" Num examples = %d", len(train_examples))
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
tf.logging.info(" Num steps = %d", num_train_steps)
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
dataset = train_input_fn(FLAGS.train_batch_size)
features = dataset.make_one_shot_iterator().get_next()
model_fn = model_fn_builder(bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
total_loss = model_fn(features)
train_op, _ = optimization.create_optimizer(total_loss,
FLAGS.learning_rate,
num_train_steps,
num_warmup_steps,
FLAGS.use_tpu)
def run(sess, num_workers, worker_id, num_replicas_per_worker):
print('num_workers: {} | worker_id: {}'.format(num_workers, worker_id))
sys.stdout.flush()
dataset = train_input_fn(
FLAGS.train_batch_size * num_replicas_per_worker, num_workers,
worker_id)
features = dataset.make_one_shot_iterator().get_next()
total_loss = model_fn(features)
train_op, global_step = optimization.create_optimizer(
total_loss, FLAGS.learning_rate, num_train_steps, num_warmup_steps,
FLAGS.use_tpu)
_global_step = 0
for i in range(num_train_steps):
print("[before session run - worker:{} | global step:{}]".format(
worker_id, _global_step))
sys.stdout.flush()
loss, _global_step, _ = sess.run(
[total_loss, global_step, train_op])
print("[after session run - worker:{} | global step:{} - loss:{}]".
format(worker_id, _global_step, loss))
sys.stdout.flush()
if i % 100 == 0:
print('[worker:{} | global step:{} - loss:{}]'.format(
worker_id, _global_step, loss))
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph,
FLAGS.resource_info_file,
sync=True,
parallax_config=parallax_config.build_config())
run(sess, num_workers, worker_id, num_replicas_per_worker)
def main(_):
default_hparams = nmt.create_hparams(FLAGS)
## Train / Decode
out_dir = FLAGS.out_dir
if not tf.gfile.Exists(out_dir): tf.gfile.MakeDirs(out_dir)
# Load hparams.
hparams = nmt.create_or_load_hparams(out_dir,
default_hparams,
FLAGS.hparams_path,
save_hparams=False)
log_device_placement = hparams.log_device_placement
out_dir = hparams.out_dir
num_train_steps = hparams.num_train_steps
steps_per_stats = hparams.steps_per_stats
avg_ckpts = hparams.avg_ckpts
if not hparams.attention:
model_creator = nmt_model.Model
else: # Attention
if (hparams.encoder_type == "gnmt"
or hparams.attention_architecture in ["gnmt", "gnmt_v2"]):
model_creator = gnmt_model.GNMTModel
elif hparams.attention_architecture == "standard":
model_creator = attention_model.AttentionModel
else:
raise ValueError("Unknown attention architecture %s" %
hparams.attention_architecture)
train_model =\
model_helper.create_train_model(model_creator, hparams, scope=None)
config_proto = utils.get_config_proto(
log_device_placement=log_device_placement,
num_intra_threads=1,
num_inter_threads=36)
def run(train_sess, num_workers, worker_id, num_replicas_per_worker):
# Random
random_seed = FLAGS.random_seed
if random_seed is not None and random_seed > 0:
utils.print_out("# Set random seed to %d" % random_seed)
random.seed(random_seed + worker_id)
np.random.seed(random_seed + worker_id)
# Log and output files
log_file = os.path.join(out_dir, "log_%d" % time.time())
log_f = tf.gfile.GFile(log_file, mode="a")
utils.print_out("# log_file=%s" % log_file, log_f)
global_step = train_sess.run(train_model.model.global_step)[0]
last_stats_step = global_step
# This is the training loop.
stats, info, start_train_time = before_train(train_model, train_sess,
global_step, hparams,
log_f,
num_replicas_per_worker)
epoch_steps = FLAGS.epoch_size / (FLAGS.batch_size * num_workers *
num_replicas_per_worker)
for i in range(FLAGS.max_steps):
### Run a step ###
start_time = time.time()
if hparams.epoch_step != 0 and hparams.epoch_step % epoch_steps == 0:
hparams.epoch_step = 0
skip_count = train_model.skip_count_placeholder
feed_dict = {}
feed_dict[skip_count] = [
0 for i in range(num_replicas_per_worker)
]
init = train_model.iterator.initializer
train_sess.run(init, feed_dict=feed_dict)
if worker_id == 0:
results = train_sess.run([
train_model.model.update, train_model.model.train_loss,
train_model.model.predict_count,
train_model.model.train_summary,
train_model.model.global_step,
train_model.model.word_count, train_model.model.batch_size,
train_model.model.grad_norm,
train_model.model.learning_rate
])
step_result = [r[0] for r in results]
else:
global_step, _ = train_sess.run(
[train_model.model.global_step, train_model.model.update])
hparams.epoch_step += 1
if worker_id == 0:
# Process step_result, accumulate stats, and write summary
global_step, info["learning_rate"], step_summary = \
train.update_stats(stats, start_time, step_result)
# Once in a while, we print statistics.
if global_step - last_stats_step >= steps_per_stats:
last_stats_step = global_step
is_overflow = train.process_stats(stats, info, global_step,
steps_per_stats, log_f)
train.print_step_info(" ", global_step, info,
train._get_best_results(hparams),
log_f)
if is_overflow:
break
# Reset statistics
stats = train.init_stats()
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(train_model.graph,
FLAGS.resource_info_file,
sync=FLAGS.sync,
parallax_config=parallax_config.build_config())
run(sess, num_workers, worker_id, num_replicas_per_worker)
train_datsets = parallax.shard.shard(train_datasets) # sharding 추가해줘야함.
iterator = train_datasets.make_one_shot_iterator()
inputs, labels = iterator.get_next()
single_worker_model = build_and_compile_cnn_model(forward_only=True)
logits = single_worker_model(inputs, training=True)
accuracy = tf.metrics.accuracy(
labels=labels, predictions=tf.argmax(logits, axis=1))[1]
loss = tf.keras.losses.sparse_categorical_crossentropy(labels, logits)
optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.001)
step = tf.train.get_or_create_global_step()
train_op = optimizer.minimize(loss, step)
loss = tf.reduce_mean(loss)
parallax_config = parallax.Config()
parallax_config.run_option = FLAGS.run_option
parallax_config.ckpt_config = parallax.CheckPointConfig(ckpt_dir=FLAGS.ckpt_dir,
save_ckpt_steps=FLAGS.save_ckpt_steps)
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph,
FLAGS.resource_info_file,
parallax_config=parallax_config)
for i in range(NUM_EPOCHS * STEPS_PER_EPOCH / NUM_WORKERS):
step_, loss_, accuracy_, _ = sess.run([step, loss, accuracy, train_op])
if i % 10 == 0:
print('step:%d, loss: %2f, accuracy: %2f' % (step_[0], loss_[0], accuracy_[0]))
print('작업이 깔끔하게 끝났습니다.')
def main(_):
vocab = Vocabulary.from_file(
os.path.join(FLAGS.datadir, "1b_word_vocab.txt"))
dataset = Dataset(
vocab,
os.path.join(FLAGS.datadir,
"training-monolingual.tokenized.shuffled/*"))
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
with tf.variable_scope("model"):
model = language_model_graph.build_model()
def run(sess, num_workers, worker_id, num_replicas_per_worker):
state_c = []
state_h = []
if len(state_c) == 0:
state_c.extend([
np.zeros([FLAGS.batch_size, model.state_size],
dtype=np.float32)
for _ in range(num_replicas_per_worker)
])
state_h.extend([
np.zeros([FLAGS.batch_size, model.projected_size],
dtype=np.float32)
for _ in range(num_replicas_per_worker)
])
prev_global_step = sess.run(model.global_step)[0]
prev_time = time.time()
data_iterator = dataset.iterate_forever(
FLAGS.batch_size * num_replicas_per_worker, FLAGS.num_steps,
num_workers, worker_id)
fetches = {
'global_step': model.global_step,
'loss': model.loss,
'train_op': model.train_op,
'final_state_c': model.final_state_c,
'final_state_h': model.final_state_h
}
for local_step in range(FLAGS.max_steps):
if FLAGS.use_synthetic:
x = np.random.randint(
low=0,
high=model.vocab_size,
size=(FLAGS.batch_size * num_replicas_per_worker,
FLAGS.num_steps))
y = np.random.randint(
low=0,
high=model.vocab_size,
size=(FLAGS.batch_size * num_replicas_per_worker,
FLAGS.num_steps))
w = np.ones((FLAGS.batch_size * num_replicas_per_worker,
FLAGS.num_steps))
else:
x, y, w = next(data_iterator)
feeds = {}
feeds[model.x] = np.split(x, num_replicas_per_worker)
feeds[model.y] = np.split(y, num_replicas_per_worker)
feeds[model.w] = np.split(w, num_replicas_per_worker)
feeds[model.initial_state_c] = state_c
feeds[model.initial_state_h] = state_h
fetched = sess.run(fetches, feeds)
state_c = fetched['final_state_c']
state_h = fetched['final_state_h']
if local_step % FLAGS.log_frequency == 0:
cur_time = time.time()
elapsed_time = cur_time - prev_time
num_words = FLAGS.batch_size * FLAGS.num_steps
wps = (fetched['global_step'][0] -
prev_global_step) * num_words / elapsed_time
prev_global_step = fetched['global_step'][0]
parallax.log.info(
"Iteration %d, time = %.2fs, wps = %.0f, train loss = %.4f"
% (fetched['global_step'][0], cur_time - prev_time, wps,
fetched['loss'][0]))
prev_time = cur_time
sess, num_workers, worker_id, num_replicas_per_worker = \
parallax.parallel_run(single_gpu_graph,
FLAGS.resource_info_file,
sync=FLAGS.sync,
parallax_config=parallax_config.build_config())
run(sess, num_workers, worker_id, num_replicas_per_worker)
