samples_per_epoch=10000,
task_index=args['task_index'])
validationset = bc.DummySet(input_shape=args['input_shape'],
samples_per_epoch=1000,
task_index=args['task_index'])
#Determine stopping point, i.e. compute last_step:
args["last_step"] = int(
args["trainsamples"] * args["num_epochs"] /
(args["train_batch_size_per_node"] * args["num_workers"]))
#config the stopping criterion
mc.config_team(0,
0,
ksteps=np.max(
[int(args["fully_sync_fraction"] * args["last_step"]), 1]),
max_steps=args["last_step"],
verbosity=0,
perf_freq=200)
#print info
print("Stopping after %d global steps" % (args["last_step"]))
# Train Model
#determining which model to load:
metafilelist = [
args['modelpath'] + '/' + x for x in os.listdir(args['modelpath'])
if x.endswith('.meta')
]
if not metafilelist:
def resnet_main(flags,
model_function,
input_function,
num_train_samps,
num_eval_samps,
shape=None):
"""Shared main loop for ResNet Models.
Args:
flags: FLAGS object that contains the params for running. See
ResnetArgParser for created flags.
model_function: the function that instantiates the Model and builds the
ops for train/eval. This will be passed directly into the estimator.
input_function: the function that processes the dataset and returns a
dataset that the estimator can train on. This will be wrapped with
all the relevant flags for running and passed to estimator.
shape: list of ints representing the shape of the images used for training.
This is only used if flags.export_dir is passed.
"""
# Using the Winograd non-fused algorithms provides a small performance boost.
os.environ['TF_ENABLE_WINOGRAD_NONFUSED'] = '1'
if flags.multi_gpu:
validate_batch_size_for_multi_gpu(flags.batch_size)
# There are two steps required if using multi-GPU: (1) wrap the model_fn,
# and (2) wrap the optimizer. The first happens here, and (2) happens
# in the model_fn itself when the optimizer is defined.
model_function = tf.contrib.estimator.replicate_model_fn(
loss_reduction=tf.losses.Reduction.MEAN)
# Create session config based on values of inter_op_parallelism_threads and
# intra_op_parallelism_threads. Note that we default to having
# allow_soft_placement = True, which is required for multi-GPU and not
# harmful for other modes.
myrank = 0
numworkers = 1
if (flags.enable_ml_comm == 1):
# initialize the Cray PE ML Plugin
# config the thread team (correcting the number of epochs for the effectice batch size))
#totsize = sum([reduce(lambda x, y: x*y, v.get_shape().as_list()) for v in tf.trainable_variables()])
totsize = 25551401 #Specific size for resnet50-v2
mc.init(2, 1, totsize, "tensorflow")
myrank = mc.get_rank()
numworkers = mc.get_nranks()
if (myrank == 0):
print("ResNet with {:9d} parameters".format(totsize))
max_steps_train = int(
math.ceil(flags.train_epochs * (num_train_samps + num_eval_samps) /
(mc.get_nranks() * flags.batch_size)))
#(0,0,num_steps_before_going_nonblock, max_steps_train, verbose=1, how_often_to_print=100)
mc.config_team(0, 0, max_steps_train, max_steps_train, 1, 100)
flags.model_dir = flags.model_dir if mc.get_rank() == 0 else None
flags.benchmark_log_dir = flags.benchmark_log_dir if mc.get_rank(
) == 0 else None
flags.export_dir = flags.export_dir if mc.get_rank() == 0 else None
else:
rank_id = myrank
session_config = tf.ConfigProto(
log_device_placement=False,
inter_op_parallelism_threads=flags.inter_op_parallelism_threads,
intra_op_parallelism_threads=flags.intra_op_parallelism_threads,
allow_soft_placement=True)
# Set up a RunConfig to save checkpoint and set session config.
run_config = tf.estimator.RunConfig().replace(
save_checkpoints_steps=500, session_config=session_config)
classifier = tf.estimator.Estimator(model_fn=model_function,
model_dir=flags.model_dir,
config=run_config,
params={
'resnet_size': flags.resnet_size,
'data_format': flags.data_format,
'batch_size': flags.batch_size,
'multi_gpu': flags.multi_gpu,
'train_epochs': flags.train_epochs,
'version': flags.version,
'loss_scale': flags.loss_scale,
'dtype': flags.dtype,
'mlcomm': flags.enable_ml_comm,
'log_freq':
flags.global_perf_log_freq,
'weight_decay': flags.weight_decay,
'init_lr': flags.init_lr,
'base_lr': flags.base_lr,
'warmup_epochs':
flags.warmup_epochs,
'log_freq':
flags.global_perf_log_freq,
})
benchmark_logger = logger.config_benchmark_logger(flags.benchmark_log_dir)
benchmark_logger.log_run_info('resnet')
for _ in range(flags.train_epochs // flags.epochs_between_evals):
train_hooks = hooks_helper.get_train_hooks(
flags.hooks,
batch_size=flags.batch_size,
benchmark_log_dir=flags.benchmark_log_dir)
if (myrank == 0):
print('Starting a training cycle.')
def input_fn_train():
return input_function(True, flags.data_dir, flags.batch_size,
flags.epochs_between_evals,
flags.num_parallel_calls, flags.multi_gpu,
numworkers, myrank)
tsteps = math.ceil(
float(flags.epochs_between_evals * num_train_samps) /
(numworkers * flags.batch_size))
classifier.train(input_fn=input_fn_train,
steps=tsteps,
max_steps=flags.max_train_steps)
if (myrank == 0):
print('Starting to evaluate.')
# Evaluate the model and print results
def input_fn_eval():
return input_function(False, flags.data_dir, flags.batch_size, 3,
flags.num_parallel_calls, flags.multi_gpu,
numworkers, myrank)
# flags.max_train_steps is generally associated with testing and profiling.
# As a result it is frequently called with synthetic data, which will
# iterate forever. Passing steps=flags.max_train_steps allows the eval
# (which is generally unimportant in those circumstances) to terminate.
# Note that eval will run for max_train_steps each loop, regardless of the
# global_step count.
esteps = math.ceil(
float(num_eval_samps) / (numworkers * flags.batch_size))
eval_results = classifier.evaluate(input_fn=input_fn_eval,
steps=esteps)
benchmark_logger.log_evaluation_result(eval_results)
if model_helpers.past_stop_threshold(flags.stop_threshold,
eval_results['accuracy']):
break
if flags.export_dir is not None:
warn_on_multi_gpu_export(flags.multi_gpu)
# Exports a saved model for the given classifier.
input_receiver_fn = export.build_tensor_serving_input_receiver_fn(
shape, batch_size=flags.batch_size)
classifier.export_savedmodel(flags.export_dir, input_receiver_fn)
if (flags.enable_ml_comm == 1):
mc.finalize()
def on_train_begin(self, logs=None):
mc.init(1, 1, self.buffer_size, "tensorflow")
#this enables pipelining after certain number of steps. it screws model accuracy here though
#mc.config_team(0, 0, int(0.2*self.max_steps), self.max_steps, 0, 100)
mc.config_team(0, 0, self.max_steps, self.max_steps, 0, 100)
def on_train_begin(self, logs=None):
mc.init(1, 1, self.buffer_size, "tensorflow")
mc.config_team(0, 0, int(0.2 * self.max_steps), self.max_steps, 2, 100)
def train(self):
train_step, loss, lossL1Train,train_true,train_predict = self.optimize()
lossL1Val,val_true,val_predict = self.validation_loss()
lossL1Test,test_true,test_predict = self.test_loss()
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
### taking config from the MKL benchmarks.
config.allow_soft_placement = True
config.intra_op_parallelism_threads = 1 ## default
config.inter_op_parallelism_threads = 2 ## Default
#used to save the model
saver = tf.train.Saver()
global best_validation_accuracy
global last_improvement
global total_iterations
best_validation_accuracy = 1.0 #Best validation accuracy seen so far
last_improvement = 0 #Iteration-number for last improvement to validation accuracy.
require_improvement = hp.RUNPARAM['require_improvement'] #Stop optimization if no improvement found in this many iterations.
total_iterations = 0 #Counter for total number of iterations performed so far.
#initialize the CPE ML Plugin with one team (single thread for now) and the model size
totsize = sum([reduce(lambda x, y: x*y, v.get_shape().as_list()) for v in tf.trainable_variables()])
mc.init(1, 1, totsize, "tensorflow")
hp.RUNPARAM['batch_per_epoch'] = hp.RUNPARAM['batch_per_epoch'] / mc.get_nranks()
hp.RUNPARAM['batch_per_epoch_val'] = hp.RUNPARAM['batch_per_epoch_val'] / mc.get_nranks()
totsteps = hp.RUNPARAM['num_epoch'] * hp.RUNPARAM['batch_per_epoch']
mc.config_team(0, 0, totsteps, totsteps, 2, 50)
if (mc.get_rank() == 0):
print("+------------------------------+")
print("| CosmoFlow |")
print("| # Ranks = {:5d} |".format(mc.get_nranks()))
print("| Global Batch = {:6d} |".format(mc.get_nranks() * hp.Input['BATCH_SIZE']))
print("| # Parameters = {:9d} |".format(totsize))
print("+------------------------------+")
#use the CPE ML Plugin to broadcast initial model parameter values
new_vars = mc.broadcast(tf.trainable_variables(),0)
bcast = tf.group(*[tf.assign(v,new_vars[k]) for k,v in enumerate(tf.trainable_variables())])
if(self.is_train):
with tf.Session(config=config) as sess:
losses_train = []
losses_val = []
losses = []
val_accuracys = []
data_accuracys = []
#do all parameter initializations
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
sess.run(bcast)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
elapsed_time = 0.
for epoch in range(hp.RUNPARAM['num_epoch']):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
total_iterations += 1
start_time = time.time()
loss_per_epoch_val = 0
loss_per_epoch_train = 0
for i in range(hp.RUNPARAM['batch_per_epoch']):
step_start_time = time.time()
_,lossTrain,lossL1Train_,train_true_,train_predict_ = sess.run([train_step,loss,lossL1Train,train_true,train_predict])
step_finish_time = time.time()
elapsed_time += (step_finish_time-step_start_time)
samps_per_sec = mc.get_nranks() * (epoch * hp.RUNPARAM['batch_per_epoch'] * hp.Input['BATCH_SIZE'] + (i+1) * hp.Input['BATCH_SIZE']) / elapsed_time
if (mc.get_rank() == 0):
print("Train Step: " + str(i) + ", Samples/Sec = " + str(samps_per_sec) + ", Loss = " + str(lossTrain))
loss_per_epoch_train +=lossL1Train_
global_loss = np.array([loss_per_epoch_train],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_train = global_loss / hp.RUNPARAM['batch_per_epoch']
losses.append(loss_per_epoch_train)
losses_train.append(loss_per_epoch_train)
for i in range(hp.RUNPARAM['batch_per_epoch_val']):
if (mc.get_rank() == 0):
print("Val Step = " + str(i))
loss_,val_true_,val_predict_ = sess.run([lossL1Val,val_true,val_predict])
loss_per_epoch_val += loss_
global_loss = np.array([loss_per_epoch_val],dtype=np.float32)
mc.average(global_loss)
loss_per_epoch_val = global_loss / hp.RUNPARAM['batch_per_epoch_val']
losses_val.append(loss_per_epoch_val)
if(loss_per_epoch_val < best_validation_accuracy):
best_validation_accuracy = loss_per_epoch_val
last_improvement = total_iterations
if (mc.get_rank() == 0):
saver.save(sess=sess, save_path=save_path)
if (mc.get_rank() == 0):
print("Epoch {} took {:.3f}s".format(epoch, time.time() - start_time))
print " training loss: %.3f" %(loss_per_epoch_train)
print " validation loss: %.3f" %(loss_per_epoch_val)
print " best loss: %.3f"%best_validation_accuracy
np.savetxt(os.path.join(hp.Path['train_result'],'loss_train.txt'),losses_train)
np.savetxt(os.path.join(hp.Path['val_result'],'loss_val.txt'),losses_val)
np.savetxt(os.path.join(hp.Path['train_result'],'losses.txt'),losses)
#np.savetxt(os.path.join(hp.Path['train_result'],'train_pred'+str(epoch)+'.txt'),np.c_[train_true_,train_predict_])
#np.savetxt(os.path.join(hp.Path['val_result'],'val_pred'+str(epoch)+'.txt'),np.c_[val_true_,val_predict_])
if(total_iterations - last_improvement > require_improvement):
if (mc.get_rank() == 0):
print ("No improvement found in a while, stopping optimization.")
break
coord.request_stop();
coord.join(threads);
if(self.is_test and mc.get_rank() == 0):
save_path = os.path.join(hp.Path['Model_path'], 'best_validation')
if self.save_path != None:
save_path = self.save_path
with tf.Session() as sess:
saver.restore(sess=sess,save_path=save_path)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
loss_test = []
for i in range(0,hp.RUNPARAM['iter_test']):
start_time = time.time()
lossL1Test_,test_true_,test_predict_ = sess.run([lossL1Test,test_true,test_predict])
loss_test.append(lossL1Test_)
print("Box {} took {:.3f}s".format(i, time.time() - start_time))
print " test loss: %.3f"%lossL1Test_
np.savetxt(os.path.join(hp.Path['test_result'],'test_batch_'+str(i)+'.txt'),np.c_[test_true_,test_predict_])
np.savetxt(os.path.join(hp.Path['test_result'],'loss_test.txt'),loss_test)
coord.request_stop()
coord.join(threads)
#cleanup the CPE ML Plugin
mc.finalize()