def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tf.config.set_soft_device_placement(True)
if FLAGS.checkpoint_dir:
model_lib_v2.eval_continuously(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300, timeout=FLAGS.eval_timeout)
else:
if FLAGS.use_tpu:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver()
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
elif FLAGS.num_workers > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
else:
strategy = tf.compat.v2.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
use_tpu=FLAGS.use_tpu)
def test_train_loop_then_eval_loop(self):
"""Tests that Estimator and input function are constructed correctly."""
model_dir = tf.test.get_temp_dir()
pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
new_pipeline_config_path = os.path.join(model_dir,
'new_pipeline.config')
config_util.clear_fine_tune_checkpoint(pipeline_config_path,
new_pipeline_config_path)
config_kwarg_overrides = _get_config_kwarg_overrides()
train_steps = 2
strategy = tf2.distribute.MirroredStrategy(['/cpu:0', '/cpu:1'])
with strategy.scope():
model_lib_v2.train_loop(new_pipeline_config_path,
model_dir=model_dir,
train_steps=train_steps,
checkpoint_every_n=1,
**config_kwarg_overrides)
model_lib_v2.eval_continuously(new_pipeline_config_path,
model_dir=model_dir,
checkpoint_dir=model_dir,
train_steps=train_steps,
wait_interval=1,
timeout=10,
**config_kwarg_overrides)
def test_train_loop_then_eval_loop(self):
"""Tests that Estimator and input function are constructed correctly."""
hparams = model_hparams.create_hparams(
hparams_overrides='load_pretrained=false')
pipeline_config_path = get_pipeline_config_path(MODEL_NAME_FOR_TEST)
config_kwarg_overrides = _get_config_kwarg_overrides()
model_dir = tf.test.get_temp_dir()
train_steps = 2
model_lib_v2.train_loop(
hparams,
pipeline_config_path,
model_dir=model_dir,
train_steps=train_steps,
checkpoint_every_n=1,
**config_kwarg_overrides)
model_lib_v2.eval_continuously(
hparams,
pipeline_config_path,
model_dir=model_dir,
checkpoint_dir=model_dir,
train_steps=train_steps,
wait_interval=10,
**config_kwarg_overrides)
def evaluate(self):
"""
Evaluates all Training Checkpoints.
"""
model_lib_v2.eval_continuously(pipeline_config_path=self.config_path,
model_dir=self.checkpoint_path,
checkpoint_dir=self.checkpoint_path,
postprocess_on_cpu=True)
def eval_continuously(self):
print("Running evaluation loop...")
strategy = tf.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.eval_continuously(
pipeline_config_path=os.path.join(self._training_loop_path,
"pipeline.config"),
model_dir=self._training_loop_path,
checkpoint_dir=self._training_loop_path)
def evaluate(_):
pipeline_config = os.path.join(FLAGS.model_dir, 'pipeline.config')
eval_continuously(pipeline_config_path=pipeline_config,
train_steps=FLAGS.train_steps,
model_dir=FLAGS.model_dir,
checkpoint_dir=FLAGS.model_dir,
wait_interval=FLAGS.wait_interval,
timeout=FLAGS.timeout)
def main(unused_argv):
# ste the gpu (device:GPU:0)
print("Num GPUs Available: ",
len(tf.config.experimental.list_physical_devices('GPU')))
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
# Restrict TensorFlow to only use the first GPU
try:
tf.config.experimental.set_visible_devices(gpus[0], 'GPU')
tf.config.experimental.set_memory_growth(gpus[0], True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPU")
except RuntimeError as e:
# Visible devices must be set before GPUs have been initialized
print(e)
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tf.config.set_soft_device_placement(True)
if FLAGS.checkpoint_dir:
model_lib_v2.eval_continuously(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300,
timeout=FLAGS.eval_timeout)
else:
if FLAGS.use_tpu:
# TPU is automatically inferred if tpu_name is None and
# we are running under cloud ai-platform.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
# elif FLAGS.num_workers > 1:
# strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
# else:
# strategy = tf.compat.v2.distribute.MirroredStrategy()
# with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
use_tpu=FLAGS.use_tpu,
checkpoint_every_n=FLAGS.checkpoint_every_n,
record_summaries=FLAGS.record_summaries)
def evaluate_model(self, hyper_params: HyperParameterInformation) -> None:
pipeline_config_path = os.path.join(self.path.model_dir,
'pipeline.config')
eval_continuously(pipeline_config_path=pipeline_config_path,
train_steps=hyper_params.training_steps,
model_dir=self.path.model_dir,
checkpoint_dir=self.path.model_dir,
override_eval_num_epochs=False,
wait_interval=180,
timeout=3600)
def main(unused_argv):
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tf.config.set_soft_device_placement(True)
if FLAGS.checkpoint_dir:
if FLAGS.eval_all_checkpoints:
model_lib_v2.eval_all_checkpoints(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir)
else:
model_lib_v2.eval_continuously(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300,
timeout=FLAGS.eval_timeout)
else:
if FLAGS.use_tpu:
# TPU is automatically inferred if tpu_name is None and
# we are running under cloud ai-platform.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
elif FLAGS.num_workers > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
else:
strategy = tf.compat.v2.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
use_tpu=FLAGS.use_tpu,
checkpoint_every_n=FLAGS.checkpoint_every_n,
record_summaries=FLAGS.record_summaries)
def main(_):
with open('system_dict.json') as json_file:
args = json.load(json_file)
tf.config.set_soft_device_placement(True)
if args["checkpoint_dir"]:
model_lib_v2.eval_continuously(
pipeline_config_path=args["pipeline_config_path"],
model_dir=args["model_dir"],
train_steps=args["num_train_steps"],
sample_1_of_n_eval_examples=args["sample_1_of_n_eval_examples"],
sample_1_of_n_eval_on_train_examples=(
args["sample_1_of_n_eval_on_train_examples"]),
checkpoint_dir=args["checkpoint_dir"],
wait_interval=300,
timeout=args["eval_timeout"])
else:
if args["use_tpu"]:
# TPU is automatically inferred if tpu_name is None and
# we are running under cloud ai-platform.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
args["tpu_name"])
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
elif args["num_workers"] > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
else:
strategy = tf.compat.v2.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=args["pipeline_config_path"],
model_dir=args["model_dir"],
train_steps=args["num_train_steps"],
use_tpu=args["use_tpu"],
checkpoint_every_n=args["checkpoint_every_n"],
record_summaries=args["record_summaries"])
def main(unused_argv):
if FLAGS.checkpoint_dir:
print("\n-------Running evaluation")
else:
print("\n-------Running traingin!")
flags.mark_flag_as_required('model_dir')
flags.mark_flag_as_required('pipeline_config_path')
tf.config.set_soft_device_placement(True)
print(
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
)
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
try:
# Currently, memory growth needs to be the same across GPUs
for gpu in gpus:
tf.config.experimental.set_memory_growth(gpu, True)
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
print(len(gpus), "Physical GPUs,", len(logical_gpus),
"Logical GPUs")
except RuntimeError as e:
# Memory growth must be set before GPUs have been initialized
print(e)
print(
"++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++"
)
if FLAGS.checkpoint_dir:
model_lib_v2.eval_continuously(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
train_steps=FLAGS.num_train_steps,
sample_1_of_n_eval_examples=FLAGS.sample_1_of_n_eval_examples,
sample_1_of_n_eval_on_train_examples=(
FLAGS.sample_1_of_n_eval_on_train_examples),
checkpoint_dir=FLAGS.checkpoint_dir,
wait_interval=300,
timeout=FLAGS.eval_timeout)
else:
if FLAGS.use_tpu:
# TPU is automatically inferred if tpu_name is None and
# we are running under cloud ai-platform.
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name)
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.experimental.TPUStrategy(resolver)
elif FLAGS.num_workers > 1:
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()
else:
strategy = tf.compat.v2.distribute.MirroredStrategy()
with strategy.scope():
model_lib_v2.train_loop(
pipeline_config_path=FLAGS.pipeline_config_path,
model_dir=FLAGS.model_dir,
save_final_config=True,
train_steps=FLAGS.num_train_steps,
use_tpu=FLAGS.use_tpu,
checkpoint_every_n=FLAGS.checkpoint_every_n,
record_summaries=FLAGS.record_summaries)
