def get_estimator(
self, force_build_lm=False, build_explain=False, context_dim=None
):
config = self._get_estimator_config()
model_fn = get_model_fn(
target_model_fn=self._target_model,
predict_op=self._predict_op,
predict_proba_op=self._predict_proba_op,
build_target_model=self.input_pipeline.target_dim is not None,
build_lm=force_build_lm or self.config.lm_loss_coef > 0.0,
encoder=self.input_pipeline.text_encoder,
target_dim=self.input_pipeline.target_dim,
label_encoder=self.input_pipeline.label_encoder,
saver=self.saver,
build_explain=build_explain,
context_dim=context_dim or self.input_pipeline.config.context_dim,
)
hooks = [InitializeHook(self.saver)]
est = tf.estimator.Estimator(
model_dir=self.estimator_dir,
model_fn=model_fn,
config=config,
params=self.config,
)
return est, hooks
def get_estimator(self, force_build_lm=False, build_explain=False):
build_lm = force_build_lm or self.config.lm_loss_coef > 0.0
config = self._get_estimator_config()
model_fn = get_model_fn(
target_model_fn=self._target_model,
pre_target_model_hook=self._pre_target_model_hook,
predict_op=self._predict_op,
predict_proba_op=self._predict_proba_op,
build_target_model=self.input_pipeline.target_dim is not None,
lm_type=self.config.lm_type if build_lm else None,
encoder=self.input_pipeline.text_encoder,
target_dim=self.input_pipeline.target_dim,
label_encoder=self.input_pipeline.label_encoder,
build_explain=build_explain,
n_replicas=max(1, len(self.resolved_gpus)))
hooks = [InitializeHook(self.saver)]
est = tf.estimator.Estimator(
model_dir=self.estimator_dir,
model_fn=model_fn,
config=config,
params=self.config,
)
return est, hooks
def get_estimator(self, force_build_lm=False):
conf = tf.ConfigProto(
allow_soft_placement=self.config.soft_device_placement,
log_device_placement=self.config.log_device_placement,
)
num_gpus = len(self.config.visible_gpus)
if num_gpus > 1:
distribute_strategy = PatchedParameterServerStrategy(num_gpus_per_worker=num_gpus)
else:
distribute_strategy = None
config = tf.estimator.RunConfig(
tf_random_seed=self.config.seed,
save_summary_steps=self.config.val_interval,
save_checkpoints_secs=None,
save_checkpoints_steps=None,
# disable auto summaries
session_config=conf,
log_step_count_steps=100,
train_distribute=distribute_strategy,
keep_checkpoint_max=1
)
model_fn = get_model_fn(
target_model_fn=self._target_model,
predict_op=self._predict_op,
predict_proba_op=self._predict_proba_op,
build_target_model=self.input_pipeline.target_dim is not None,
build_lm=force_build_lm or self.config.lm_loss_coef > 0.0 or self.input_pipeline.target_dim is None,
encoder=ENCODER,
target_dim=self.input_pipeline.target_dim,
label_encoder=self.input_pipeline.label_encoder,
saver=self.saver
)
return tf.estimator.Estimator(
model_dir=self.estimator_dir,
model_fn=model_fn,
config=config,
params=self.config
)
def get_estimator(self, force_build_lm=False):
conf = tf.ConfigProto(
allow_soft_placement=self.config.soft_device_placement,
log_device_placement=self.config.log_device_placement,
)
conf.gpu_options.per_process_gpu_memory_fraction = (
self.config.per_process_gpu_memory_fraction)
distribute_strategy = self._distribute_strategy(
self.config.visible_gpus)
config = tf.estimator.RunConfig(
tf_random_seed=self.config.seed,
save_summary_steps=self.config.val_interval,
save_checkpoints_secs=None,
save_checkpoints_steps=None,
# disable auto summaries
session_config=conf,
log_step_count_steps=100,
train_distribute=distribute_strategy,
keep_checkpoint_max=1)
model_fn = get_model_fn(
target_model_fn=self._target_model,
predict_op=self._predict_op,
predict_proba_op=self._predict_proba_op,
build_target_model=self.input_pipeline.target_dim is not None,
build_lm=force_build_lm or self.config.lm_loss_coef > 0.0,
encoder=self.input_pipeline.text_encoder,
target_dim=self.input_pipeline.target_dim,
label_encoder=self.input_pipeline.label_encoder,
saver=self.saver)
hooks = [InitializeHook(self.saver)]
est = tf.estimator.Estimator(model_dir=self.estimator_dir,
model_fn=model_fn,
config=config,
params=self.config)
return est, hooks