Update deep_speech.py (#8694)

research/deep_speech/deep_speech.py

@@ -28,8 +28,6 @@ import data.dataset as dataset
 import decoder
 import deep_speech_model
 from official.utils.flags import core as flags_core
-from official.r1.utils.logs import hooks_helper
-from official.r1.utils.logs import logger
 from official.utils.misc import distribution_utils
 from official.utils.misc import model_helpers
 
@@ -276,16 +274,6 @@ def run_deep_speech(_):
       "use_bias": flags_obj.use_bias
   }
 
-  dataset_name = "LibriSpeech"
-  benchmark_logger = logger.get_benchmark_logger()
-  benchmark_logger.log_run_info("deep_speech", dataset_name, run_params,
-                                test_id=flags_obj.benchmark_test_id)
-
-  train_hooks = hooks_helper.get_train_hooks(
-      flags_obj.hooks,
-      model_dir=flags_obj.model_dir,
-      batch_size=flags_obj.batch_size)
-
   per_replica_batch_size = per_device_batch_size(flags_obj.batch_size, num_gpus)
 
   def input_fn_train():
@@ -307,7 +295,7 @@ def run_deep_speech(_):
         train_speech_dataset.entries, cycle_index, flags_obj.sortagrad,
         flags_obj.batch_size)
 
-    estimator.train(input_fn=input_fn_train, hooks=train_hooks)
+    estimator.train(input_fn=input_fn_train)
 
     # Evaluation
     tf.logging.info("Starting to evaluate...")
@@ -433,8 +421,7 @@ def define_deep_speech_flags():
 
 
 def main(_):
-  with logger.benchmark_context(flags_obj):
-    run_deep_speech(flags_obj)
+  run_deep_speech(flags_obj)
 
 
 if __name__ == "__main__":