[NCF/TF1] Remove metrics that combine both performance and convergence

TensorFlow/Recommendation/NCF/ncf.py

@@ -181,8 +181,6 @@ def main():
     dllogger.metadata("best_hr", {"unit": None})
     dllogger.metadata("average_eval_time_per_epoch", {"unit": "s"})
     dllogger.metadata("average_train_time_per_epoch", {"unit": "s"})
-    dllogger.metadata("time_to_best", {"unit": "s"})
-    dllogger.metadata("time_to_train", {"unit": "s"})
     dllogger.metadata("average_train_throughput", {"unit": "samples/s"})
     dllogger.metadata("average_eval_throughput", {"unit": "samples/s"})
 
@@ -346,7 +344,6 @@ def main():
     eval_times = list()
     # Accuracy Metrics
     first_to_target = None
-    time_to_train = 0.0
     best_hr = 0
     best_epoch = 0
     # Buffers for global metrics
@@ -361,7 +358,6 @@ def main():
     local_ndcg_count = np.ones(1)
 
     # Begin training
-    begin_train = time.time()
     for epoch in range(args.epochs):
         # Train for one epoch
         train_start = time.time()
@@ -430,11 +426,9 @@ def main():
                 # Update summary metrics
                 if hit_rate > args.target and first_to_target is None:
                     first_to_target = epoch
-                    time_to_train = time.time() - begin_train
                 if hit_rate > best_hr:
                     best_hr = hit_rate
                     best_epoch = epoch
-                    time_to_best =  time.time() - begin_train
                     if hit_rate > args.target and final_checkpoint_path:
                         saver.save(sess, final_checkpoint_path)
 
@@ -451,8 +445,6 @@ def main():
             'average_eval_time_per_epoch': np.mean(eval_times),
             'average_eval_throughput': np.mean(eval_throughputs),
             'first_epoch_to_hit': first_to_target,
-            'time_to_train': time_to_train,
-            'time_to_best': time_to_best,
             'best_hr': best_hr,
             'best_epoch': best_epoch})
         dllogger.flush()