Switch output writing to h5py to reduce memory footprint. Update version

VERSION.txt

@@ -1 +1,2 @@
-1.1.1
+1.1.2
+

rinokeras/core/v1x/train/RinokerasGraph.py

@@ -2,6 +2,7 @@
 from typing import Sequence, Union, Any, Optional, Dict
 import pickle as pkl
 
+import h5py
 import tensorflow as tf
 from tensorflow.python.client import timeline
 from tqdm import tqdm
@@ -160,21 +161,22 @@ def run_epoch(self,
                   epoch_num: Optional[int] = None,
                   summary_writer: Optional[tf.summary.FileWriter] = None,
                   save_outputs: Optional[str] = None) -> MetricsAccumulator:
-        all_outputs = []
 
         with self.add_progress_bar(data_len, epoch_num).initialize():
             assert self.epoch_metrics is not None
-            while True:
-                if save_outputs is not None:
-                    loss, outputs = self.run('default', return_outputs=True)
-                    all_outputs.append(outputs)
-                else:
+            if save_outputs is not None:
+                i = 0
+                with h5py.File(save_outputs, 'w') as f:
+                    while True:
+                        loss, outputs = self.run('default', return_outputs=True)
+                        grp = f.create_group(str(i))
+                        outputs = outputs[0]  # can we rely on this being a tuple of length 1?
+                        for key in outputs.keys():
+                            grp.create_dataset(key, data=outputs[key])
+                        i += 1
+            else:
+                while True:
                     self.run('default')
-
-        if save_outputs is not None:
-            with open(save_outputs, 'wb') as f:
-                pkl.dump(all_outputs, f)
-
         return self.epoch_metrics
 
     def run_for_n_steps(self,