Don't save model twice, copy instead

ml-agents/mlagents/model_serialization.py

@@ -1,5 +1,6 @@
 from distutils.util import strtobool
 import os
+import shutil
 from typing import Any, List, Set, NamedTuple
 from distutils.version import LooseVersion
 
@@ -227,3 +228,20 @@ def _enforce_onnx_conversion() -> bool:
         return strtobool(val)
     except Exception:
         return False
+
+
+def copy_model_files(source_nn_path: str, destination_nn_path: str) -> None:
+    """
+    Copy the .nn file at the given source to the destination.
+    Also copies the corresponding .onnx file if it exists.
+    """
+    shutil.copyfile(source_nn_path, destination_nn_path)
+    logger.info(f"Copied {source_nn_path} to {destination_nn_path}.")
+    # Copy the onnx file if it exists
+    source_onnx_path = os.path.splitext(source_nn_path)[0] + ".onnx"
+    destination_onnx_path = os.path.splitext(destination_nn_path)[0] + ".onnx"
+    try:
+        shutil.copyfile(source_onnx_path, destination_onnx_path)
+        logger.info(f"Copied {source_onnx_path} to {destination_onnx_path}.")
+    except OSError:
+        pass

ml-agents/mlagents/trainers/trainer/rl_trainer.py

@@ -5,7 +5,7 @@
 import abc
 import time
 import attr
-from mlagents.model_serialization import SerializationSettings
+from mlagents.model_serialization import SerializationSettings, copy_model_files
 from mlagents.trainers.policy.checkpoint_manager import (
     NNCheckpoint,
     NNCheckpointManager,
@@ -131,12 +131,14 @@ def save_model(self) -> None:
                 "Trainer has multiple policies, but default behavior only saves the first."
             )
         policy = list(self.policies.values())[0]
-        settings = SerializationSettings(policy.model_path, self.brain_name)
         model_checkpoint = self._checkpoint()
+
+        # Copy the checkpointed model files to the final output location
+        copy_model_files(model_checkpoint.file_path, f"{policy.model_path}.nn")
+
         final_checkpoint = attr.evolve(
             model_checkpoint, file_path=f"{policy.model_path}.nn"
         )
-        policy.save(policy.model_path, settings)
         NNCheckpointManager.track_final_checkpoint(self.brain_name, final_checkpoint)
 
     @abc.abstractmethod