changed release method to __del__ & added comments to explain that ge…

…nerate_artifacts typically will throw an error if unsuccessful

orttraining/orttraining/python/training/artifacts.py

@@ -268,5 +268,3 @@ def _export_to_ort_format(model_path, output_dir, ort_format, custom_op_library_
     onnx.save(optim_model, optimizer_model_path)
     _export_to_ort_format(optimizer_model_path, artifact_directory, ort_format, custom_op_library_path)
     logging.info("Saved optimizer model to %s", optimizer_model_path)
-
-    training_block.release()

orttraining/orttraining/python/training/onnxblock/blocks.py

@@ -90,7 +90,7 @@ def infer_shapes_on_base(self):
         else:
             return onnx.shape_inference.infer_shapes(accessor._GLOBAL_ACCESSOR.model)
 
-    def release(self):
+    def __del__(self):
         # since the ModelProto does not store the external data parameters themselves, just the metadata
         # for where the external data can be found, we retain the external data files for the intermediate
         # calls until the Block no longer needs to be used.

orttraining/orttraining/test/python/orttraining_test_ort_apis_onnxblock.py

@@ -1177,6 +1177,8 @@ def test_generate_artifacts_external_data_separate_files():
             artifact_directory=temp_dir,
         )
 
+        # generate_artifacts should have thrown if it didn't complete successfully.
+        # Below is a sanity check to validate that all the expected files were created.
         assert os.path.exists(os.path.join(temp_dir, "training_model.onnx"))
         assert os.path.exists(os.path.join(temp_dir, "eval_model.onnx"))
         assert os.path.exists(os.path.join(temp_dir, "optimizer_model.onnx"))