save model

recommend-model-produce/src/main/python/tools/static_ps_trainer_v2.py

@@ -37,10 +37,10 @@ from utils.oss_client import HangZhouOSSClient
 import utils.compress as compress
 
 
-sys.path.append(os.path.dirname(os.path.abspath("")) + os.sep  +  "lib")
-print(os.path.dirname(os.path.abspath("")) + os.sep  +  "lib1")
+sys.path.append(os.path.abspath("") + os.sep  +  "lib")
+print(os.path.abspath("") + os.sep  +  "lib1")
 import brpc_flags
-print(os.path.dirname(os.path.abspath("")) + os.sep  +  "lib2")
+print(os.path.abspath("") + os.sep  +  "lib2")
 
 
 __dir__ = os.path.dirname(os.path.abspath(__file__))
@@ -237,15 +237,25 @@ class Main(object):
                     self.exe, model_dir,
                     [feed.name for feed in self.inference_feed_var],
                     self.inference_target_var)
+
             else:
                 paddle.static.save_inference_model(
                     model_dir,
                     [feed.name for feed in self.inference_feed_var],
                     [self.inference_target_var], self.exe)
 
-            compress.compress_tar(model_dir, "test")
+
+
+            # trans to new format
+            paddle.static.load_inference_model(os.path.join(model_dir, "dnn_plugin"))
+            paddle.static.save_inference_model(
+                    os.path.join(model_dir, "dnn_plugin_new"),
+                    [feed.name for feed in self.inference_feed_var],
+                    [self.inference_target_var], self.exe)
+
+            compress.compress_tar(os.path.join(model_dir, "dnn_plugin_new"), "dnn_plugin_new.tar.gz")
             client = HangZhouOSSClient("art-recommend")
-            client.put_object_from_file("dyp/test.tar.gz", "test.tar.gz")
+            client.put_object_from_file("dyp/model.tar.gz", "dnn_plugin_new.tar.gz")
 
         if reader_type == "InmemoryDataset":
             self.reader.release_memory()