only eval and save at trainer 0

tools/train.py

@@ -62,9 +62,7 @@ def main(args):
     startup_prog = fluid.Program()
     train_prog = fluid.Program()
 
-    # best_top1_acc_list[0]: top1 acc
-    # best_top1_acc_list[1]: epoch id
-    best_top1_acc_list = [0.0, 0]
+    best_top1_acc_list = (0.0, -1)  # (top1_acc, epoch_id)
 
     train_dataloader, train_fetchs = program.build(
         config, train_prog, startup_prog, is_train=True)
@@ -97,15 +95,16 @@ def main(args):
         # 1. train with train dataset
         program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
                     epoch_id, 'train')
+        if int(os.environ.get("PADDLE_TRAINERS_ID", 0)) == 0:
             # 2. validate with validate dataset
             if config.validate and epoch_id % config.valid_interval == 0:
-            top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog,
-                                   valid_fetchs, epoch_id, 'valid')
+                top1_acc = program.run(valid_dataloader, exe,
+                                       compiled_valid_prog, valid_fetchs,
+                                       epoch_id, 'valid')
                 if top1_acc > best_top1_acc_list[0]:
-                best_top1_acc_list[0] = top1_acc
-                best_top1_acc_list[1] = epoch_id
+                    best_top1_acc_list = (top1_acc, epoch_id)
                     logger.info("Best top1 acc: {}, in epoch: {}".format(
-                    best_top1_acc_list[0], best_top1_acc_list[1]))
+                        *best_top1_acc_list))
                     model_path = os.path.join(config.model_save_dir,
                                               config.ARCHITECTURE["name"])
                     save_model(train_prog, model_path, "best_model")