diff --git a/tools/train_multi_platform.py b/tools/train_multi_platform.py
index 558339893b414884873088308cd8082eda10b0d8..ba024e89d287e66bd6228b9219349ed1728c10c4 100644
--- a/tools/train_multi_platform.py
+++ b/tools/train_multi_platform.py
@@ -120,37 +120,36 @@ def main(args):
         # 1. train with train dataset
         program.run(train_dataloader, exe, compiled_train_prog, train_fetchs,
                     epoch_id, 'train', vdl_writer)
-        if int(os.getenv("PADDLE_TRAINER_ID", 0)) == 0:
-            # 2. validate with validate dataset
-            if config.validate and epoch_id % config.valid_interval == 0:
-                if config.get('use_ema'):
-                    logger.info(logger.coloring("EMA validate start..."))
-                    with ema.apply(exe):
-                        top1_acc = program.run(valid_dataloader, exe,
-                                               compiled_valid_prog,
-                                               valid_fetchs, epoch_id, 'valid')
-                    logger.info(logger.coloring("EMA validate over!"))
-
-                top1_acc = program.run(valid_dataloader, exe,
-                                       compiled_valid_prog, valid_fetchs,
-                                       epoch_id, 'valid')
-                if top1_acc > best_top1_acc:
-                    best_top1_acc = top1_acc
-                    message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
-                        best_top1_acc, epoch_id)
-                    logger.info("{:s}".format(logger.coloring(message, "RED")))
-                    if epoch_id % config.save_interval == 0:
-
-                        model_path = os.path.join(config.model_save_dir,
-                                                  config.ARCHITECTURE["name"])
-                        save_model(train_prog, model_path,
-                                   "best_model_in_epoch_" + str(epoch_id))
-
-            # 3. save the persistable model
-            if epoch_id % config.save_interval == 0:
-                model_path = os.path.join(config.model_save_dir,
-                                          config.ARCHITECTURE["name"])
-                save_model(train_prog, model_path, epoch_id)
+
+        # 2. validate with validate dataset
+        if config.validate and epoch_id % config.valid_interval == 0:
+            if config.get('use_ema'):
+                logger.info(logger.coloring("EMA validate start..."))
+                with ema.apply(exe):
+                    top1_acc = program.run(valid_dataloader, exe,
+                                           compiled_valid_prog, valid_fetchs,
+                                           epoch_id, 'valid')
+                logger.info(logger.coloring("EMA validate over!"))
+
+            top1_acc = program.run(valid_dataloader, exe, compiled_valid_prog,
+                                   valid_fetchs, epoch_id, 'valid')
+            if top1_acc > best_top1_acc:
+                best_top1_acc = top1_acc
+                message = "The best top1 acc {:.5f}, in epoch: {:d}".format(
+                    best_top1_acc, epoch_id)
+                logger.info("{:s}".format(logger.coloring(message, "RED")))
+                if epoch_id % config.save_interval == 0:
+
+                    model_path = os.path.join(config.model_save_dir,
+                                              config.ARCHITECTURE["name"])
+                    save_model(train_prog, model_path,
+                               "best_model_in_epoch_" + str(epoch_id))
+
+        # 3. save the persistable model
+        if epoch_id % config.save_interval == 0:
+            model_path = os.path.join(config.model_save_dir,
+                                      config.ARCHITECTURE["name"])
+            save_model(train_prog, model_path, epoch_id)
 
 
 if __name__ == '__main__':