!4965 fix googlenet deepfm

Merge pull request !4965 from panfengfeng/fix_googlenet_deepfm

model_zoo/official/cv/googlenet/README.md

@@ -82,10 +82,10 @@ After installing MindSpore via the official website, you can start training and
 python train.py > train.log 2>&1 & 
 
 # run distributed training example
-sh scripts/run_train.sh rank_table.json
+Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
 
 # run evaluation example
-python eval.py > eval.log 2>&1 &  OR  sh run_eval.sh
+python eval.py > eval.log 2>&1 &  OR  Ascend: sh run_eval.sh OR GPU: sh run_eval_gpu.sh
 ```
 
 
@@ -161,7 +161,7 @@ The model checkpoint will be saved in the current directory.
 ### Distributed Training
 
 ```
-sh scripts/run_train.sh rank_table.json
+Ascend: sh scripts/run_train.sh rank_table.json OR GPU: sh scripts/run_train_gpu.sh 8 0,1,2,3,4,5,6,7
 ```
 
 The above shell script will run distribute training in the background. You can view the results through the file `train_parallel[X]/log`. The loss value will be achieved as follows:
@@ -187,7 +187,9 @@ Before running the command below, please check the checkpoint path used for eval
 ```
 python eval.py > eval.log 2>&1 &  
 OR
-sh scripts/run_eval.sh
+Ascned: sh scripts/run_eval.sh
+OR
+GPU: sh scripts/run_eval_gpu.sh
 ```
 
 The above python command will run in the background. You can view the results through the file "eval.log". The accuracy of the test dataset will be as follows:

model_zoo/official/recommend/deepfm/train.py

@@ -83,6 +83,8 @@ if __name__ == '__main__':
                               rank_size=rank_size,
                               rank_id=rank_id)
 
+    steps_size = ds_train.get_dataset_size()
+
     model_builder = ModelBuilder(ModelConfig, TrainConfig)
     train_net, eval_net = model_builder.get_train_eval_net()
     auc_metric = AUCMetric()
@@ -95,8 +97,12 @@ if __name__ == '__main__':
     if train_config.save_checkpoint:
         if rank_size:
             train_config.ckpt_file_name_prefix = train_config.ckpt_file_name_prefix + str(get_rank())
-        config_ck = CheckpointConfig(save_checkpoint_steps=train_config.save_checkpoint_steps,
-                                     keep_checkpoint_max=train_config.keep_checkpoint_max)
+        if args_opt.device_target == "GPU":
+            config_ck = CheckpointConfig(save_checkpoint_steps=steps_size,
+                                         keep_checkpoint_max=train_config.keep_checkpoint_max)
+        else:
+            config_ck = CheckpointConfig(save_checkpoint_steps=train_config.save_checkpoint_steps,
+                                         keep_checkpoint_max=train_config.keep_checkpoint_max)
         ckpt_cb = ModelCheckpoint(prefix=train_config.ckpt_file_name_prefix,
                                   directory=args_opt.ckpt_path,
                                   config=config_ck)