diff --git a/official/recommend/fibinet/README.md b/official/recommend/fibinet/README.md
index d9dfcabaf37e2a0e9d1f140a96e9cf4e8b6b9720..5afba7dfb3f1f51d8c12eefea2738b25a92070c5 100644
--- a/official/recommend/fibinet/README.md
+++ b/official/recommend/fibinet/README.md
@@ -79,10 +79,10 @@ Once the dataset is ready, the model can be trained and evaluated by the command
 
 ```bash
 # Python command
-python train.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --eval_while_train=True
+python train.py --data_path=./data/mindrecord --device_target=GPU --eval_while_train=True
 
 # Shell command
-bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU --eval_while_train=True
+bash ./script/run_train_gpu.sh './data/mindrecord/' 1 GPU True
 ```
 
 To evaluate the model, command as follows:
@@ -92,7 +92,7 @@ To evaluate the model, command as follows:
 python eval.py  --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU
 
 # Shell command
-bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_eval_gpu.sh './data/mindrecord/' 1 GPU
 ```
 
 # [Script Description](#contents)
@@ -105,15 +105,13 @@ bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
     ├── requirements.txt                          # python environment
     ├── script
     │   ├── common.sh
-    │   ├── run_train_and eval_gpu.sh             # Shell script of GPU singledevice training and evaluation (quick start)
-    │   ├── run_train_gpu.sh                      # Shell script of GPU singledevice training
-    │   └── run_eval_gpu.sh                       # Shell script of GPU singledevice evaluation
+    │   ├── run_train_gpu.sh                      # Shell script of GPU single-device training
+    │   └── run_eval_gpu.sh                       # Shell script of GPU single-device evaluation
     ├──src
     │   ├── callbacks.py
     │   ├── datasets.py                           # Create dataset
     │   ├── generate_synthetic_data.py            # Generate synthetic data
     │   ├── __init__.py
-    │   ├── model_builder.py
     │   ├── metrics.py                            # Script of metrics
     │   ├── preprocess_data.py                    # Preprocess on dataset
     │   ├── process_data.py
@@ -142,7 +140,7 @@ Arguments:
 
   --device_target                     Device where the code will be implemented, only support GPU currently. (Default:GPU)
   --data_path                         Where the preprocessed data is put in
-  --epochs                            Total train epochs. (Default:100)
+  --epochs                            Total train epochs. (Default:10)
   --full_batch                        Enable loading the full batch. (Default:False)
   --batch_size                        Training batch size.(Default:1000)
   --eval_batch_size                   Eval batch size.(Default:1000)
@@ -164,7 +162,7 @@ Arguments:
   --loss_file_name                    Loss output file.(Default:loss.log)
   --dataset_type                      The data type of the training files, chosen from [tfrecord, mindrecord, hd5].(Default:mindrecord)
   --vocab_cache_size                  Enable cache mode.(Default:0)
-  --eval_while_train                  Whether to evaluate after each epoch
+  --eval_while_train                  Whether to evaluate after training each epoch
 ```
 
 ### [Preprocess Script Parameters](#contents)
@@ -194,7 +192,7 @@ usage: preprocess_data.py
   --threshold                         Word frequency below this value will be regarded as OOV. It aims to reduce the vocab size.(default: 100)
   --train_line_count                  The number of examples in your dataset.
   --skip_id_convert                   0 or 1. If set 1, the code will skip the id convert, regarding the original id as the final id.(default: 0)
-  --eval_size                         The percent of eval samples in the whole dataset.
+  --eval_size                         The percent of eval samples in the whole dataset.(default: 0.1)
   --line_per_sample                   The number of sample per line, must be divisible by batch_size.
 ```
 
@@ -235,30 +233,16 @@ python src/generate_synthetic_data.py --output_file=syn_data/origin_data/train.t
 python src/preprocess_data.py --data_path=./syn_data/  --dense_dim=13 --slot_dim=51 --threshold=0 --train_line_count=40000000 --skip_id_convert=1
 ```
 
-## [Training Process](#contents)
+## [Train Process](#contents)
 
-### [SingleDevice](#contents)
-
-To train and evaluate the model, command as follows:
-
-```bash
-python train.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU
-
-# Or
-
-bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
-```
-
-### [SingleDevice For Cache Mode](#contents)
-
-To train and evaluate the model, command as follows:
+To train the model, command as follows:
 
 ```bash
 python train.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU
 
 # Or
 
-bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_train_gpu.sh './data/mindrecord/' 1 GPU False
 ```
 
 ## [Evaluation Process](#contents)
@@ -266,11 +250,11 @@ bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
 To evaluate the model, command as follows:
 
 ```bash
-python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --ckpt_path=./ckpt/fibinet_train-15_2582.ckpt
+python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --ckpt_path=./ckpt/fibinet_train-10_41265.ckpt
 
 # Or
 
-bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_eval_gpu.sh './data/mindrecord/' 1 GPU
 ```
 
 ## Inference Process
@@ -289,7 +273,7 @@ The ckpt_file parameter is required,
 Inference result is saved in current path, you can find result like this in acc.log file.
 
 ```markdown
-auc : 0.7607724041538813
+auc : 0.7814143582416716
 ```
 
 # [Model Description](#contents)
@@ -302,17 +286,17 @@ auc : 0.7607724041538813
 | ----------------- | --------------------------- |
 | Resource          |A100-SXM4-40GB   |
 | Uploaded Date     | 07/29/2022 |
-| MindSpore Version | 1.7.1                       |
+| MindSpore Version | 1.9                       |
 | Dataset           | [1]                        |
 | Batch Size        | 1000                       |
-| Epoch           | 100                         |
+| Epoch           | 10                         |
 | Learning rate   | 0.0001               |
 | Optimizer           | FTRL,Adam                         |
 | Loss Function     | Sigmoid cross entropy    |
-| Loss           | 0.4269                         |
-| Speed           | 12.837 ms/step                         |
+| Loss           | 0.4702615                       |
+| Speed           | 15.588 ms/step                         |
 | Outputs           | AUC                         |
-| Accuracy          | AUC=0.76077                   |
+| Accuracy          | AUC= 0.7814143582416716                   |
 
 # [Description of Random Situation](#contents)
 
diff --git a/official/recommend/fibinet/README_CN.md b/official/recommend/fibinet/README_CN.md
index 6ece99f41cc4a255b6e044080e6267af6d528c22..f868c40a1b98e3ad0699794bad0404b32c413694 100644
--- a/official/recommend/fibinet/README_CN.md
+++ b/official/recommend/fibinet/README_CN.md
@@ -81,10 +81,10 @@ python src/preprocess_data.py  --data_path=./data/ --dense_dim=13 --slot_dim=26
 
 ```bash
 # 执行Python脚本
-python train.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --eval_while_train=True
+python train.py --data_path=./data/mindrecord --device_target=GPU --eval_while_train=True
 
 # 执行Shell脚本
-bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU --eval_while_train=True
+bash ./script/run_train_gpu.sh './data/mindrecord/' 1 GPU True
 ```
 
 按如下操作单独评估模型：
@@ -94,7 +94,7 @@ bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
 python eval.py  --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU
 
 # 执行Shell脚本
-bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_eval_gpu.sh './data/mindrecord/' 1 GPU
 ```
 
 ## 脚本说明
@@ -107,7 +107,6 @@ bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
     ├── requirements.txt                          # python环境
     ├── script
     │   ├── common.sh
-    │   ├── run_train_and eval_gpu.sh             # GPU处理器单卡训练与评估shell脚本 （快速开始）
     │   ├── run_train_gpu.sh                      # GPU处理器单卡训练shell脚本
     │   └── run_eval_gpu.sh                       # GPU处理器单卡评估shell脚本
     ├──src
@@ -116,7 +115,6 @@ bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
     │   ├── generate_synthetic_data.py            # 生成虚拟数据
     │   ├── __init__.py
     │   ├── metrics.py                            # 模型表现评价指标脚本
-    │   ├── model_builder.py
     │   ├── preprocess_data.py                    # 数据预处理
     │   ├── process_data.py
     │   ├── fibinet.py                            # FiBiNET主体架构
@@ -142,7 +140,7 @@ Arguments:
 
   --device_target                     Device where the code will be implemented, only support GPU currently. (Default:GPU)
   --data_path                         Where the preprocessed data is put in
-  --epochs                            Total train epochs. (Default:100)
+  --epochs                            Total train epochs. (Default:10)
   --full_batch                        Enable loading the full batch. (Default:False)
   --batch_size                        Training batch size.(Default:1000)
   --eval_batch_size                   Eval batch size.(Default:1000)
@@ -164,7 +162,7 @@ Arguments:
   --loss_file_name                    Loss output file.(Default:loss.log)
   --dataset_type                      The data type of the training files, chosen from [tfrecord, mindrecord, hd5].(Default:mindrecord)
   --vocab_cache_size                  Enable cache mode.(Default:0)
-  --eval_while_train                  Whether to evaluate after each epoch
+  --eval_while_train                  Whether to evaluate after training each epoch
 ```
 
 ### 预处理脚本参数
@@ -246,7 +244,7 @@ python train.py --data_path=./data/mindrecord --dataset_type=mindrecord --device
 
 # Or
 
-bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_train_gpu.sh './data/mindrecord/' 1 GPU False
 ```
 
 ## 评估过程
@@ -254,11 +252,11 @@ bash ./script/run_train_gpu.sh --data_path=./data/mindrecord --device_target=GPU
 运行如下命令单独评估模型：
 
 ```bash
-python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --ckpt_path=./ckpt/fibinet_train-100_45830.ckpt
+python eval.py --data_path=./data/mindrecord --dataset_type=mindrecord --device_target=GPU --ckpt_path=./ckpt/fibinet_train-10_41265.ckpt
 
 # Or
 
-bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
+bash ./script/run_eval_gpu.sh './data/mindrecord/' 1 GPU
 ```
 
 ## 推理过程
@@ -269,7 +267,7 @@ bash ./script/run_eval_gpu.sh --data_path=./data/mindrecord --device_target=GPU
 python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --device_target [DEVICE_TARGET] --file_format [FILE_FORMAT]
 ```
 
-参数ckpt_file为必填项，默认值："./ckpt/fibinet_train-100_45830.ckpt"；
+参数ckpt_file为必填项，默认值："./ckpt/fibinet_train-10_41265.ckpt"；
 
 `FILE_FORMAT` 必须在 ["AIR", "MINDIR"]中选择，默认值："MINDIR"。
 
@@ -278,7 +276,7 @@ python export.py --ckpt_file [CKPT_PATH] --file_name [FILE_NAME] --device_target
 推理结果保存在脚本执行的当前路径，在acc.log中可以看到以下精度计算结果。
 
 ```markdown
-auc : 0.7607724041538813
+auc :  0.7814143582416716
 ```
 
 # 模型描述
@@ -292,18 +290,18 @@ auc : 0.7607724041538813
 | 处理器                 | GPU                                             |
 | 资源                 | A100-SXM4-40GB                                  |
 | 上传日期            | 2022-07-29                                      |
-| MindSpore版本        | 1.7.1                                           |
+| MindSpore版本        | 1.9                                           |
 | 数据集                  | [1](#数据集)                                       |
-| 训练参数      | Epoch=100,<br />batch_size=1000,<br />lr=0.0001 |
+| 训练参数      | Epoch=10,<br />batch_size=1000,<br />lr=0.0001 |
 | 优化器                | FTRL,Adam                                       |
 | 损失函数       | Sigmoid交叉熵                                      |
-| AUC分数        | 0.76077                                         |
-| 速度           | 12.837毫秒/步                                      |
-| 损失           | 0.4269                                          |
+| AUC分数        |  0.7814143582416716                                         |
+| 速度           | 15.588毫秒/步                                      |
+| 损失           | 0.4702615                                          |
 | 参数(M)           | 30                                              |
-| 推理检查点 | 179.56MB（.ckpt文件）                               |
+| 推理检查点 | 180MB（.ckpt文件）                               |
 
-所有可执行脚本参见[此处](https://gitee.com/mindspore/models/tree/master/official/recommend/FiBiNet/script)。
+所有可执行脚本参见[此处](https://gitee.com/mindspore/models/tree/master/official/recommend/fibinet/script)。
 
 # 随机情况说明
 
diff --git a/official/recommend/fibinet/default_config.yaml b/official/recommend/fibinet/default_config.yaml
index 7de64eed8df5209d0f7170558630a754b32c36e4..8b4b7ff79c97bb3d34602e9e170988855ceb2eef 100644
--- a/official/recommend/fibinet/default_config.yaml
+++ b/official/recommend/fibinet/default_config.yaml
@@ -1,4 +1,4 @@
-# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly what you are doing)
+# Builtin Configurations(DO NOT CHANGE THESE CONFIGURATIONS unless you know exactly the meaning of each parameter)
 enable_modelarts: False
 data_url: ""
 train_url: ""
@@ -12,7 +12,7 @@ enable_profiling: False
 
 # ==============================================================================
 # argparse_init 'fibinet'
-epochs: 100
+epochs: 10
 full_batch: False
 batch_size: 1000
 eval_batch_size: 1000
@@ -31,7 +31,7 @@ loss_file_name: "loss.log"
 dataset_type: "mindrecord"
 field_slice: False
 sparse: False
-eval_while_train: True
+eval_while_train: False
 deep_table_slice_mode: "column_slice"
 seed: 1024
 weight_bias_init: ['normal', 'normal']
@@ -45,7 +45,7 @@ bilinear_type: "all"  #bilinear_type: ['all', 'each', 'interaction']
 # ==============================================================================
 # fibinet export
 device_id: 0
-ckpt_file: "./ckpt/fibinet_train-100_45830.ckpt"
+ckpt_file: "./ckpt/fibinet_train-10_41265.ckpt"
 file_name: "fibinet"
 file_format: "MINDIR"
 
@@ -67,7 +67,7 @@ threshold: 100
 train_line_count: 45840617
 skip_id_convert: 0
 line_per_sample: 10
-eval_size: 0.2
+eval_size: 0.1
 
 # src/generate_synthetic_data.py 'Generate Synthetic Data'
 output_file: "./train.txt"
diff --git a/official/recommend/fibinet/export.py b/official/recommend/fibinet/export.py
index 8308779491d83876143042557490e8e038b005f0..f92c693899b597e62fbfc47293881b113616578a 100644
--- a/official/recommend/fibinet/export.py
+++ b/official/recommend/fibinet/export.py
@@ -23,7 +23,9 @@ from src.model_utils.config import config
 from src.model_utils.moxing_adapter import moxing_wrapper
 
 
-context.set_context(mode=context.GRAPH_MODE, device_target=config.device_target)
+context.set_context(mode=context.GRAPH_MODE,
+                    device_target=config.device_target,
+                    max_call_depth=10000)
 
 def modelarts_pre_process():
     pass
diff --git a/official/recommend/fibinet/script/run_eval_gpu.sh b/official/recommend/fibinet/script/run_eval_gpu.sh
index 55b1e280df8bef62fce86482abaf5747c479680c..ebcae2e84bef81a03f4864238098e839107268d1 100644
--- a/official/recommend/fibinet/script/run_eval_gpu.sh
+++ b/official/recommend/fibinet/script/run_eval_gpu.sh
@@ -17,24 +17,29 @@
 echo "=============================================================================================================="
 echo "Please run the script as: "
 echo "bash run_eval_gpu.sh"
-echo "         "
+echo "______________________________________________________________________________________________________________"
 echo "If your data_path or device_id or device_target is different from those in default_config.yaml, "
 echo "please run the script as: "
 echo "bash run_eval_gpu.sh DATA_PATH DEVICE_ID DEVICE_TARGET"
 echo "for example: bash ./script/run_eval_gpu.sh './data/mindrecord/' 1 GPU "
 echo "         "
 echo "**** FYI: only DEVICE_TARGET=GPU is supported currently. ****"
-echo "         "
+echo "______________________________________________________________________________________________________________"
 echo "Then you can find detailed log and results in files: eval_output.log, eval.log and loss.log. "
 echo "         "
 echo "If you want to set up more parameters by yourself, "
 echo "you are suggested to check the file default_config.yaml and change parameter values there. "
 echo "=============================================================================================================="
 
+if [ $# != 3 ]
+then
+  echo "Usage: bash run_train_gpu.sh [DATA_PATH] [DEVICE_ID] [DEVICE_TARGET] "
+exit 1
+fi
+
 DATA_PATH=$1
 DEVICE_ID=$2
-DEVICE_TARGET=GPU
-
+DEVICE_TARGET=$3
 
 export CUDA_VISIBLE_DEVICES=$DEVICE_ID
 python ./eval.py --data_path=$DATA_PATH --device_target=$DEVICE_TARGET > eval_output.log 2>&1 &
diff --git a/official/recommend/fibinet/script/run_train_gpu.sh b/official/recommend/fibinet/script/run_train_gpu.sh
index 1e46e3928430e655c06ff46bbf389e22c02aa207..51083a032caadca9a196f6baeee21eab4613ae14 100644
--- a/official/recommend/fibinet/script/run_train_gpu.sh
+++ b/official/recommend/fibinet/script/run_train_gpu.sh
@@ -17,24 +17,30 @@
 echo "=============================================================================================================="
 echo "Please run the script as: "
 echo "bash run_train_gpu.sh"
-echo "                 "
+echo "______________________________________________________________________________________________________________"
 echo "If your data_path or device_id or device_target is different from those in default_config.yaml, "
 echo "please run the script as: "
 echo "bash run_train_gpu.sh DATA_PATH DEVICE_ID DEVICE_TARGET EVAL_WHILE_TRAIN "
 echo "for example: bash ./script/run_train_gpu.sh './data/mindrecord/' 1 GPU True"
 echo "         "
 echo "**** FYI: only DEVICE_TARGET=GPU is supported currently. ****"
-echo "                        "
+echo "______________________________________________________________________________________________________________"
 echo "Then you can find detailed log and results in files: train.log, eval.log and loss.log. "
 echo "         "
 echo "If you want to set up more parameters by yourself, "
 echo "you are suggested to check the file default_config.yaml and change parameter values there. "
 echo "=============================================================================================================="
 
+if [ $# != 4 ]
+then
+  echo "Usage: bash run_train_gpu.sh [DATA_PATH] [DEVICE_ID] [DEVICE_TARGET] [EVAL_WHILE_TRAIN] "
+exit 1
+fi
+
 DATA_PATH=$1
 DEVICE_ID=$2
-DEVICE_TARGET=GPU
-EVAL_WHILE_TRAIN=$3
+DEVICE_TARGET=$3
+EVAL_WHILE_TRAIN=$4
 
 export CUDA_VISIBLE_DEVICES=$DEVICE_ID
 python ./train.py --data_path=$DATA_PATH --device_target=$DEVICE_TARGET --eval_while_train=$EVAL_WHILE_TRAIN > train.log 2>&1 &
diff --git a/official/recommend/fibinet/src/fibinet.py b/official/recommend/fibinet/src/fibinet.py
index 49930027d7d9b3ebd55f6fe8054b81bd438b15b1..559079aead87a913698fe02ad00049bdf50e9559 100644
--- a/official/recommend/fibinet/src/fibinet.py
+++ b/official/recommend/fibinet/src/fibinet.py
@@ -343,8 +343,7 @@ class NetWithLossClass(nn.Cell):
         sparse = config.sparse
         parallel_mode = context.get_auto_parallel_context("parallel_mode")
         is_auto_parallel = parallel_mode in (ParallelMode.SEMI_AUTO_PARALLEL, ParallelMode.AUTO_PARALLEL)
-        if sparse:
-            self.no_l2loss = True
+        self.no_l2loss = sparse
         self.network = network
         self.l2_coef = config.l2_coef
         self.loss = ops.SigmoidCrossEntropyWithLogits()
diff --git a/official/recommend/fibinet/train.py b/official/recommend/fibinet/train.py
index 8d8f43eafb009a8cb9dc533d1172d2424e529838..03c7a377063ca9e511d8f95f15dbcbe28b541888 100644
--- a/official/recommend/fibinet/train.py
+++ b/official/recommend/fibinet/train.py
@@ -30,6 +30,7 @@ def train_eval_fibinet(config):
     data_path = config.data_path
     batch_size = config.batch_size
     epochs = config.epochs
+    sparse = config.sparse
     if config.dataset_type == "tfrecord":
         dataset_type = DataType.TFRECORD
     elif config.dataset_type == "mindrecord":
@@ -57,7 +58,6 @@ def train_eval_fibinet(config):
         auc_metric = AUCMetric()
         model = Model(train_net, eval_network=eval_net, metrics={"auc": auc_metric})
         eval_callback = EvalCallBack(model, ds_eval, auc_metric, config)
-        sparse = config.sparse
         out = model.eval(ds_eval, dataset_sink_mode=(not sparse))
         print("=====" * 5 + "model.eval() initialized: {}".format(out))
         model.train(epochs, ds_train,