diff --git a/test_tipc/benchmark_train.sh b/test_tipc/benchmark_train.sh
index bd3e8de4d36cbfb62ab8818a654f67892d06dfc1..a3d041e522a02e2ae654ba9b6170713396cfddfd 100644
--- a/test_tipc/benchmark_train.sh
+++ b/test_tipc/benchmark_train.sh
@@ -134,16 +134,16 @@ if  [ ! -n "$PARAMS" ] ;then
     device_num_list=(N1C4)
     run_mode="DP"
 else
-    # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_process_type}_${run_mode}_${device_num}
+    # parser params from input: modeltype_bs${bs_item}_${fp_item}_${run_mode}_${device_num}
     IFS="_"
     params_list=(${PARAMS})
     model_type=${params_list[0]}
     batch_size=${params_list[1]}
     batch_size=`echo  ${batch_size} | tr -cd "[0-9]" `
     precision=${params_list[2]}
-    run_process_type=${params_list[3]}
-    run_mode=${params_list[4]}
-    device_num=${params_list[5]}
+    # run_process_type=${params_list[3]}
+    run_mode=${params_list[3]}
+    device_num=${params_list[4]}
     IFS=";"
 
     if [ ${precision} = "null" ];then
diff --git a/test_tipc/docs/benchmark_train.md b/test_tipc/docs/benchmark_train.md
index 82de39020ab1be7933b899f57242050f5f7d9209..e8a978ce6815275dee052291c44f4de47f9ff0ef 100644
--- a/test_tipc/docs/benchmark_train.md
+++ b/test_tipc/docs/benchmark_train.md
@@ -19,27 +19,24 @@ bash test_tipc/prepare.sh test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt
 # 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
 bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train
 
-# 单机多卡训练，MultiP 表示多进程；单卡训练用SingleP
-# 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
-bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train
 ```
 
 `test_tipc/benchmark_train.sh`支持根据传入的第三个参数实现只运行某一个训练配置，如下：
 ```shell
 # 运行格式：bash test_tipc/benchmark_train.sh train_benchmark.txt mode
-bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train  dynamic_bs8_null_SingleP_DP_N1C1
+bash test_tipc/benchmark_train.sh test_tipc/configs/det_mv3_db_v2_0/train_infer_python.txt benchmark_train  dynamic_bs8_fp32_DP_N1C1
 ```
-dynamic_bs8_null_SingleP_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
-`${modeltype}_${batch_size}_${fp_item}_${run_process_type}_${run_mode}_${device_num}`
-包含的信息有：模型类型、batchsize大小、训练精度如fp32,fp16等、分布式训练进程类型、分布式运行模式以及分布式训练使用的机器信息如单机单卡（N1C1）。
+dynamic_bs8_fp32_DP_N1C1为test_tipc/benchmark_train.sh传入的参数，格式如下：
+`${modeltype}_${batch_size}_${fp_item}_${run_mode}_${device_num}`
+包含的信息有：模型类型、batchsize大小、训练精度如fp32,fp16等、分布式运行模式以及分布式训练使用的机器信息如单机单卡（N1C1）。
 
 
 ## 2. 日志输出
 
-运行后将输出模型的训练日志和日志解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt` 参数文件的训练日志解析结果是：
+运行后将保存模型的训练日志和解析日志，使用 `test_tipc/configs/det_mv3_db_v2_0/train_benchmark.txt` 参数文件的训练日志解析结果是：
 
 ```
-{"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "det_mv3_db_v2_0_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "images/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"}
+{"model_branch": "dygaph", "model_commit": "7c39a1996b19087737c05d883fd346d2f39dbcc0", "model_name": "det_mv3_db_v2_0_bs8_fp32_SingleP_DP", "batch_size": 8, "fp_item": "fp32", "run_process_type": "SingleP", "run_mode": "DP", "convergence_value": "5.413110", "convergence_key": "loss:", "ips": 19.333, "speed_unit": "samples/s", "device_num": "N1C1", "model_run_time": "0", "frame_commit": "8cc09552473b842c651ead3b9848d41827a3dbab", "frame_version": "0.0.0"}
 ```
 
 训练日志和日志解析结果保存在benchmark_log目录下，文件组织格式如下：
diff --git a/tools/program.py b/tools/program.py
index f253e5817840b3aa2ad9d647fd65bac402e2d88c..e7393f7de156333f469e4268c74b9b473e881143 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -283,7 +283,7 @@ def train(config,
                 eta_sec_format = str(datetime.timedelta(seconds=int(eta_sec)))
                 strs = 'epoch: [{}/{}], global_step: {}, {}, avg_reader_cost: ' \
                        '{:.5f} s, avg_batch_cost: {:.5f} s, avg_samples: {}, ' \
-                       'ips: {:.5f} , eta: {}'.format(
+                       'ips: {:.5f} samples/s, eta: {}'.format(
                     epoch, epoch_num, global_step, logs,
                     train_reader_cost / print_batch_step,
                     train_batch_cost / print_batch_step,