diff --git a/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_cn.md b/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_cn.md
old mode 100644
new mode 100755
index e40afcf3f4cc311747de9be5cbe9eacc2ca44175..a924943907fd208fbe864ddd5f54c2805124e35c
--- a/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_cn.md
+++ b/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_cn.md
@@ -60,9 +60,19 @@ python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=time
 ## 分布式使用
 一般来说，分布式的训练程序都会有两种程序：pserver和trainer。我们提供了把pserver和trainer的profile日志用timeline来显示的方式。
 
-1. trainer打开方式与[本地使用](#local)部分的第1步相同
+1. trainer打开方式与[本地使用](#local)部分的第1步基本相同，但因为存在多个trainer，需要对每个trainer做区分。例如：
+    ```python
+    # or other method to get the unique id of the current trainer
+    trainer_id = int(os.environ.get('PADDLE_TRAINER_ID'))
+
+    if pass_id == 0 and batch_id == 5:
+        profiler.start_profiler("All")
+    elif pass_id == 0 and batch_id == 10:
+        profiler.stop_profiler("total", "/tmp/profile_"+ str(trainer_id))
+        
+    ```
 
-1. pserver可以通过加两个环境变量打开profile，例如：
+2. pserver可以通过加两个环境变量打开profile，例如：
 ```
 FLAGS_rpc_server_profile_period=10 FLAGS_rpc_server_profile_path=./tmp/pserver python train.py
 ```
diff --git a/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_en.md b/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_en.md
old mode 100644
new mode 100755
index fb51802a168452a0649ebbcd0a6f4d37c07ea823..3ba1a8295840ae72563a0edd1bce2a22ac269941
--- a/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_en.md
+++ b/doc/fluid/advanced_guide/performance_improving/analysis_tools/timeline_en.md
@@ -62,7 +62,17 @@ python Paddle/tools/timeline.py --profile_path=/tmp/profile --timeline_path=time
 ## Distributed
 This tool can support distributed train programs(pserver and trainer) too.
 
-1. Open traniner profiler just like how to use in [local](#local).
+1. Open traniner profiler just like how to use in [local](#local), but remember to adjust the path of profile to each trainer, since there maybe more than one trainer in the same node.
+    ```python
+    # or other method to get the unique id of the current trainer
+    trainer_id = int(os.environ.get('PADDLE_TRAINER_ID'))
+
+    if pass_id == 0 and batch_id == 5:
+        profiler.start_profiler("All")
+    elif pass_id == 0 and batch_id == 10:
+        profiler.stop_profiler("total", "/tmp/profile_"+ str(trainer_id))
+
+    ```
 
 2. Open pserver profiler: add two environment variables, e.g.:
 ```