diff --git a/tutorials/source_en/debug/profiler.md b/tutorials/source_en/debug/profiler.md
index 84c8e58f524c8ff1bf51eccb9ba250ad57ba04e7..0d1948fb090a1fafd55a37484213465785571d63 100644
--- a/tutorials/source_en/debug/profiler.md
+++ b/tutorials/source_en/debug/profiler.md
@@ -22,7 +22,7 @@ There are five ways to collect training performance data, and the following desc
 
 ### Method 1: mindspore.Profiler Interface Enabling
 
-Add the MindSpore Profiler related interfaces in the training script, see [MindSpore Profiler parameter details](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.Profiler.html) for details.
+Add the MindSpore Profiler related interfaces in the training script, users can refer to  [MindSpore Profiler parameter details](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.Profiler.html) and [_ExperimentalConfig Parameter Details](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.profiler._ExperimentalConfig.html) to configure parameters such as profiler_level according to their data requirements.
 
 The interface supports two collection modes: CallBack mode and custom for loop mode, and supports both Graph and PyNative modes.
 
@@ -44,20 +44,22 @@ net = Net()
 
 # Configure the extensibility parameters
 experimental_config = mindspore.profiler._ExperimentalConfig(
-                        profiler_level=ProfilerLevel.Level0,
-                        aic_metrics=AicoreMetrics.AiCoreNone,
-                        l2_cache=False,
-                        mstx=False,
-                        data_simplification=False,
-                        host_sys=[HostSystem.CPU, HostSystem.MEM])
+    profiler_level=ProfilerLevel.Level0,
+    aic_metrics=AicoreMetrics.AiCoreNone,
+    l2_cache=False,
+    mstx=False,
+    data_simplification=False,
+    host_sys=[HostSystem.CPU, HostSystem.MEM]
+)
 
 # Initialize profile
 with mindspore.profiler.profile(activities=[ProfilerActivity.CPU, ProfilerActivity.NPU],
-                                    schedule=mindspore.profiler.schedule(wait=0, warmup=0, active=1,
-                                            repeat=1, skip_first=0),
-                                    on_trace_ready=mindspore.profiler.tensorboard_trace_handler("./data"),
-                                    profile_memory=False,
-                                    experimental_config=experimental_config) as prof:
+                                schedule=mindspore.profiler.schedule(wait=0, warmup=0, active=1,
+                                    repeat=1, skip_first=0),
+                                # Disable online parsing by setting analyse_flag=False in tensorboard_trace_handler
+                                on_trace_ready=mindspore.profiler.tensorboard_trace_handler("./data"),
+                                profile_memory=False,
+                                experimental_config=experimental_config) as prof:
         for step in range(steps):
             train(net)
             # Call step collection
@@ -75,14 +77,9 @@ As illustrated in the following figure, schedule has 5 configurable parameters:
 
 ![schedule.png](../../source_zh_cn/debug/images/schedule.png)
 
-For example: The model training consists of 100 steps. The schedule is configured as schedule = schedule(skip_first=10,
-wait=10, warmup=5, active=5, repeat=2), indicating that the first 10 steps are skipped.
-Starting from the 11th step, in the first repeat, 10 steps will be waited for, 5 steps of preheating will be executed,
-and finally the performance data of a total of 5 steps from the 26th to the 30th will be collected.
-In the second repeat, it will continue to wait for 10 steps, perform 5 steps of preheating, and finally collect the
-performance data of a total of 5 steps from step 46 to step 50.
+For example: If there are 100 steps (0-99) in model training and the schedule is configured as `schedule(skip_first=10, wait=10, warmup=5, active=5, repeat=2)` . Profiler will first skip the first 10 steps (0-9). Starting from step 10, the first repeat will wait for 10 steps (10-19), warm up for 5 steps (20-24), and finally collect performance data for 5 steps (25-29). The second repeat will again wait for 10 steps (30-39), warm up for 5 steps (40-44), and finally collect performance data for 5 steps (45-49).
 
-> - Profiler generates multiple performance data files in the same directory based on the repeat count. Each repeat corresponds to a folder containing performance data collected from all active steps in that repeat. When repeat is configured to 0, the specific number of repetitions is determined by the total number of steps, continuously repeating the wait-warmup-active cycle until all steps are completed.
+> - In single-card scenarios, profiler generates multiple performance data files in the same directory based on the repeat count. Each repeat corresponds to a folder containing performance data collected from all active steps in that repeat. In multi-card scenarios, each card generates performance data independently, and the data from each card is divided into multiple parts based on the repeat count. When repeat is configured to 0, the specific number of repetitions is determined by the total number of steps, continuously repeating the wait-warmup-active cycle until all steps are completed.
 > - The schedule needs to be used with [mindspore.profiler.profile.step](https://www.mindspore.cn/docs/en/master/api_python/mindspore/mindspore.profiler.profile.html#mindspore.profiler.profile.step) interface. If you only configure schedule without using mindspore.profiler.profile.step interface to collect data, all collected data will belong to step 0. Therefore, performance data files will only be generated when step 0 corresponds to active (wait, warmup, skip_first are all set to 0).
 
 #### CallBack Mode Collection Example
diff --git a/tutorials/source_zh_cn/debug/profiler.md b/tutorials/source_zh_cn/debug/profiler.md
index 88a1e10683825b69a9f2f8ca2ef1a82f7b0b4f68..30f147a72976a3dadcd765b27f04ae2fe31aaad5 100644
--- a/tutorials/source_zh_cn/debug/profiler.md
+++ b/tutorials/source_zh_cn/debug/profiler.md
@@ -22,7 +22,7 @@
 
 ### 方式一：mindspore.profiler.profile接口使能
 
-在训练脚本中添加MindSpore profile相关接口，profile接口详细介绍请参考[MindSpore profile参数详解](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.profiler.profile.html)。
+在训练脚本中添加MindSpore profile相关接口，用户可以参考[MindSpore profile参数详解](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.profiler.profile.html)和[_ExperimentalConfig可扩展参数详解](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.profiler._ExperimentalConfig.html)，针对自己的数据需求配置采集性能数据的级别等参数。
 
 该接口支持两种采集方式：自定义for循环方式和CallBack方式，且在Graph和PyNative两种模式下都支持。
 
@@ -44,20 +44,22 @@ net = Net()
 
 # 配置可扩展参数
 experimental_config = mindspore.profiler._ExperimentalConfig(
-                        profiler_level=ProfilerLevel.Level0,
-                        aic_metrics=AicoreMetrics.AiCoreNone,
-                        l2_cache=False,
-                        mstx=False,
-                        data_simplification=False,
-                        host_sys=[HostSystem.CPU, HostSystem.MEM])
+    profiler_level=ProfilerLevel.Level0,
+    aic_metrics=AicoreMetrics.AiCoreNone,
+    l2_cache=False,
+    mstx=False,
+    data_simplification=False,
+    host_sys=[HostSystem.CPU, HostSystem.MEM]
+)
 
 # 初始化profile
 with mindspore.profiler.profile(activities=[ProfilerActivity.CPU, ProfilerActivity.NPU],
-                                    schedule=mindspore.profiler.schedule(wait=0, warmup=0, active=1,
-                                            repeat=1, skip_first=0),
-                                    on_trace_ready=mindspore.profiler.tensorboard_trace_handler("./data"),
-                                    profile_memory=False,
-                                    experimental_config=experimental_config) as prof:
+                                schedule=mindspore.profiler.schedule(wait=0, warmup=0, active=1,
+                                    repeat=1, skip_first=0),
+                                # 可以通过配置tensorboard_trace_handler的参数analyse_flag为False关闭在线解析
+                                on_trace_ready=mindspore.profiler.tensorboard_trace_handler("./data"),
+                                profile_memory=False,
+                                experimental_config=experimental_config) as prof:
         for step in range(steps):
             train(net)
             # 调用step采集
@@ -77,11 +79,9 @@ with mindspore.profiler.profile(activities=[ProfilerActivity.CPU, ProfilerActivi
 
 ![schedule.png](./images/schedule.png)
 
-例如：模型训练共100个step，schedule配置为schedule = schedule(skip_first=10, wait=10, warmup=5, active=5, repeat=2)，表示跳过前10个step，
-从第11个step开始，在第1个repeat中将等待10个step，执行5个step的预热，最终采集第26~第30个step（一共5个step）的性能数据，
-在第2个repeat中将继续等待10个step，执行5个step的预热，最终采集第46个~第50个step（一共5个step）的性能数据。
+例如：模型训练共100个step（0-99），此时配置schedule为 `schedule(skip_first=10, wait=10, warmup=5, active=5, repeat=2)` 。那么profiler将会先跳过前10个step（0-9）。从step 10开始，第1个repeat将等待10个step（10-19），预热5个step（20-24），最终采集5个step（25-29）的性能数据。第2个repeat重复等待10个step（30-39），预热5个step（40-44），最终采集5个step（45-49）的性能数据。
 
-> - profiler根据repeat次数在同一目录下生成多份性能数据。每个repeat对应一个文件夹，包含该repeat中所有active step采集到的性能数据。当repeat配置为0时，表示重复执行的具体次数由总step数确定，不断重复wait-warmup-active直到所有step执行完毕。
+> - 在单卡场景下，profiler根据repeat次数在同一目录下生成多份性能数据。每个repeat对应一个文件夹，包含该repeat中所有active step采集到的性能数据。在多卡场景下，每张卡会独立生成性能数据，每张卡的数据都会根据repeat次数分成多份。当repeat配置为0时，表示重复执行的具体次数由总step数确定，不断重复wait-warmup-active直到所有step执行完毕。
 > - schedule需要配合[mindspore.profiler.profile.step](https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/mindspore.profiler.profile.html#mindspore.profiler.profile.step)接口使用，如果配置了schedule而没有调用mindspore.profiler.profile.step接口进行数据采集，则profiler数据采集区间的所有数据都属于第0个step，因此只有在第0个step对应active（wait、warmup、skip_first都配置为0）时，才会生成性能数据文件。
 
 #### CallBack方式采集样例