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!154 add multiple scalars comparision into visualization tutorials 

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tutorials/source_en/advanced_use/visualization_tutorials.md
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......@@ -8,15 +8,17 @@
- [Preparing the Training Script](#preparing-the-training-script)
- [MindInsight Commands](#mindinsight-commands)
- [Visualization Components](#visualization-components)
- [Computational Graph Visualization](#computational-graph-visualization)
- [Scalar Visualization](#scalar-visualization)
- [Image Visualization](#image-visualization)
- [Model Lineage Visualization](#model-lineage-visualization)
- [Dataset Graph Visualization](#dataset-graph-visualization)
- [Dataset Lineage Visualization](#dataset-lineage-visualization)
- [Parameter Distribution](#parameter-distribution)
- [Performance Profiler](#performance-profiler)
- [Operator Performance Analysis](#operator-performance-analysis)
- [Training Dashboard](#training-dashboard)
- [Scalar Visualization](#scalar-visualization)
- [Parameter Distribution Visualization](#parameter-distribution-visualization)
- [Computational Graph Visualization](#computational-graph-visualization)
- [Dataset Graph Visualization](#dataset-graph-visualization)
- [Image Visualization](#image-visualization)
- [Model Lineage](#model-lineage)
- [Dataset Lineage](#dataset-lineage)
- [Scalars Comparision](#scalars-comparision)
- [Performance Profiler](#performance-profiler)
- [Operator Performance Analysis](#operator-performance-analysis)
- [Specifications](#specifications)
<!-- /TOC -->
......@@ -238,37 +240,20 @@ gunicorn <PID> <USER> <FD> <TYPE> <DEVICE> <SIZE/OFF> <NODE> <WORKSPACE>
```
## Visualization Components
### Computational Graph Visualization
Computational graph visualization is used to display the graph structure, data flow direction, and control flow direction of a computational graph. It supports visualization of summary log files and pb files generated by `save_graphs` configuration in `context`.
![graph.png](./images/graph.png)
### Training Dashboard
Figure 1: Computational graph display area
Figure 1 shows the network structure of a computational graph. As shown in the figure, select an operator in the area of the display area. The operator has two inputs and one outputs (the solid line indicates the data flow direction of the operator).
![graph_sidebar.png](./images/graph_sidebar.png)
Figure 2 Computational graph function area
Figure 2 shows the function area of the computational graph, including:
* File selection box: View the computational graphs of different files.
* Search box: Enter a node name and press Enter to view the node.
* Thumbnail: Display the thumbnail of the entire network structure. When viewing an extra large image structure, you can view the currently browsed area.
* Node information: Display the basic information of the selected node, including the node name, properties, input node, and output node.
* Legend: Display the meaning of each icon in the computational graph.
Access the Training Dashboard by selecting a specific training from the training list.
### Scalar Visualization
#### Scalar Visualization
Scalar visualization is used to display the change trend of scalars during training.
![scalar.png](./images/scalar.png)
Figure 3: Scalar trend chart
Figure 1: Scalar trend chart
Figure 3 shows a change process of loss values during the neural network training. The horizontal coordinate indicates the training step, and the vertical coordinate indicates the loss value.
Figure 1 shows a change process of loss values during the neural network training. The horizontal coordinate indicates the training step, and the vertical coordinate indicates the loss value.
Buttons from left to right in the upper right corner of the figure are used to display the chart in full screen, switch the Y-axis scale, enable or disable the rectangle selection, roll back the chart step by step, and restore the chart.
......@@ -278,12 +263,11 @@ Buttons from left to right in the upper right corner of the figure are used to d
- Step-by-step Rollback: Cancel operations step by step after continuously drawing rectangles to select and zooming in the same area.
- Restore Chart: Restore a chart to the original state.
![scalar_select.png](./images/scalar_select.png)
Figure 4: Scalar visualization function area
Figure 2: Scalar visualization function area
Figure 4 shows the scalar visualization function area, which allows you to view scalar information by selecting different tags, different dimensions of the horizontal axis, and smoothness.
Figure 2 shows the scalar visualization function area, which allows you to view scalar information by selecting different tags, different dimensions of the horizontal axis, and smoothness.
- Tag: Select the required tags to view the corresponding scalar information.
- Horizontal Axis: Select any of Step, Relative Time, and Absolute Time as the horizontal axis of the scalar curve.
......@@ -292,128 +276,181 @@ Figure 4 shows the scalar visualization function area, which allows you to view
![scalar_compound.png](./images/scalar_compound.png)
Figure 5: Scalar synthesis of Accuracy and Loss curves
Figure 3: Scalar synthesis of Accuracy and Loss curves
Figure 3 shows the scalar synthesis of the Accuracy and Loss curves. The function area of scalar synthesis is similar to that of scalar visualization. Different from the scalar visualization function area, the scalar synthesis function allows you to select a maximum of two tags at a time to synthesize and display their curves.
#### Parameter Distribution Visualization
The parameter distribution in a form of a histogram displays tensors specified by a user.
![histogram.png](./images/histogram.png)
Figure 4: Histogram
Figure 4 shows tensors recorded by a user in a form of a histogram. Click the upper right corner to zoom in the histogram.
![histogram_func.png](./images/histogram_func.png)
Figure 5: Function area of the parameter distribution histogram
Figure 5 shows the function area of the parameter distribution histogram, including:
- Tag selection: Select the required tags to view the corresponding histogram.
- Vertical axis: Select any of `Step`, `Relative time`, and `Absolute time` as the data displayed on the vertical axis of the histogram.
- Angle of view: Select either `Front` or `Top`. `Front` view refers to viewing the histogram from the front view. In this case, data between different steps is overlapped. `Top` view refers to viewing the histogram at an angle of 45 degrees. In this case, data between different steps can be presented.
#### Computational Graph Visualization
Computational graph visualization is used to display the graph structure, data flow direction, and control flow direction of a computational graph. It supports visualization of summary log files and pb files generated by `save_graphs` configuration in `context`.
![graph.png](./images/graph.png)
Figure 6: Computational graph display area
Figure 6 shows the network structure of a computational graph. As shown in the figure, select an operator in the area of the display area. The operator has two inputs and one outputs (the solid line indicates the data flow direction of the operator).
![graph_sidebar.png](./images/graph_sidebar.png)
Figure 7: Computational graph function area
Figure 7 shows the function area of the computational graph, including:
* File selection box: View the computational graphs of different files.
* Search box: Enter a node name and press Enter to view the node.
* Thumbnail: Display the thumbnail of the entire network structure. When viewing an extra large image structure, you can view the currently browsed area.
* Node information: Display the basic information of the selected node, including the node name, properties, input node, and output node.
* Legend: Display the meaning of each icon in the computational graph.
#### Dataset Graph Visualization
Dataset graph visualization is used to display data processing and augmentation information of a single model training.
![data_function.png](./images/data_function.png)
Figure 8: Dataset graph function area
Figure 8 shows the dataset graph function area which includes the following content:
Figure 5 shows the scalar synthesis of the Accuracy and Loss curves. The function area of scalar synthesis is similar to that of scalar visualization. Different from the scalar visualization function area, the scalar synthesis function allows you to select a maximum of two tags at a time to synthesize and display their curves.
* Legend: Display the meaning of each icon in the data lineage graph.
* Data Processing Pipeline: Display the data processing pipeline used for training. Select a single node in the graph to view details.
* Node Information: Display basic information about the selected node, including names and parameters of the data processing and augmentation operators.
### Image Visualization
#### Image Visualization
Image visualization is used to display images specified by users.
![image.png](./images/image_vi.png)
Figure 6: Image visualization
Figure 9: Image visualization
Figure 6 shows how to view images of different steps by sliding the Step slider.
Figure 9 shows how to view images of different steps by sliding the Step slider.
![image_function.png](./images/image_function.png)
Figure 7: Image visualization function area
Figure 10: Image visualization function area
Figure 7 shows the function area of image visualization. You can view image information by selecting different tags, brightness, and contrast.
Figure 10 shows the function area of image visualization. You can view image information by selecting different tags, brightness, and contrast.
- Tag: Select the required tags to view the corresponding image information.
- Brightness Adjustment: Adjust the brightness of all displayed images.
- Contrast Adjustment: Adjust the contrast of all displayed images.
### Model Lineage Visualization
### Model Lineage
Model lineage visualization is used to display the parameter information of all training models.
![image.png](./images/lineage_label.png)
Figure 8: Model parameter selection area
Figure 11: Model parameter selection area
Figure 8 shows the model parameter selection area, which lists the model parameter tags that can be viewed. You can select required tags to view the corresponding model parameters.
Figure 11 shows the model parameter selection area, which lists the model parameter tags that can be viewed. You can select required tags to view the corresponding model parameters.
![image.png](./images/lineage_model_chart.png)
Figure 9: Model lineage function area
Figure 12: Model lineage function area
Figure 9 shows the model lineage function area, which visualizes the model parameter information. You can select a specific area in the column to display the model information within the area.
Figure 12 shows the model lineage function area, which visualizes the model parameter information. You can select a specific area in the column to display the model information within the area.
![image.png](./images/lineage_model_table.png)
Figure 10: Model list
Figure 10 shows all model information in groups. You can sort the model information in ascending or descending order by specified column.
### Dataset Graph Visualization
Dataset graph visualization is used to display data processing and augmentation information of a single model training.
![data_function.png](./images/data_function.png)
Figure 11: Dataset graph function area
Figure 11 shows the dataset graph function area which includes the following content:
Figure 13: Model list
* Legend: Display the meaning of each icon in the data lineage graph.
* Data Processing Pipeline: Display the data processing pipeline used for training. Select a single node in the graph to view details.
* Node Information: Display basic information about the selected node, including names and parameters of the data processing and augmentation operators.
Figure 13 shows all model information in groups. You can sort the model information in ascending or descending order by specified column.
### Dataset Lineage Visualization
### Dataset Lineage
Dataset lineage visualization is used to display data processing and augmentation information of all model trainings.
![data_label.png](./images/data_label.png)
Figure 12: Data processing and augmentation operator selection area
Figure 14: Data processing and augmentation operator selection area
Figure 12 shows the data processing and augmentation operator selection area, which lists names of data processing and augmentation operators that can be viewed. You can select required tags to view related parameters.
Figure 14 shows the data processing and augmentation operator selection area, which lists names of data processing and augmentation operators that can be viewed. You can select required tags to view related parameters.
![data_chart.png](./images/data_chart.png)
Figure 13: Dataset lineage function area
Figure 15: Dataset lineage function area
Figure 13 shows the dataset lineage function area, which visualizes the parameter information used for data processing and augmentation. You can select a specific area in the column to display the parameter information within the area.
Figure 15 shows the dataset lineage function area, which visualizes the parameter information used for data processing and augmentation. You can select a specific area in the column to display the parameter information within the area.
![data_table.png](./images/data_table.png)
Figure 14: Dataset lineage list
Figure 16: Dataset lineage list
Figure 14 shows the data processing and augmentation information of all model trainings.
Figure 16 shows the data processing and augmentation information of all model trainings.
### Parameter Distribution
### Scalars Comparision
The parameter distribution in a form of a histogram displays tensors specified by a user.
Scalars Comparision can be used to compare scalar curves between multiple trainings
![histogram.png](./images/histogram.png)
![multi_scalars.png](./images/multi_scalars.png)
Figure 15: Histogram
Figure 17: Scalars comparision curve area
Figure 15 shows tensors recorded by a user in a form of a histogram. Click the upper right corner to zoom in the histogram.
Figure 17 shows the scalar curve comparision between multiple trainings. The horizontal coordinate indicates the training step, and the vertical coordinate indicates the scalar value.
![histogram_func.png](./images/histogram_func.png)
Buttons from left to right in the upper right corner of the figure are used to display the chart in full screen, switch the Y-axis scale, enable or disable the rectangle selection, roll back the chart step by step, and restore the chart.
- Full-screen Display: Display the scalar curve in full screen. Click the button again to restore it.
- Switch Y-axis Scale: Perform logarithmic conversion on the Y-axis coordinate.
- Enable/Disable Rectangle Selection: Draw a rectangle to select and zoom in a part of the chart. You can perform rectangle selection again on the zoomed-in chart.
- Step-by-step Rollback: Cancel operations step by step after continuously drawing rectangles to select and zooming in the same area.
- Restore Chart: Restore a chart to the original state.
Figure 16: Function area of the parameter distribution histogram
![multi_scalars_select.png](./images/multi_scalars_select.png)
Figure 16 shows the function area of the parameter distribution histogram, including:
Figure 18: Scalars comparision function area
- Tag selection: Select the required tags to view the corresponding histogram.
- Vertical axis: Select any of `Step`, `Relative time`, and `Absolute time` as the data displayed on the vertical axis of the histogram.
- Angle of view: Select either `Front` or `Top`. `Front` view refers to viewing the histogram from the front view. In this case, data between different steps is overlapped. `Top` view refers to viewing the histogram at an angle of 45 degrees. In this case, data between different steps can be presented.
Figure 18 shows the scalars comparision function area, which allows you to view scalar information by selecting different trainings or tags, different dimensions of the horizontal axis, and smoothness.
## Performance Profiler
- Training: Select or filter the required trainings to view the corresponding scalar information.
- Tag: Select the required tags to view the corresponding scalar information.
- Horizontal Axis: Select any of Step, Relative Time, and Absolute Time as the horizontal axis of the scalar curve.
- Smoothness: Adjust the smoothness to smooth the scalar curve.
### Performance Profiler
### Operator Performance Analysis
Access the Performance Profiler by selecting a specific training from the training list.
#### Operator Performance Analysis
The operator performance analysis component is used to display the execution time of the operators during MindSpore run.
![op_type_statistics.png](./images/op_type_statistics.PNG)
Figure 17: Statistics for Operator Types
Figure 19: Statistics for Operator Types
Figure 17 displays the statistics for the operator types, including:
Figure 19 displays the statistics for the operator types, including:
- Choose pie or bar graph to show the proportion time occupied by each operator type. The time of one operator type is calculated by accumulating the execution time of operators belong to this type.
- Display top 20 operator types with longest execution time, show the proportion and execution time (ms) of each operator type.
![op_statistics.png](./images/op_statistics.PNG)
Figure 18: Statistics for Operators
Figure 20: Statistics for Operators
Figure 18 displays the statistics table for the operators, including:
Figure 20 displays the statistics table for the operators, including:
- Choose All: Display statistics for the operators, including operator name、type、excution time、full scope time、information etc. The table will be sorted by execution time by default.
- Choose Type: Display statistics for the operator types, including operator type name、execution time、execution frequency and proportion of total time. Users can click on each line, querying for all the operators belong to this type.
......@@ -429,4 +466,9 @@ To limit memory usage, MindInsight limits the number of tags and steps:
- There are 10 steps at most for each image tag in each training dashboard. When steps exceed limit, MindInsight will sample steps randomly to meet this limit.
- There are 50 steps at most for each parameter distribution(histogram) tag in each training dashboard. When steps exceed limit, MindInsight will sample steps randomly to meet this limit.
To ensure performance, MindInsight implements scalars comparision with the cache mechanism and the following restrictions:
- The scalars comparision supports only for trainings in cache.
- The maximum of 15 latest trainings (sorted by modification time) can be retained in the cache.
- The maximum of 5 trainings can be selected for scalars comparision at the same time.
To limit the data size generated by the Profiler, MindInsight suggests that for large neural network, the profiled steps should better below 10.
tutorials/source_zh_cn/advanced_use/visualization_tutorials.md
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- [停止服务](#停止服务)
- [查看服务进程信息](#查看服务进程信息)
- [可视化组件](#可视化组件)
- [计算图可视化](#计算图可视化)
- [标量可视化](#标量可视化)
- [图像可视化](#图像可视化)
- [模型溯源可视化](#模型溯源可视化)
- [数据图可视化](#数据图可视化)
- [数据溯源可视化](#数据溯源可视化)
- [参数分布图](#参数分布图)
- [性能调试](#性能调试)
- [算子性能分析](#算子性能分析)
- [训练看板](#训练看板)
- [标量可视化](#标量可视化)
- [参数分布图可视化](#参数分布图可视化)
- [计算图可视化](#计算图可视化)
- [数据图可视化](#数据图可视化)
- [图像可视化](#图像可视化)
- [模型溯源](#模型溯源)
- [数据溯源](#数据溯源)
- [对比看板](#对比看板)
- [性能调试](#性能调试)
- [算子性能分析](#算子性能分析)
- [规格](#规格)
<!-- /TOC -->
......@@ -101,7 +103,7 @@ class MyOptimizer(Optimizer):
self.histogram_summary(self.weight_names[0], self.paramters[0])
# Record gradient
self.histogram_summary(self.weight_names[0] + ".gradient", grads[0])
......
......@@ -200,7 +202,7 @@ mindinsight start [-h] [--config <CONFIG>] [--workspace <WORKSPACE>]
[--port <PORT>] [--reload-interval <RELOAD_INTERVAL>]
[--summary-base-dir <SUMMARY_BASE_DIR>]
```
参数含义如下:
- `-h, --help` : 显示启动命令的帮助信息。
......@@ -244,37 +246,20 @@ gunicorn <PID> <USER> <FD> <TYPE> <DEVICE> <SIZE/OFF> <NODE> <WORKSPACE>
```
## 可视化组件
### 计算图可视化
计算图可视化用于展示计算图的图结构，数据流以及控制流的走向，支持展示summary日志文件与通过`context`的`save_graphs`参数导出的`pb`文件。
![graph.png](./images/graph.png)
图1：计算图展示区
图1展示了计算图的网络结构。如图中所展示的，在展示区中，选中其中一个算子（图中圈红算子），可以看到该算子有两个输入和一个输出（实线代表算子的数据流走向）。
![graph_sidebar.png](./images/graph_sidebar.png)
图2：计算图功能区
图2展示了计算图可视化的功能区，包含以下内容：
### 训练看板
* 文件选择框: 可以选择查看不同文件的计算图。
* 搜索框：可以对节点进行搜索，输入节点名称点击回车，即可展示该节点。
* 缩略图：展示整个网络图结构的缩略图，在查看超大图结构时，方便查看当前浏览的区域。
* 节点信息：展示选中的节点的基本信息，包括节点的名称、属性、输入节点、输出节点等信息。
* 图例：展示的是计算图中各个图标的含义。
用户从训练列表中选择指定的训练，进入训练看板。
### 标量可视化
#### 标量可视化
标量可视化用于展示训练过程中，标量的变化趋势情况。
![scalar.png](./images/scalar.png)
图3：标量趋势图
图1：标量趋势图
图3展示了神经网络在训练过程中损失值的变化过程。横坐标是训练步骤，纵坐标是损失值。
图1展示了神经网络在训练过程中损失值的变化过程。横坐标是训练步骤，纵坐标是损失值。
图中右上角有几个按钮功能，从左到右功能分别是全屏展示，切换Y轴比例，开启/关闭框选，分步回退和还原图形。
......@@ -284,12 +269,11 @@ gunicorn <PID> <USER> <FD> <TYPE> <DEVICE> <SIZE/OFF> <NODE> <WORKSPACE>
- 分步回退是指对同一个区域连续框选并放大查看时，可以逐步撤销操作。
- 还原图形是指进行了多次框选后，点击此按钮可以将图还原回原始状态。
![scalar_select.png](./images/scalar_select.png)
图4：标量可视化功能区
图2：标量可视化功能区
图4展示的标量可视化的功能区，提供了根据选择不同标签，水平轴的不同维度和平滑度来查看标量信息的功能。
图2展示的标量可视化的功能区，提供了根据选择不同标签，水平轴的不同维度和平滑度来查看标量信息的功能。
- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的标量信息。
- 水平轴：可以选择“步骤”、“相对时间”、“绝对时间”中的任意一项，来作为标量曲线的水平轴。
......@@ -298,126 +282,181 @@ gunicorn <PID> <USER> <FD> <TYPE> <DEVICE> <SIZE/OFF> <NODE> <WORKSPACE>
![scalar_compound.png](./images/scalar_compound.png)
图5：Accuracy和Loss的标量合成图
图3：Accuracy和Loss的标量合成图
图3展示Accuracy曲线和Loss曲线的标量合成图。标量合成的功能区与标量可视化的功能区相似。其中与标量可视化功能区不一样的地方，在于标签选择时，标量合成功能最多只能同时选择两个标签，将其曲线合成并展示。
#### 参数分布图可视化
参数分布图用于将用户所指定的张量以直方图的形式进行展示。
![histogram.png](./images/histogram.png)
图4: 直方图展示
图4将用户所记录的张量以直方图的形式进行展示。点击图中右上角，可以将图放大。
![histogram_func.png](./images/histogram_func.png)
图5: 参数分布图功能区
图5展示参数分布图的功能区，包含以下内容:
- 标签选择：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的直方图。
- 纵轴：可以选择`步骤`、`相对时间`、`绝对时间`中的任意一项，来作为直方图纵轴显示的数据。
- 视角：可以选择`正视`和`俯视`中的一种。`正视`是指从正面的角度查看直方图，此时不同步骤之间的数据会覆盖在一起。`俯视`是指偏移以45度角俯视直方图区域，这时可以呈现不同步骤之间数据的差异。
#### 计算图可视化
计算图可视化用于展示计算图的图结构，数据流以及控制流的走向，支持展示summary日志文件与通过`context`的`save_graphs`参数导出的`pb`文件。
![graph.png](./images/graph.png)
图6：计算图展示区
图6展示了计算图的网络结构。如图中所展示的，在展示区中，选中其中一个算子（图中圈红算子），可以看到该算子有两个输入和一个输出（实线代表算子的数据流走向）。
![graph_sidebar.png](./images/graph_sidebar.png)
图7：计算图功能区
图7展示了计算图可视化的功能区，包含以下内容：
* 文件选择框: 可以选择查看不同文件的计算图。
* 搜索框：可以对节点进行搜索，输入节点名称点击回车，即可展示该节点。
* 缩略图：展示整个网络图结构的缩略图，在查看超大图结构时，方便查看当前浏览的区域。
* 节点信息：展示选中的节点的基本信息，包括节点的名称、属性、输入节点、输出节点等信息。
* 图例：展示的是计算图中各个图标的含义。
#### 数据图可视化
数据图可视化用于展示单次模型训练的数据处理和数据增强信息。
![data_function.png](./images/data_function.png)
图8：数据图功能区
图5展示Accuracy曲线和Loss曲线的标量合成图。标量合成的功能区与标量可视化的功能区相似。其中与标量可视化功能区不一样的地方，在于标签选择时，标量合成功能最多只能同时选择两个标签，将其曲线合成并展示。
图8展示的数据图功能区包含以下内容：
### 图像可视化
* 图例：展示数据溯源图中各个图标的含义。
* 数据处理流水线：展示训练所使用的数据处理流水线，可以选择图中的单个节点查看详细信息。
* 节点信息：展示选中的节点的基本信息，包括使用的数据处理和增强算子的名称、参数等。
#### 图像可视化
图像可视化用于展示用户所指定的图片。
![image.png](./images/image_vi.png)
图6：图像可视化
图9：图像可视化
图6展示通过滑动图中“步骤”滑条，查看不同步骤的图片。
图9展示通过滑动图中“步骤”滑条，查看不同步骤的图片。
![image_function.png](./images/image_function.png)
图7：图像可视化功能区
图10：图像可视化功能区
图7展示图像可视化的功能区，提供了选择查看不同标签，不同亮度和不同对比度来查看图片信息。
图10展示图像可视化的功能区，提供了选择查看不同标签，不同亮度和不同对比度来查看图片信息。
- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的图片信息。
- 亮度调整：可以调整所展示的所有图片亮度。
- 对比度调整：可以调整所展示的所有图片对比度。
### 模型溯源可视化
### 模型溯源
模型溯源可视化用于展示所有训练的模型参数信息。
![image.png](./images/lineage_label.png)
图8：模型参数选择区
图11：模型参数选择区
图8展示的模型参数选择区，列举了可供查看的模型参数标签。用户可以通过勾选所需的标签，查看相应的模型参数。
图11展示的模型参数选择区，列举了可供查看的模型参数标签。用户可以通过勾选所需的标签，查看相应的模型参数。
![image.png](./images/lineage_model_chart.png)
图9：模型溯源功能区
图12：模型溯源功能区
图9展示的模型溯源功能区，图像化展示了模型的参数信息。用户可以通过选择列的特定区域，展示区域范围内的模型信息。
图12展示的模型溯源功能区，图像化展示了模型的参数信息。用户可以通过选择列的特定区域，展示区域范围内的模型信息。
![image.png](./images/lineage_model_table.png)
图10：模型列表
图10分组展示所有模型信息，用户可以按指定列进行升序或降序展示模型信息。
图13：模型列表
### 数据图可视化
图13分组展示所有模型信息，用户可以按指定列进行升序或降序展示模型信息。
数据图可视化用于展示单次模型训练的数据处理和数据增强信息。
### 数据溯源
![data_function.png](./images/data_function.png)
数据溯源可视化用于展示所有训练的数据处理和数据增强信息。
图11：数据图功能区
![data_label.png](./images/data_label.png)
图11展示的数据图功能区包含以下内容：
图14：数据处理和增强算子选择区
* 图例：展示数据溯源图中各个图标的含义。
* 数据处理流水线：展示训练所使用的数据处理流水线，可以选择图中的单个节点查看详细信息。
* 节点信息：展示选中的节点的基本信息，包括使用的数据处理和增强算子的名称、参数等。
图14展示的数据处理和数据增强算子选择区，列举了可供查看的数据处理和增强算子的名称。用户可以通过勾选所需的标签，查看相应的参数等信息。
### 数据溯源可视化
![data_chart.png](./images/data_chart.png)
数据溯源可视化用于展示所有训练的数据处理和数据增强信息。
图15：数据溯源功能区
![data_label.png](./images/data_label.png)
图15展示的数据溯源功能区，图像化展示了数据处理和数据增强使用的参数信息。用户可以通过选择列的特定区域，展示区域范围内的参数信息。
图12：数据处理和增强算子选择区
![data_table.png](./images/data_table.png)
图12展示的数据处理和数据增强算子选择区，列举了可供查看的数据处理和增强算子的名称。用户可以通过勾选所需的标签，查看相应的参数等信息。
图16：数据溯源列表
![data_chart.png](./images/data_chart.png)
图16展示所有模型训练的数据处理和数据增强信息。
图13：数据溯源功能区
### 对比看板
图13展示的数据溯源功能区，图像化展示了数据处理和数据增强使用的参数信息。用户可以通过选择列的特定区域，展示区域范围内的参数信息。
对比看板可视用于多个训练之间的标量曲线对比。
![data_table.png](./images/data_table.png)
![multi_scalars.png](./images/multi_scalars.png)
图14：数据溯源列表
图17: 标量对比曲线图
图14展示所有模型训练的数据处理和数据增强信息。
图17展示了多个训练之间的标量曲线对比效果，横坐标是训练步骤，纵坐标是标量值。
### 参数分布图
图中右上角有几个按钮功能，从左到右功能分别是全屏展示，切换Y轴比例，开启/关闭框选，分步回退和还原图形。
参数分布图用于将用户所指定的张量以直方图的形式进行展示。
- 全屏展示即全屏展示该标量曲线，再点击一次即可恢复。
- 切换Y轴比例是指可以将Y轴坐标进行对数转换。
- 开启/关闭框选是指可以框选图中部分区域，并放大查看该区域， 可以在已放大的图形上叠加框选。
- 分步回退是指对同一个区域连续框选并放大查看时，可以逐步撤销操作。
- 还原图形是指进行了多次框选后，点击此按钮可以将图还原回原始状态。
![histogram.png](./images/histogram.png)
![multi_scalars_select.png](./images/multi_scalars_select.png)
图15: 直方图展示
图18：对比看板可视功能区
图15将用户所记录的张量以直方图的形式进行展示。点击图中右上角，可以将图放大。
图18展示的对比看板可视的功能区，提供了根据选择不同训练或标签，水平轴的不同维度和平滑度来进行标量对比的功能。
![histogram_func.png](./images/histogram_func.png)
- 训练: 提供了对所有训练进行多项选择的功能，用户可以通过勾选或关键字筛选所需的训练。
- 标签：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的标量信息。
- 水平轴：可以选择“步骤”、“相对时间”、“绝对时间”中的任意一项，来作为标量曲线的水平轴。
- 平滑度：可以通过调整平滑度，对标量曲线进行平滑处理。
图16: 参数分布图功能区
### 性能调试
图16展示参数分布图的功能区，包含以下内容:
用户从训练列表中选择指定的训练，进入性能调试。
- 标签选择：提供了对所有标签进行多项选择的功能，用户可以通过勾选所需的标签，查看对应的直方图。
- 纵轴：可以选择`步骤`、`相对时间`、`绝对时间`中的任意一项，来作为直方图纵轴显示的数据。
- 视角：可以选择`正视`和`俯视`中的一种。`正视`是指从正面的角度查看直方图，此时不同步骤之间的数据会覆盖在一起。`俯视`是指偏移以45度角俯视直方图区域，这时可以呈现不同步骤之间数据的差异。
#### 算子性能分析
## 性能调试
### 算子性能分析
使用算子性能分析组件可以对MindSpore运行过程中的各个算子的执行时间进行统计展示。
![op_type_statistics.png](./images/op_type_statistics.PNG)
图17： 算子类别统计分析
图19： 算子类别统计分析
图17展示了按算子类别进行统计分析的结果，包含以下内容：
图19展示了按算子类别进行统计分析的结果，包含以下内容：
- 可以选择饼图/柱状图展示各算子类别的时间占比，每个算子类别的执行时间会统计属于该类别的算子执行时间总和；
- 统计前20个占比时间最长的算子类别，展示其时间所占的百分比以及具体的执行时间（毫秒）。
![op_statistics.png](./images/op_statistics.PNG)
图18： 算子统计分析
图20： 算子统计分析
图18展示了算子性能统计表，包含以下内容：
图20展示了算子性能统计表，包含以下内容：
- 选择全部：按单个算子的统计结果进行排序展示，展示维度包括算子名称、算子类型、算子执行时间、算子全scope名称、算子信息等；默认按算子执行时间排序；
- 选择分类：按算子类别的统计结果进行排序展示，展示维度包括算子分类名称、算子类别执行时间、执行频次、占总时间的比例等。点击每个算子类别，可以进一步查看该类别下所有单个算子的统计信息；
......@@ -433,4 +472,9 @@ gunicorn <PID> <USER> <FD> <TYPE> <DEVICE> <SIZE/OFF> <NODE> <WORKSPACE>
- 每个训练看板的每个图片标签最多有10个步骤的数据。当实际步骤的数目超过这一限制时，将对数据进行随机采样，以满足这一限制。
- 每个训练看板的每个参数分布图（直方图）标签最多有50个步骤的数据。当实际步骤的数目超过这一限制时，将对数据进行随机采样，以满足这一限制。
为了控制性能测试时生成数据的大小，大型网络建议profile的step数目限制在10以内。
出于性能上的考虑，MindInsight对比看板使用缓存机制加载训练的标量曲线数据，并进行以下限制：
- 对比看板只支持在缓存中的训练进行比较标量曲线对比。
- 缓存最多保留最新（按修改时间排列）的15个训练。
- 用户最多同时对比5个训练的标量曲线。
为了控制性能测试时生成数据的大小，大型网络建议性能调试的step数目限制在10以内。
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