MindInsight provides MindSpore with easy-to-use debugging and tuning capabilities. It
enables users to visualize the experiments. The features of MindInsight are as follows.
[简体中文](./README.md)
- Visualization of training process:
Provide visualization of training process information,
such as computation graph, training process metrics, etc.
- Traceability of training result:
Provide visualization of model parameters information,
such as training data, model accuracy, etc.
- Visualization of training performance:
Provide visualization of training performance information, such as operator execution time,
data input pipeline performance, etc.
# Index
-[More about MindInsight](#more-about-mindinsight)
-[Introduction ](#introduction)
-[Installation](#installation)
-[QuickStart](#quickstart)
-[Docs](#docs)
-[Community](#community)
-[Governance](#governance)
-[Communication](#communication)
-[Contributing](#contributing)
-[Release Notes](#release-notes)
-[License](#license)
# More about MindInsight
The architecture diagram of MindInsight is illustrated as follows:
## Introduction
MindInsight provides MindSpore with easy-to-use debugging and tuning capabilities. During the training, data such as scalar, tensor, image, computational graph, model hyper parameter and training’s execution time can be recorded in the file for viewing and analysis through the visual page of MindInsight.
Click to view the [Design document](https://www.mindspore.cn/docs/en/master/design.html)，learn more about the design.
Click to view the [Tutorial documentation](https://www.mindspore.cn/tutorial/en/master/advanced_use/visualization_tutorials.html) learn more about the MindInsight tutorial.
## Summary log file
The summary log file consists of a series of operation events. Each event contains
the necessary data for visualization.
MindSpore uses the Callback mechanism to record graph, scalar, image and model
information into summary log file.
- The scalar and image is recorded by Summary operator.
- The computation graph is recorded by SummaryRecord after it was compiled.
- The model parameters is recorded by TrainLineage or EvalLineage.
MindInsight provides the capability to analyze summary log files and visualize
relative information.
## Visualization
MindInsight provides users with a full-process visualized GUI during
AI development, in order to help model developers to improve the model
precision efficiently.
MindInsight has the following visualization capabilities:
## Installation
Download whl package from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
The GUI of MindInsight displays the structure of neural network, the data flow and control
flow of each operator during the entire training process.
For more details on how to install MindInsight, click on the MindInsight section of the [installation tutorial](https://www.mindspore.cn/install/en).
### Scalar visualization
## Quick Start
Before using MindInsight, the data in the training process should be recorded. When starting MindInsight, the directory of the saved data should be specified. After successful startup, the data can be viewed through the web page. Here is a brief introduction to recording training data, as well as starting and stopping MindInsight.
The GUI of MindInsight displays the change tendency of a specific scalar during the entire
training process, such as loss value and accuracy rate of each iteration.
[SummaryCollector](https://www.mindspore.cn/api/en/master/api/python/mindspore/mindspore.train.html?highlight=summarycollector#mindspore.train.callback.SummaryCollector) is the interface MindSpore provides for a quick and easy collection of common data about computational graphs, loss values, learning rates, parameter weights, and so on. Below is an example of using `SummaryCollector` for data collection, specifying the directory where the data is stored in `./summary_dir`.
```
...
Two scalar curves can be combined and displayed in one chart.
from mindspore.train.callback import SummaryCollector
For more ways to record visual data, see the [MindInsight Tutorial](https://www.mindspore.cn/tutorial/en/master/advanced_use/visualization_tutorials.html).
The GUI of MindInsight displays the distribution change tendency of a tensor such as weight
or gradient during the entire training process.
After you've collected the data, when you launch MindInsight, specify the directory in which the data has been stored.
The GUI of MindInsight displays the pipeline of dataset processing and augmentation.
### Dataset Lineage visualization
The GUI of MindInsight displays the parameters and operations of the dataset processing and augmentation.
### Performance visualization
The GUI of MindInsight displays the performance data of the neural networks.
# Installation
See [Install MindInsight](https://www.mindspore.cn/install/en).
# QuickStart
See [guidance](https://www.mindspore.cn/tutorial/en/master/advanced_use/visualization_tutorials.html)
# Docs
See [API Reference](https://www.mindspore.cn/api/en/master/index.html)
# Community
-[MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/enQtOTcwMTIxMDI3NjM0LTNkMWM2MzI5NjIyZWU5ZWQ5M2EwMTQ5MWNiYzMxOGM4OWFhZjI4M2E5OGI2YTg3ODU1ODE2Njg1MThiNWI3YmQ) - Communication platform for developers.
# Contributing
Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for more details.
# Release Notes
## Contributing
Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for
