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wangshuide2020 e66e41006c 1. add the limitation of the number of tag in tensor visualization; 2. update the max step per tensor tag to 20; 3. support query one train_job in the interface of train_jobs. 5 years ago
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README.md

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.

  • 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

The architecture diagram of MindInsight is illustrated as follows:

MindInsight Architecture

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:

Graph visualization

The GUI of MindInsight displays the structure of neural network, the data flow and control
flow of each operator during the entire training process.

Scalar visualization

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.

Two scalar curves can be combined and displayed in one chart.

Parameter distribution

The GUI of MindInsight displays the distribution change tendency of a tensor such as weight
or gradient during the entire training process.

Image visualization

The GUI of MindInsight displays both original images and enhanced images during the entire
training process.

Model lineage visualization

The GUI of MindInsight displays the parameters and metrics of all models, such as the
learning rate, the number of samples and the loss function of each model.

Dataset Graph visualization

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.

QuickStart

See guidance

Docs

See API Reference

Community

Contributing

Welcome contributions. See our Contributor Wiki for more details.

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0

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MindInsight为MindSpore提供了简单易用的调优调试能力。在训练过程中，可以将标量、张量、图像、计算图、模型超参、训练耗时等数据记录到文件中，通过MindInsight可视化页面进行查看及分析。

SVG Text Python Vue CSV other