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GVPMindSpore / mindinsight

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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.

MindInsight Architecture

Click to view the MindInsight design document, learn more about the design. Click to view the Tutorial documentation learn more about the MindInsight tutorial.


System Environment Information Confirmation

  • The hardware platform supports Ascend, GPU and CPU.
  • Confirm that Python 3.7.5 is installed.
  • The versions of MindInsight and MindSpore must be consistent.
  • If you use source code to compile and install, the following dependencies also need to be installed:
    • Confirm that node.js 10.19.0 or later is installed.
    • Confirm that wheel 0.32.0 or later is installed.
  • All other dependencies are included in requirements.txt.

Installation Methods

You can install MindInsight either by pip or by source code.

Installation by pip

Install from PyPI:

pip install mindinsight

Install with customized version:

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindInsight/any/mindinsight-{version}-py3-none-any.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://pypi.tuna.tsinghua.edu.cn/simple
  • When the network is connected, dependency items are automatically downloaded during .whl package installation. (For details about other dependency items, see requirements.txt). In other cases, you need to manually install dependency items.
  • {version} denotes the version of MindInsight. For example, when you are downloading MindSpore 1.3.0, {version} should be 1.3.0.
  • MindInsight supports only Linux distro with x86 architecture 64-bit or ARM architecture 64-bit.

Installation by Source Code

Downloading Source Code from Gitee
git clone https://gitee.com/mindspore/mindinsight.git
Compiling MindInsight

You can choose any of the following installation methods:

  1. Run the following command in the root directory of the source code:

    cd mindinsight
    pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
    python setup.py install
  2. Build the whl package for installation.

    Enter the root directory of the source code, first execute the MindInsight compilation script in the build directory, and then execute the command to install the whl package generated in the output directory.

    cd mindinsight
    bash build/build.sh
    pip install output/mindinsight-{version}-py3-none-any.whl -i https://pypi.tuna.tsinghua.edu.cn/simple

Installation Verification

Execute the following command:

mindinsight start [--port PORT]

notes: the param --port default value is 8080

If it prompts the following information, the installation is successful:

Web address:
service start state: success

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.

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.


from mindspore import SummaryCollector
summary_collector = SummaryCollector(summary_dir='./summary_dir')
model.train(epoch=1, ds_train, callbacks=[summary_collector])

For more ways to record visual data, see the MindInsight Tutorial.

After you've collected the data, when you launch MindInsight, specify the directory in which the data has been stored.

mindinsight start --summary-base-dir ./summary_dir [--port PORT]

notes: the param --port default value is 8080

After successful startup, visit through the browser to view the web page.

Command of stopping the MindInsight service:

mindinsight stop [--port PORT]

notes: the param --port default value is 8080, you can stop the specified port MI service.

For more about MindInsight command_line,see the MindInsight Command_line.


More details about installation guide, tutorials and APIs, please see the User Documentation.



Check out how MindSpore Open Governance works.


Vulkan Vision

Vulkan Vision(V-Vision) provides an unprecedented level of detail into the execution of Vulkan applications through dynamic instrumentation. V-Vision supports analyzing AI workloads implemented using the a compute pipeline as well as traditional raster and ray-tracing Vulkan applications. To use V-Vision please refer to the build instructions. For more information, please refer to the paper published at CGO 2021.


Welcome contributions. See our Contributor Wiki for more details.

Release Notes

The release notes, see our RELEASE.


Apache License 2.0


A visual dashboard for model tuning. expand collapse

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