Documentation

Key Functions

Project Management

MindStudio supports functions including project creation, opening, closing, and deletion, as well as project directory addition and project property setting.

Analysis and Migration

  • X2MindSpore migrates PyTorch and TensorFlow training scripts to code that can run on MindSpore.
  • PyTorch GPU2Ascend migrates PyTorch training scripts from GPU platforms to the Ascend NPU platform.

Model Training

MindStudio runs the training framework, submits the script, dataset, and parameters to be executed by the framework to the Graph Engine (GE), and instructs the GE to perform network analysis and output analysis results through APIs. MindStudio then displays the network analysis results on the GUI.

Model Conversion

Models trained in frameworks such as Caffe and TensorFlow can be converted into offline models compatible with the Ascend AI Processor by using the ATC. During model conversion, you can enable operator scheduling tuning, weight data rearrangement, and memory optimization to preprocess your model without depending on the device.

Application Development

  • MindX SDK-based application development: The MindX SDK adopts the modular design to encapsulate each functional unit in the service process into an independent plugin, and connect all plugins to quickly construct services.

    MindStudio uses the MindX SDK to enable visualized process orchestration. You can drag plugins on the canvas to organize them, manage the overall process, and generate pipeline files for development.

  • AscendCL-based application development: You can also use MindStudio to develop Ascend Computing Language (AscendCL) application projects. With the C language API library provided by AscendCL, you can manage and schedule Ascend hardware compute resources, and perform deep learning inference, image preprocessing, and single-operator accelerated computing on the Ascend CANN platform.

Operator Development

MindStudio provides a complete operator development process, including the unit testing (UT), system testing (ST), and TIK operator debugging. It allows you to develop TBE and AI CPU operators based on mainstream frameworks such as TensorFlow, PyTorch, and MindSpore.

  • UT: MindStudio provides an upgraded UT solution based on the GTest framework, simplifying UT case development.
  • ST: MindStudio provides an upgraded ST framework to automatically generate test cases, verify operator functionality and compute accuracy in real hardware environments, and generate execution test reports.
  • TIK operator debugging: MindStudio supports visualized debugging of TIK operators. You can set breakpoints, perform single-step debugging, continuously run to the end or next breakpoint, view variable information, and exit debugging.

Model Accuracy Analyzer

Model Accuracy Analyzer is a tool designed to compare the accuracy difference of computation results of Huawei proprietary operators and third-party equivalents to quickly resolve the operator accuracy issues.

Performance Analysis

The performance analysis tool Profiling collects software and hardware profile data during AI application running, analyzes the performance metrics, and displays the result on a graphical user interface (GUI), helping users quickly detect and locate performance bottlenecks of AI applications and significantly improving the performance analysis efficiency of AI jobs.

MindStudio Advisor

MindStudio Advisor is a tool used to locate top performance tuning issues of models and operators, identify and analyze bottlenecks, and output tuning suggestions, thereby improving the development efficiency.

Query for Supported Operators and Models

The Supported Operators and Supported Models dialog boxes help users query information about operators supported by the current CANN version and models supported by ModelZoo.

AI Core Error Analysis

If you encounter AI Core errors in the inference or training process, you can use the AI Core Error Analyzer to collect necessary information and quickly locate the errors.