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MindInsight

Release 0.6.0-beta

Major Features and Improvements

  • Provide monitoring capabilities for each of Ascend AI processor and other hardware resources, including CPU and memory.
  • Visualization of weight, gradient and other tensor data in model training.
    • Provide tabular from presentation of tensor data.
    • Provide histogram to show the distribution of tensor data and its change over time.

Bugfixes

  • UI fix for the error message display mode of the tensor during real-time training. (!465)
  • The summary file size is larger than max_file_size. (!3481)
  • Fix real-time training error when disk is full. (!3058)

Thanks to our Contributors

Thanks goes to these wonderful people:

Congli Gao, Weifeng Huang, Zhenzhong Kou, Hongzhang Li, Longfei Li, Yongxiong Liang, Chongming Liu, Pengting Luo, Yanming Miao, Gongchang Ou, Yongxiu Qu, Hui Pan, Luyu Qiu, Junyan Qin, Kai Wen, Weining Wang, Yue Wang, Zhuanke Wu, Yifan Xia, Lihua Ye, Weibiao Yu, Ximiao Yu, Yunshu Zhang, Ting Zhao, Jianfeng Zhu, Ning Ma, Yihui Zhang, Shuide Wang.

Contributions of any kind are welcome!

Release 0.5.0-beta

Major Features and Improvements

  • MindSpore Profiler
    • Provide performance analyse tool for the input data pipeline.
    • Provide timeline analyse tool, which can show the detail of the streams/tasks.
    • Provide a tool to visualize the step trace information, which can be used to analyse the general performance of the neural network in each phase.
    • Provide profiling guides for the users to find the performance bottlenecks quickly.
  • CPU summary operations support for CPU summary data.
  • Over threshold warn support in scalar training dashboard.
  • Provide more user-friendly callback function for visualization
    • Provide unified callback SummaryCollector to log most commonly visualization event.
    • Discard the original visualization callback SummaryStep, TrainLineage and EvalLineage.
    • SummaryRecord provide new API add_value to collect data into cache for summary persistence.
    • SummaryRecord provide new API set_mode to distinguish summary persistence mode at different stages.
  • MindConverter supports conversion of more operators and networks, and improves its ease of use.

Bugfixes

  • Fix FileNotFound exception by adding robust check for summary watcher (!281).
  • UI fix operator table sort jump problem (!283).
  • Dataset serializer return schema json str when schema type is mindspore.dataset.engine.Schema (!2185).

Thanks to our Contributors

Thanks goes to these wonderful people:

Chao Chen, Congli Gao, Ye Huang, Weifeng Huang, Zhenzhong Kou, Hongzhang Li, Longfei Li, Yongxiong Liang, Chongming Liu, Pengting Luo, Yanming Miao, Gongchang Ou, Yongxiu Qu, Hui Pan, Luyu Qiu, Junyan Qin, Kai Wen, Weining Wang, Yue Wang, Zhuanke Wu, Yifan Xia, Lihua Ye, Weibiao Yu, Ximiao Yu, Yunshu Zhang, Ting Zhao, Jianfeng Zhu.

Contributions of any kind are welcome!

Release 0.3.0-alpha

Major Features and Improvements

  • Profiling
    • Provide easy to use apis for profiling start/stop and profiling data analyse (on Ascend only).
    • Provide operators performance display and analysis on MindInsight UI.
  • Large scale network computation graph visualization.
  • Optimize summary record implementation and improve its performance.
  • Improve lineage usability
    • Optimize lineage display and enrich tabular operation.
    • Decouple lineage callback from SummaryRecord.
  • Support scalar compare of multiple runs.
  • Scripts conversion from other frameworks
    • Support for converting PyTorch scripts within TorchVision to MindSpore scripts automatically.

Bugfixes

  • Fix pb files loaded problem when files are modified at the same time (!53).
  • Fix load data thread stuck in LineageCacheItemUpdater (!114).
  • Fix samples from previous steps erased due to tags size too large problem (!86).
  • Fix image and histogram event package error (!1143).
  • Equally distribute histogram ignoring actual step number to avoid large white space (!66).

Thanks to our Contributors

Thanks goes to these wonderful people:

Chao Chen, Congli Gao, Ye Huang, Weifeng Huang, Zhenzhong Kou, Hongzhang Li, Longfei Li, Yongxiong Liang, Pengting Luo, Yanming Miao, Gongchang Ou, Yongxiu Qu, Hui Pan, Luyu Qiu, Junyan Qin, Kai Wen, Weining Wang, Yue Wang, Zhuanke Wu, Yifan Xia, Weibiao Yu, Ximiao Yu, Ting Zhao, Jianfeng Zhu.

Contributions of any kind are welcome!

Release 0.2.0-alpha

Major Features and Improvements

  • Parameter distribution graph (Histogram). Now you can use HistogramSummary and MindInsight to record and visualize distribution info of tensors. See our tutorial for details.
  • Lineage support Custom information
  • GPU support
  • Model and dataset tracking linkage support

Bugfixes

  • Reduce cyclomatic complexity of list_summary_directories (!11).
  • Fix unsafe functions and duplication files and redundant codes (!14).
  • Fix sha256 checksum missing bug (!24).
  • Fix graph bug when node name is empty (!34).
  • Fix start/stop command error code incorrect (!44).

Thanks to our Contributors

Thanks goes to these wonderful people:

Ye Huang, Weifeng Huang, Zhenzhong Kou, Pengting Luo, Hongzhang Li, Yongxiong Liang, Gongchang Ou, Hui Pan, Luyu Qiu, Junyan Qin, Kai Wen, Weining Wang, Yifan Xia, Yunshu Zhang, Ting Zhao

Contributions of any kind are welcome!

Release 0.1.0-alpha

  • Training process observation

    • Provides and displays training process information, including computational graphs and training process indicators.

  • Training result tracing

    • Provides functions of tracing and visualizing model training parameter information, including filtering and sorting of training data, model accuracy and training hyperparameters.