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RELEASE.md 8.0 kB

5 years ago
5 years ago
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  1. ## MindInsight
  2. # Release 0.7.0-beta
  3. ## Major Features and Improvements
  4. * Optimize node name display in computation graph.
  5. * MindSpore Profiler supports network training with GPU operators.
  6. * MindWizard generates classic network scripts according to user preference.
  7. * Web UI supports language internationalization, including both Chinese and English.
  8. ## Bugfixes
  9. * Optimize UI page initialization to handle timeout requests. ([!503](https://gitee.com/mindspore/mindinsight/pulls/503))
  10. * Fix the line break problem when the profiling file number is too long. ([!532](https://gitee.com/mindspore/mindinsight/pulls/532))
  11. ## Thanks to our Contributors
  12. Thanks goes to these wonderful people:
  13. 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, Hong Sheng, Lin Pan, Ran Mo.
  14. Contributions of any kind are welcome!
  15. # Release 0.6.0-beta
  16. ## Major Features and Improvements
  17. * Provide monitoring capabilities for each of Ascend AI processor and other hardware resources, including CPU and memory.
  18. * Visualization of weight, gradient and other tensor data in model training.
  19. * Provide tabular from presentation of tensor data.
  20. * Provide histogram to show the distribution of tensor data and its change over time.
  21. ## Bugfixes
  22. * UI fix for the error message display mode of the tensor during real-time training. ([!465](https://gitee.com/mindspore/mindinsight/pulls/465))
  23. * The summary file size is larger than max_file_size. ([!3481](https://gitee.com/mindspore/dashboard/projects/mindspore/mindspore/pulls/3481))
  24. * Fix real-time training error when disk is full. ([!3058](https://gitee.com/mindspore/mindspore/pulls/3058))
  25. ## Thanks to our Contributors
  26. Thanks goes to these wonderful people:
  27. 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.
  28. Contributions of any kind are welcome!
  29. # Release 0.5.0-beta
  30. ## Major Features and Improvements
  31. * MindSpore Profiler
  32. * Provide performance analyse tool for the input data pipeline.
  33. * Provide timeline analyse tool, which can show the detail of the streams/tasks.
  34. * 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.
  35. * Provide profiling guides for the users to find the performance bottlenecks quickly.
  36. * CPU summary operations support for CPU summary data.
  37. * Over threshold warn support in scalar training dashboard.
  38. * Provide more user-friendly callback function for visualization
  39. * Provide unified callback `SummaryCollector` to log most commonly visualization event.
  40. * Discard the original visualization callback `SummaryStep`, `TrainLineage` and `EvalLineage`.
  41. * `SummaryRecord` provide new API `add_value` to collect data into cache for summary persistence.
  42. * `SummaryRecord` provide new API `set_mode` to distinguish summary persistence mode at different stages.
  43. * MindConverter supports conversion of more operators and networks, and improves its ease of use.
  44. ## Bugfixes
  45. * Fix FileNotFound exception by adding robust check for summary watcher ([!281](https://gitee.com/mindspore/mindinsight/pulls/281)).
  46. * UI fix operator table sort jump problem ([!283](https://gitee.com/mindspore/mindinsight/pulls/283)).
  47. * Dataset serializer return schema json str when schema type is `mindspore.dataset.engine.Schema` ([!2185](https://gitee.com/mindspore/mindspore/pulls/2185)).
  48. ## Thanks to our Contributors
  49. Thanks goes to these wonderful people:
  50. 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.
  51. Contributions of any kind are welcome!
  52. # Release 0.3.0-alpha
  53. ## Major Features and Improvements
  54. * Profiling
  55. * Provide easy to use apis for profiling start/stop and profiling data analyse (on Ascend only).
  56. * Provide operators performance display and analysis on MindInsight UI.
  57. * Large scale network computation graph visualization.
  58. * Optimize summary record implementation and improve its performance.
  59. * Improve lineage usability
  60. * Optimize lineage display and enrich tabular operation.
  61. * Decouple lineage callback from `SummaryRecord`.
  62. * Support scalar compare of multiple runs.
  63. * Scripts conversion from other frameworks
  64. * Support for converting PyTorch scripts within TorchVision to MindSpore scripts automatically.
  65. ## Bugfixes
  66. * Fix pb files loaded problem when files are modified at the same time ([!53](https://gitee.com/mindspore/mindinsight/pulls/53)).
  67. * Fix load data thread stuck in `LineageCacheItemUpdater` ([!114](https://gitee.com/mindspore/mindinsight/pulls/114)).
  68. * Fix samples from previous steps erased due to tags size too large problem ([!86](https://gitee.com/mindspore/mindinsight/pulls/86)).
  69. * Fix image and histogram event package error ([!1143](https://gitee.com/mindspore/mindspore/pulls/1143)).
  70. * Equally distribute histogram ignoring actual step number to avoid large white space ([!66](https://gitee.com/mindspore/mindinsight/pulls/66)).
  71. ## Thanks to our Contributors
  72. Thanks goes to these wonderful people:
  73. 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.
  74. Contributions of any kind are welcome!
  75. # Release 0.2.0-alpha
  76. ## Major Features and Improvements
  77. * Parameter distribution graph (Histogram).
  78. Now you can use [`HistogramSummary`](https://www.mindspore.cn/api/zh-CN/master/api/python/mindspore/mindspore.ops.operations.html#mindspore.ops.operations.HistogramSummary) and MindInsight to record and visualize distribution info of tensors. See our [tutorial](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/visualization_tutorials.html) for details.
  79. * Lineage support Custom information
  80. * GPU support
  81. * Model and dataset tracking linkage support
  82. ## Bugfixes
  83. * Reduce cyclomatic complexity of `list_summary_directories` ([!11](https://gitee.com/mindspore/mindinsight/pulls/11)).
  84. * Fix unsafe functions and duplication files and redundant codes ([!14](https://gitee.com/mindspore/mindinsight/pulls/14)).
  85. * Fix sha256 checksum missing bug ([!24](https://gitee.com/mindspore/mindinsight/pulls/24)).
  86. * Fix graph bug when node name is empty ([!34](https://gitee.com/mindspore/mindinsight/pulls/34)).
  87. * Fix start/stop command error code incorrect ([!44](https://gitee.com/mindspore/mindinsight/pulls/44)).
  88. ## Thanks to our Contributors
  89. Thanks goes to these wonderful people:
  90. 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
  91. Contributions of any kind are welcome!
  92. # Release 0.1.0-alpha
  93. * Training process observation
  94. * Provides and displays training process information, including computational graphs and training process indicators.
  95. * Training result tracing
  96. * Provides functions of tracing and visualizing model training parameter information, including filtering and sorting of training data, model accuracy and training hyperparameters.