Dear TSC members,

 

Please find below the release plan for our upcoming release 0.5.0-beta targeting the end of June.  We¡¯ve also documented the release management related info at https://gitee.com/mindspore/mindspore/wikis/June%202020?sort_id=2339889 

Please also note that this is not the final release note, which will contain additional information on bug fixes and so forth.

 

Feel free to ask any questions or provide any feedback through the list !

 

Planned Features and Improvements For 0.5.0-beta release

MindSpore

Ascend 910 Training and Inference Framework

• New models
  • ResNext50: a simple, highly modularized network architecture using aggregated resdiual transformations for image classification on ImageNet 2012 dataset.
  • WarpCTC: a recurrent neural network with CTCLoss for labelling unsegmented sequence data on Captcha images.
  • MASS: a pre-training method for sequence to sequence based language generation tasks on Text Summarization and Conversational Response Generation using News Crawls 2007-2017 dataset, Gigaword corpus and Cornell movie dialog corpus.
  • Transformer: a neural network architecture for language understanding on WMT 2014 English-German dataset.
  • GCN£ºGraph Convolutional Networks for the task of classification of nodes in a graph on Cora and Citeseer datasets.
  • GAT£ºan attention-based graph neural network for node classification on Cora and CiteSeer dataset.
• Frontend and user interface
  • Support tensor value and assignment of mixed tensor index in graph mode.
  • Support tensor comparison, len operator, constexpr syntax, value and assignment of tensor index in pynative mode.
  • Support converting MindSpore IR to pb format for infer model.
  • Support print operator to write data directly on the hard disk.
  • Add the double recursive programming solution for very high speed parallel strategy search in automatic parallel.
  • Support some sparse expression(such as sparse tesnor, sparse optimizier).
• Executor and performance optimization
  • Heterogeneous execution on CPU and ascend devices supported.
  • Parameter Server for distributed deep learning supported.
  • Quantitative training of MobileNetV2, Resnet50, Warpctc and YoloV3 are supported.
  • Serving£ºa flexible service deployment framework for deep learning models.
  • Support new fusion architecture, which can do fusion optimization across graphs and operators to improve execution speed.
• Data processing, augmentation, and save format
  • Support data processing pipeline performance profiling.
  • Support public dataset loading, such as CLUE and Coco.
  • Support more text processing, such as more tokenizers and vocab data.
  • Support MindRecord padded data.

Other Hardware Support

• GPU platform
  • New model supported: Bert / Wide&Deep.
• CPU platform
  • New model supported: LSTM.

GraphEngine

• Optimize Allreduce trailing parallelism, rebuild the calculation graph dependencies, adjust the calculation order, and maximize the efficiency of calculation and gradient aggregation communication in parallel, especially in large data volume gradient aggregation and low bandwidth/large cluster scenarios You can get a bigger income.
• Advance constant folding, variable fusion, conversion operator related optimization pass to the end of the graph preparation.
• Modify memory allocation algorithm, optimize GE memory allocation, and reduce memory usage in training multi-PCS scenarios.
• Support IR composition, model compilation, inference execution in the same process.

MindArmour

• Optimizers with differential privacy

  • Differential privacy model training now supports both Pynative mode and graph mode.

  • Graph mode is recommended for its performance.

MindInsight

• MindSpore Profiler
  • Provide performance analyse tool for the input data pipeline.
  • Provide timeline analyse tool, which can show the details of the streams/tasks.
  • Provide a tool to visualize the step trace information, which can be used 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.

 

Thanks

Liucunwei

 

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