forked from mindspore-Ecosystem/mindspore
146 lines
9.5 KiB
Markdown
146 lines
9.5 KiB
Markdown
# Release 0.2.0-alpha
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## Major Features and Improvements
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### Ascend 910 Training and Inference Framework
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* New models
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* MobileNetV2: Inverted Residuals and Linear Bottlenecks.
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* ResNet101: Deep Residual Learning for Image Recognition.
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* Frontend and User Interface
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* Support for all python comparison operators.
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* Support for math operators **,//,%. Support for other python operators like and/or/not/is/is not/ in/ not in.
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* Support for the gradients of function with variable arguments.
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* Support for tensor indexing assignment for certain indexing type.
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* Support for dynamic learning rate.
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* User interfaces change log
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* DepthwiseConv2dNative, DepthwiseConv2dNativeBackpropFilter, DepthwiseConv2dNativeBackpropInput([!424](https://gitee.com/mindspore/mindspore/pulls/424))
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* ReLU6, ReLU6Grad([!224](https://gitee.com/mindspore/mindspore/pulls/224))
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* GeneratorDataset([!183](https://gitee.com/mindspore/mindspore/pulls/183))
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* VOCDataset([!477](https://gitee.com/mindspore/mindspore/pulls/477))
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* MindDataset, PKSampler([!514](https://gitee.com/mindspore/mindspore/pulls/514))
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* map([!506](https://gitee.com/mindspore/mindspore/pulls/506))
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* Conv([!226](https://gitee.com/mindspore/mindspore/pulls/226))
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* Adam([!253](https://gitee.com/mindspore/mindspore/pulls/253))
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* _set_fusion_strategy_by_idx, _set_fusion_strategy_by_size([!189](https://gitee.com/mindspore/mindspore/pulls/189))
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* CheckpointConfig([!122](https://gitee.com/mindspore/mindspore/pulls/122))
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* Constant([!54](https://gitee.com/mindspore/mindspore/pulls/54))
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* Executor and Performance Optimization
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* Support parallel execution of data prefetching and forward/backward computing.
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* Support parallel execution of gradient aggregation and forward/backward computing in distributed training scenarios.
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* Support operator fusion optimization.
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* Optimize compilation process and improve the performance.
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* Data processing, augmentation, and save format
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* Support multi-process of GeneratorDataset/PyFunc for high performance
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* Support variable batchsize
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* Support new Dataset operators, such as filter,skip,take,TextLineDataset
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### Other Hardware Support
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* GPU platform
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* Use dynamic memory pool by default on GPU.
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* Support parallel execution of computation and communication.
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* Support continuous address allocation by memory pool.
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* CPU platform
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* Support for windows 10 OS.
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## Bugfixes
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* Models
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* Fix mixed precision bug for VGG16 model ([!629](https://gitee.com/mindspore/mindspore/pulls/629)).
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* Python API
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* Fix ControlDepend operator bugs on CPU and GPU ([!396](https://gitee.com/mindspore/mindspore/pulls/396)).
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* Fix ArgMinWithValue operator bugs ([!338](https://gitee.com/mindspore/mindspore/pulls/338)).
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* Fix Dense operator bugs on PyNative mode ([!276](https://gitee.com/mindspore/mindspore/pulls/276)).
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* Fix MatMul operator bugs on PyNative mode ([!288](https://gitee.com/mindspore/mindspore/pulls/288)).
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* Executor
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* Fix operator selection bugs and make it general ([!300](https://gitee.com/mindspore/mindspore/pulls/300)).
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* Fix memory reuse bug for GetNext op ([!291](https://gitee.com/mindspore/mindspore/pulls/291)).
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* GPU platform
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* Fix memory allocation in multi-graph scenarios ([!444](https://gitee.com/mindspore/mindspore/pulls/444)).
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* Fix bias_add_grad under fp16 precision ([!598](https://gitee.com/mindspore/mindspore/pulls/598)).
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* Fix support for fp16 kernels on nvidia 1080Ti([!571](https://gitee.com/mindspore/mindspore/pulls/571)).
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* Fix parsing of tuple type parameters ([!316](https://gitee.com/mindspore/mindspore/pulls/316)).
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* Data processing
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* Fix TypeErrors about can't pickle mindspore._c_dataengine.DEPipeline objects([!434](https://gitee.com/mindspore/mindspore/pulls/434)).
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* Add TFRecord file verification([!406](https://gitee.com/mindspore/mindspore/pulls/406)).
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## Contributors
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Thanks goes to these wonderful people:
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Alexey_Shevlyakov, Cathy, Chong, Hoai, Jonathan, Junhan, JunhanHu, Peilin, SanjayChan, StrawNoBerry, VectorSL, Wei, WeibiaoYu, Xiaoda, Yanjun, YuJianfeng, ZPaC, Zhang, ZhangQinghua, ZiruiWu, amongo, anthonyaje, anzhengqi, biffex, caifubi, candanzg, caojian05, casgj, cathwong, ch-l, chang, changzherui, chenfei, chengang, chenhaozhe, chenjianping, chentingting, chenzomi, chujinjin, dengwentao, dinghao, fanglei, fary86, flywind, gaojing, geekun, gengdongjie, ghzl, gong, gongchen, gukecai, guohongzilong, guozhijian, gziyan, h.farahat, hesham, huangdongrun, huanghui, jiangzhiwen, jinyaohui, jjfeing, jojobugfree, jonathan_yan, jonyguo, jzw, kingfo, kisnwang, laiyongqiang, leonwanghui, lianliguang, lichen, lichenever, limingqi107, liubuyu, liuxiao, liyong, liyong126, lizhenyu, lupengcheng, lvliang, maoweiyong, ms_yan, mxm, ougongchang, panfengfeng, panyifeng, pengyanjun, penn, qianlong, seatea, simson, suteng, thlinh, vlne-v1, wangchengke, wanghua, wangnan39, wangqiuliang, wenchunjiang, wenkai, wukesong, xiefangqi, xulei, yanghaitao, yanghaoran, yangjie159, yangzhenzhang, yankai10, yanzhenxiang2020, yao_yf, yoonlee666, zhangbuxue, zhangz0911gm, zhangzheng, zhaojichen, zhaoting, zhaozhenlong, zhongligeng, zhoufeng, zhousiyi, zjun, zyli2020, yuhuijun, limingqi107, lizhenyu, chenweifeng.
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Contributions of any kind are welcome!
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# Release 0.1.0-alpha
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## Main Features
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### Ascend 910 Training and Inference Framework
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* Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
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* Python version: 3.7.5
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* Preset models
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* ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
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* AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
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* LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
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* VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
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* YoloV3: real-time object detection network.
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* NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
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* Execution modes
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* Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
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* PyNative mode: single-step execution mode, facilitating process debugging.
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* Debugging capability and methods
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* Save CheckPoints and Summary data during training.
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* Support asynchronous printing.
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* Dump the computing data.
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* Support profiling analysis of the execution process performance.
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* Distributed execution
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* Support AllReduce, AllGather, and BroadCast collective communication.
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* AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
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* Collective communication-based layerwise parallel: Models are divided and allocated to different devices to solve the problem of insufficient memory for large model processing and improve the training speed.
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* Automatic parallel mode: The better data and model parallel mode can be predicted based on the cost model. It is recommended that this mode be used on ResNet series networks.
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* Automatic differentiation
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* Implement automatic differentiation based on Source to Source.
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* Support distributed scenarios and automatic insertion of reverse communication operators.
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* Data processing, augmentation, and save format
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* Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
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* Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
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* Provide basic operator libraries to cover common CV scenarios.
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* Support users to customize Python data augmentation operators through the Pyfunc mechanism.
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* Support the access of user-defined datasets through the GeneratorDataset mechanism.
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* Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
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* Convert user datasets to the MindSpore data format.
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* After data processing and augmentation, provide training applications in feed and graph modes.
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* FP32/16 mixed precision computation, supporting automatic and manual configuration
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* Provide common operators such as nn, math, and array, which can be customized.
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### Inference Deployment
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* Deploy models in MindSpore format on the Ascend 310 platform for inference.
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* Save models in ONNX format.
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* Support saving models in LITE format and running models based on the lightweight inference framework.
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* Recommended OS: Android 4.3 or later
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* Supported network type: LeNet
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* Provide the generalization operators generated by TVM and operators generated after specific networks are tuned.
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### Other Hardware Support
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* GPU platform training
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* Recommended OS: Ubuntu 16.04
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* CUDA version: 9.2 or 10.1
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* CuDNN version: 7.6 or later
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* Python version: 3.7.5
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* NCCL version: 2.4.8-1
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* OpenMPI version: 3.1.5
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* Supported models: AlexNet, LeNet, and LSTM
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* Supported datasets: MNIST and CIFAR-10
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* Support data parallel.
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* CPU platform training
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* Recommended OS: Ubuntu 16.04
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* Python version: 3.7.5
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* Supported model: LeNet
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* Supported dataset: MNIST
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* Provide only the stand-alone operation version.
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## Peripherals and Tools
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* [MindSpore Official Website] (https://www.mindspore.cn/)
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* [MindInsight Visualization Debugging and Optimization] (https://gitee.com/mindspore/mindinsight)
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* [MindArmour Model Security Hardening Package] (https://gitee.com/mindspore/mindarmour)
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* [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)
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