forked from OSSInnovation/mindspore
73 lines
4.3 KiB
Markdown
73 lines
4.3 KiB
Markdown
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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 2.0
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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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