* ResNet-50: residual structure-based convolutional neural network (CNN) for image classification, which is widely used.
* AlexNet: classic CNN for image classification, achieving historical results in ImageNet LSVRC-2012.
* LeNet: classic CNN for image classification, which was proposed by Yann LeCun.
* VGG16: classic CNN for image classification, which was proposed by Oxford Visual Geometry Group.
* YoloV3: real-time object detection network.
* NEZHA: BERT-based Chinese pre-training network produced by Huawei Noah's Ark Laboratory.
* Execution modes
* Graph mode: provides graph optimization methods such as memory overcommitment, IR fusion, and buffer fusion to achieve optimal execution performance.
* PyNative mode: single-step execution mode, facilitating process debugging.
* Debugging capability and methods
* Save CheckPoints and Summary data during training.
* Support asynchronous printing.
* Dump the computing data.
* Support profiling analysis of the execution process performance.
* Distributed execution
* Support AllReduce, AllGather, and BroadCast collective communication.
* AllReduce data parallel: Each device obtains different training data, which accelerates the overall training process.
* 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.
* 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.
* Automatic differentiation
* Implement automatic differentiation based on Source to Source.
* Support distributed scenarios and automatic insertion of reverse communication operators.
* Data processing, augmentation, and save format
* Load common datasets such as ImageNet, MNIST, CIFAR-10, and CIFAR-100.
* Support common data loading pipeline operations, such as shuffle, repeat, batch, map, and sampler.
* Provide basic operator libraries to cover common CV scenarios.
* Support users to customize Python data augmentation operators through the Pyfunc mechanism.
* Support the access of user-defined datasets through the GeneratorDataset mechanism.
* Provide the MindSpore data format, data aggregation and storage, random access example, data partition, efficient parallel read, user-defined index, and dataset search.
* Convert user datasets to the MindSpore data format.
* After data processing and augmentation, provide training applications in feed and graph modes.
* FP32/16 mixed precision computation, supporting automatic and manual configuration
* Provide common operators such as nn, math, and array, which can be customized.
### Inference Deployment
* Deploy models in MindSpore format on the Ascend 310 platform for inference.
* Save models in ONNX format.
* Support saving models in LITE format and running models based on the lightweight inference framework.
* Recommended OS: Android 4.3 or later
* Supported network type: LeNet
* Provide the generalization operators generated by TVM and operators generated after specific networks are tuned.
### Other Hardware Support
* GPU platform training
* Recommended OS: Ubuntu 16.04
* CUDA version: 9.2 or 10.1
* CuDNN version: 7.6 or later
* Python version: 3.7.5
* NCCL version: 2.4.8-1
* OpenMPI version: 3.1.5
* Supported models: AlexNet, LeNet, and LSTM
* Supported datasets: MNIST and CIFAR-10
* Support data parallel.
* CPU platform training
* Recommended OS: Ubuntu 16.04
* Python version: 3.7.5
* Supported model: LeNet
* Supported dataset: MNIST
* Provide only the stand-alone operation version.
## Peripherals and Tools
* [MindSpore Official Website] (https://www.mindspore.cn/)
* [MindInsight Visualization Debugging and Optimization] (https://gitee.com/mindspore/mindinsight)
* [MindArmour Model Security Hardening Package] (https://gitee.com/mindspore/mindarmour)
