- Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.0
- Python version: 3.7.5
- Preset models
- 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.
- 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.
- 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.
- [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)
- [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)