Release 0.1.0-alpha

Main Features

Ascend 910 Training and Inference Framework

• Recommended OS: Ubuntu 16.04 (or later) or EulerOS 2.5 or EulerOS 2.8
• 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.

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)
• [GraphEngine Computational Graph Engine] (https://gitee.com/mindspore/graphengine)