forked from huawei/mindspore2022
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README.md
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README.md
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[](https://pypi.org/project/mindspore)
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[](https://badge.fury.io/py/mindspore)
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@ -8,91 +8,94 @@
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[](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w)
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[](https://gitee.com/mindspore/mindspore/pulls)
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[View English](./README.md)
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[查看中文](./README_CN.md)
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<!-- TOC -->
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- [MindSpore介绍](#mindspore介绍)
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- [自动微分](#自动微分)
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- [自动并行](#自动并行)
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- [安装](#安装)
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- [pip方式安装](#pip方式安装)
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- [源码编译方式安装](#源码编译方式安装)
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- [Docker镜像](#docker镜像)
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- [快速入门](#快速入门)
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- [文档](#文档)
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- [社区](#社区)
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- [治理](#治理)
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- [交流](#交流)
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- [贡献](#贡献)
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- [分支维护策略](#分支维护策略)
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- [现有分支维护状态](#现有分支维护状态)
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- [版本说明](#版本说明)
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- [许可证](#许可证)
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- [What Is MindSpore](#what-is-mindspore)
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- [Automatic Differentiation](#automatic-differentiation)
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- [Automatic Parallel](#automatic-parallel)
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- [Installation](#installation)
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- [Pip mode method installation](#pip-mode-method-installation)
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- [Source code compilation installation](#source-code-compilation-installation)
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- [Docker Image](#docker-image)
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- [Quickstart](#quickstart)
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- [Docs](#docs)
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- [Community](#community)
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- [Governance](#governance)
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- [Communication](#communication)
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- [Contributing](#contributing)
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- [Maintenance phases](#maintenance-phases)
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- [Maintenance status](#maintenance-status)
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- [Release Notes](#release-notes)
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- [License](#license)
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<!-- /TOC -->
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## MindSpore介绍
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## What Is MindSpore
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MindSpore是一种适用于端边云场景的新型开源深度学习训练/推理框架。
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MindSpore提供了友好的设计和高效的执行，旨在提升数据科学家和算法工程师的开发体验，并为Ascend AI处理器提供原生支持，以及软硬件协同优化。
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MindSpore is a new open source deep learning training/inference framework that
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could be used for mobile, edge and cloud scenarios. MindSpore is designed to
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provide development experience with friendly design and efficient execution for
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the data scientists and algorithmic engineers, native support for Ascend AI
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processor, and software hardware co-optimization. At the meantime MindSpore as
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a global AI open source community, aims to further advance the development and
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enrichment of the AI software/hardware application ecosystem.
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同时，MindSpore作为全球AI开源社区，致力于进一步开发和丰富AI软硬件应用生态。
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/MindSpore-architecture.png" alt="MindSpore Architecture"/>
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/MindSpore-architecture-zh.png" alt="MindSpore Architecture"/>
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For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/programming_guide/en/master/architecture.html).
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欲了解更多详情，请查看我们的[总体架构](https://www.mindspore.cn/docs/programming_guide/zh-CN/master/architecture.html)。
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### Automatic Differentiation
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### 自动微分
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Currently, there are two automatic differentiation techniques in mainstream deep learning frameworks:
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当前主流深度学习框架中有两种自动微分技术：
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- **Operator Overloading (OO)**: Overloading the basic operators of the programming language to encapsulate their gradient rules. Record the operation trajectory of the network during forward execution in an operator overloaded manner, then apply the chain rule to the dynamically generated data flow graph to implement automatic differentiation.
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- **Source Transformation (ST)**: This technology is evolving from the functional programming framework and performs automatic differential transformation on the intermediate expression (the expression form of the program during the compilation process) in the form of just-in-time compilation (JIT), supporting complex control flow scenarios, higher-order functions and closures.
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- **操作符重载法**： 通过操作符重载对编程语言中的基本操作语义进行重定义，封装其微分规则。 在程序运行时记录算子过载正向执行时网络的运行轨迹，对动态生成的数据流图应用链式法则，实现自动微分。
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- **代码变换法**： 该技术是从功能编程框架演进而来，以即时编译（Just-in-time Compilation，JIT）的形式对中间表达式（程序在编译过程中的表达式）进行自动差分转换，支持复杂的控制流场景、高阶函数和闭包。
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PyTorch used OO. Compared to ST, OO generates gradient graph in runtime, so it does not need to take function call and control flow into consideration, which makes it easier to develop. However, OO can not perform gradient graph optimization in compilation time and the control flow has to be unfolded in runtime, so it is difficult to achieve extreme optimization in performance.
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PyTorch采用的是操作符重载法。相较于代码变换法，操作符重载法是在运行时生成微分计算图的， 无需考虑函数调用与控制流等情况， 开发更为简单。 但该方法不能在编译时刻做微分图的优化， 控制流也需要根据运行时的信息来展开， 很难实现性能的极限优化。
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MindSpore则采用的是代码变换法。一方面，它支持自动控制流的自动微分，因此像PyTorch这样的模型构建非常方便。另一方面，MindSpore可以对神经网络进行静态编译优化，以获得更好的性能。
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MindSpore implemented automatic differentiation based on ST. On the one hand, it supports automatic differentiation of automatic control flow, so it is quite convenient to build models like PyTorch. On the other hand, MindSpore can perform static compilation optimization on neural networks to achieve great performance.
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
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MindSpore自动微分的实现可以理解为程序本身的符号微分。MindSpore IR是一个函数中间表达式，它与基础代数中的复合函数具有直观的对应关系。复合函数的公式由任意可推导的基础函数组成。MindSpore IR中的每个原语操作都可以对应基础代数中的基本功能，从而可以建立更复杂的流控制。
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The implementation of MindSpore automatic differentiation can be understood as the symbolic differentiation of the program itself. Because MindSpore IR is a functional intermediate expression, it has an intuitive correspondence with the composite function in basic algebra. The derivation formula of the composite function composed of arbitrary basic functions can be derived. Each primitive operation in MindSpore IR can correspond to the basic functions in basic algebra, which can build more complex flow control.
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### 自动并行
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### Automatic Parallel
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MindSpore自动并行的目的是构建数据并行、模型并行和混合并行相结合的训练方法。该方法能够自动选择开销最小的模型切分策略，实现自动分布并行训练。
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The goal of MindSpore automatic parallel is to build a training method that combines data parallelism, model parallelism, and hybrid parallelism. It can automatically select a least cost model splitting strategy to achieve automatic distributed parallel training.
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<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
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目前MindSpore采用的是算子切分的细粒度并行策略，即图中的每个算子被切分为一个集群，完成并行操作。在此期间的切分策略可能非常复杂，但是作为一名Python开发者，您无需关注底层实现，只要顶层API计算是有效的即可。
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At present, MindSpore uses a fine-grained parallel strategy of splitting operators, that is, each operator in the figure is split into a cluster to complete parallel operations. The splitting strategy during this period may be very complicated, but as a developer advocating Pythonic, you don't need to care about the underlying implementation, as long as the top-level API compute is efficient.
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## 安装
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||||
## Installation
|
||||
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||||
### pip方式安装
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||||
### Pip mode method installation
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||||
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||||
MindSpore提供跨多个后端的构建选项：
|
||||
MindSpore offers build options across multiple backends:
|
||||
|
||||
| 硬件平台 | 操作系统 | 状态 |
|
||||
| :------------ | :-------------- | :--- |
|
||||
| Ascend 910 | Ubuntu-x86 | ✔️ |
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||||
| | Ubuntu-aarch64 | ✔️ |
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||||
| | EulerOS-aarch64 | ✔️ |
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||||
| | CentOS-x86 | ✔️ |
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||||
| | CentOS-aarch64 | ✔️ |
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||||
| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
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||||
| CPU | Ubuntu-x86 | ✔️ |
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| | Ubuntu-aarch64 | ✔️ |
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||||
| | Windows-x86 | ✔️ |
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| Hardware Platform | Operating System | Status |
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||||
| :---------------- | :--------------- | :----- |
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| Ascend910 | Ubuntu-x86 | ✔️ |
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||||
| | Ubuntu-aarch64 | ✔️ |
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||||
| | EulerOS-aarch64 | ✔️ |
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||||
| | CentOS-x86 | ✔️ |
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||||
| | CentOS-aarch64 | ✔️ |
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||||
| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
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||||
| CPU | Ubuntu-x86 | ✔️ |
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||||
| | Ubuntu-aarch64 | ✔️ |
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||||
| | Windows-x86 | ✔️ |
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||||
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||||
使用`pip`命令安装，以`CPU`和`Ubuntu-x86`build版本为例：
|
||||
For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
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||||
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||||
1. 请从[MindSpore下载页面](https://www.mindspore.cn/versions)下载并安装whl包。
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||||
1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
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||||
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||||
```bash
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||||
pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.2.0-rc1/MindSpore/cpu/ubuntu_x86/mindspore-1.2.0rc1-cp37-cp37m-linux_x86_64.whl
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||||
```
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||||
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||||
2. 执行以下命令，验证安装结果。
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||||
2. Run the following command to verify the install.
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||||
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```python
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import numpy as np
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@ -122,39 +125,39 @@ MindSpore提供跨多个后端的构建选项：
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[ 4. 10. 18.]
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```
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使用pip方式，在不同的环境安装MindSpore，可参考以下文档。
|
||||
Use pip mode method to install MindSpore in different environments. Refer to the following documents.
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||||
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||||
- [Ascend环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip.md)
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- [GPU环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip.md)
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- [CPU环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip.md)
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- [Using pip mode method to install MindSpore in Ascend environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip_en.md)
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- [Using pip mode method to install MindSpore in GPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip_en.md)
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- [Using pip mode method to install MindSpore in CPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip_en.md)
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### 源码编译方式安装
|
||||
### Source code compilation installation
|
||||
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使用源码编译方式，在不同的环境安装MindSpore，可参考以下文档。
|
||||
Use the source code compilation method to install MindSpore in different environments. Refer to the following documents.
|
||||
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||||
- [Ascend环境使用源码编译方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_source.md)
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- [GPU环境使用源码编译方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_source.md)
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- [CPU环境使用源码编译方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_source.md)
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- [Using the source code compilation method to install MindSpore in Ascend environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_source_en.md)
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- [Using the source code compilation method to install MindSpore in GPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_source_en.md)
|
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- [Using the source code compilation method to install MindSpore in CPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_source_en.md)
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### Docker镜像
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### Docker Image
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MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore)上。
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目前容器化构建选项支持情况如下：
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MindSpore docker image is hosted on [Docker Hub](https://hub.docker.com/r/mindspore),
|
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currently the containerized build options are supported as follows:
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| 硬件平台 | Docker镜像仓库 | 标签 | 说明 |
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| :----- | :------------------------ | :----------------------- | :--------------------------------------- |
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| CPU | `mindspore/mindspore-cpu` | `x.y.z` | 已经预安装MindSpore `x.y.z` CPU版本的生产环境。 |
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| | | `devel` | 提供开发环境从源头构建MindSpore（`CPU`后端）。安装详情请参考<https://www.mindspore.cn/install> 。 |
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| | | `runtime` | 提供运行时环境安装MindSpore二进制包（`CPU`后端）。 |
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| GPU | `mindspore/mindspore-gpu` | `x.y.z` | 已经预安装MindSpore `x.y.z` GPU版本的生产环境。 |
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| | | `devel` | 提供开发环境从源头构建MindSpore（`GPU CUDA10.1`后端）。安装详情请参考<https://www.mindspore.cn/install> 。 |
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| | | `runtime` | 提供运行时环境安装MindSpore二进制包（`GPU CUDA10.1`后端）。 |
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| Hardware Platform | Docker Image Repository | Tag | Description |
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| :---------------- | :---------------------- | :-- | :---------- |
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| CPU | `mindspore/mindspore-cpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` CPU release. |
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| | | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to <https://www.mindspore.cn/install/en> for installation details. |
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| | | `runtime` | Runtime environment provided to install MindSpore binary package with `CPU` backend. |
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||||
| GPU | `mindspore/mindspore-gpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` GPU release. |
|
||||
| | | `devel` | Development environment provided to build MindSpore (with `GPU CUDA10.1` backend) from the source, refer to <https://www.mindspore.cn/install/en> for installation details. |
|
||||
| | | `runtime` | Runtime environment provided to install MindSpore binary package with `GPU CUDA10.1` backend. |
|
||||
|
||||
> **注意：** 不建议从源头构建GPU `devel` Docker镜像后直接安装whl包。我们强烈建议您在GPU `runtime` Docker镜像中传输并安装whl包。
|
||||
> **NOTICE:** For GPU `devel` docker image, it's NOT suggested to directly install the whl package after building from the source, instead we strongly RECOMMEND you transfer and install the whl package inside GPU `runtime` docker image.
|
||||
|
||||
- CPU
|
||||
|
||||
对于`CPU`后端，可以直接使用以下命令获取并运行最新的稳定镜像：
|
||||
For `CPU` backend, you can directly pull and run the latest stable image using the below command:
|
||||
|
||||
```bash
|
||||
docker pull mindspore/mindspore-cpu:1.1.0
|
||||
|
|
@ -163,7 +166,7 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
|
|||
|
||||
- GPU
|
||||
|
||||
对于`GPU`后端，请确保`nvidia-container-toolkit`已经提前安装，以下是`Ubuntu`用户安装指南：
|
||||
For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for `Ubuntu` users:
|
||||
|
||||
```bash
|
||||
DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
|
||||
|
|
@ -174,7 +177,7 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
|
|||
sudo systemctl restart docker
|
||||
```
|
||||
|
||||
编辑文件 daemon.json:
|
||||
Then edit the file daemon.json:
|
||||
|
||||
```bash
|
||||
$ vim /etc/docker/daemon.json
|
||||
|
|
@ -188,21 +191,21 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
|
|||
}
|
||||
```
|
||||
|
||||
再次重启docker:
|
||||
Restart docker again:
|
||||
|
||||
```bash
|
||||
sudo systemctl daemon-reload
|
||||
sudo systemctl restart docker
|
||||
```
|
||||
|
||||
使用以下命令获取并运行最新的稳定镜像：
|
||||
Then you can pull and run the latest stable image using the below command:
|
||||
|
||||
```bash
|
||||
docker pull mindspore/mindspore-gpu:1.1.0
|
||||
docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.1.0 /bin/bash
|
||||
```
|
||||
|
||||
要测试Docker是否正常工作，请运行下面的Python代码并检查输出：
|
||||
To test if the docker image works, please execute the python code below and check the output:
|
||||
|
||||
```python
|
||||
import numpy as np
|
||||
|
|
@ -231,67 +234,71 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore
|
|||
[ 2. 2. 2. 2.]]]
|
||||
```
|
||||
|
||||
如果您想了解更多关于MindSpore Docker镜像的构建过程，请查看[docker](https://gitee.com/mindspore/mindspore/blob/master/scripts/docker/README.md) repo了解详细信息。
|
||||
If you want to learn more about the building process of MindSpore docker images,
|
||||
please check out [docker](https://gitee.com/mindspore/mindspore/blob/master/scripts/docker/README.md) repo for the details.
|
||||
|
||||
## 快速入门
|
||||
## Quickstart
|
||||
|
||||
参考[快速入门](https://www.mindspore.cn/tutorials/zh-CN/master/beginner/quick_start.html)实现图片分类。
|
||||
See the [Quick Start](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html)
|
||||
to implement the image classification.
|
||||
|
||||
## 文档
|
||||
## Docs
|
||||
|
||||
有关安装指南、教程和API的更多详细信息，请参阅[用户文档](https://gitee.com/mindspore/docs)。
|
||||
More details about installation guide, tutorials and APIs, please see the
|
||||
[User Documentation](https://gitee.com/mindspore/docs).
|
||||
|
||||
## 社区
|
||||
## Community
|
||||
|
||||
### 治理
|
||||
### Governance
|
||||
|
||||
查看MindSpore如何进行[开放治理](https://gitee.com/mindspore/community/blob/master/governance.md)。
|
||||
Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/community/blob/master/governance.md).
|
||||
|
||||
### 交流
|
||||
### Communication
|
||||
|
||||
- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w) 开发者交流平台。
|
||||
- `#mindspore`IRC频道（仅用于会议记录）
|
||||
- 视频会议：待定
|
||||
- 邮件列表：<https://mailweb.mindspore.cn/postorius/lists>
|
||||
- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w) - Communication platform for developers.
|
||||
- IRC channel at `#mindspore` (only for meeting minutes logging purpose)
|
||||
- Video Conferencing: TBD
|
||||
- Mailing-list: <https://mailweb.mindspore.cn/postorius/lists>
|
||||
|
||||
## 贡献
|
||||
## Contributing
|
||||
|
||||
欢迎参与贡献。更多详情，请参阅我们的[贡献者Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md)。
|
||||
Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for
|
||||
more details.
|
||||
|
||||
## 分支维护策略
|
||||
## Maintenance phases
|
||||
|
||||
MindSpore的版本分支有以下几种维护阶段：
|
||||
Project stable branches will be in one of the following states:
|
||||
|
||||
| **状态** | **持续时间** | **说明** |
|
||||
| **State** | **Time frame** | **Summary** |
|
||||
|-------------|---------------|--------------------------------------------------|
|
||||
| Planning | 1 - 3 months | 特性规划。 |
|
||||
| Development | 3 months | 特性开发。 |
|
||||
| Maintained | 6 - 12 months | 允许所有问题修复的合入，并发布版本。 |
|
||||
| Unmaintained| 0 - 3 months | 允许所有问题修复的合入，无专人维护，不再发布版本。 |
|
||||
| End Of Life (EOL) | N/A | 不再接受修改合入该分支。 |
|
||||
| Planning | 1 - 3 months | Features are under planning. |
|
||||
| Development | 3 months | Features are under development. |
|
||||
| Maintained | 6 - 12 months | All bugfixes are appropriate. Releases produced. |
|
||||
| Unmaintained| 0 - 3 months | All bugfixes are appropriate. No Maintainers and No Releases produced. |
|
||||
| End Of Life (EOL) | N/A | Branch no longer accepting changes. |
|
||||
|
||||
## 现有分支维护状态
|
||||
## Maintenance status
|
||||
|
||||
| **分支名** | **当前状态** | **上线时间** | **后续状态** | **EOL 日期**|
|
||||
|------------|--------------|----------------------|----------------------------------------|------------|
|
||||
| **r1.6** | Maintained | 2022-01-29 | Unmaintained <br> 2023-01-29 estimated | |
|
||||
| **r1.5** | Maintained | 2021-10-15 | Unmaintained <br> 2022-10-15 estimated | |
|
||||
| **r1.4** | Maintained | 2021-08-15 | Unmaintained <br> 2022-08-15 estimated | |
|
||||
| **r1.3** | Maintained | 2021-07-15 | Unmaintained <br> 2022-07-15 estimated | |
|
||||
| **r1.2** | Unmaintained | 2021-04-15 | End Of Life <br> 2022-04-15 estimated | |
|
||||
| **r1.1** | End Of Life | 2020-12-31 | | 2021-09-30 |
|
||||
| **r1.0** | End Of Life | 2020-09-24 | | 2021-07-30 |
|
||||
| **r0.7** | End Of Life | 2020-08-31 | | 2021-02-28 |
|
||||
| **r0.6** | End Of Life | 2020-07-31 | | 2020-12-30 |
|
||||
| **r0.5** | End Of Life | 2020-06-30 | | 2021-06-30 |
|
||||
| **r0.3** | End Of Life | 2020-05-31 | | 2020-09-30 |
|
||||
| **r0.2** | End Of Life | 2020-04-30 | | 2020-08-31 |
|
||||
| **r0.1** | End Of Life | 2020-03-28 | | 2020-06-30 |
|
||||
| **Branch** | **Status** | **Initial Release Date** | **Next Phase** | **EOL Date**|
|
||||
|------------|--------------|--------------------------|----------------------------------------|-------------|
|
||||
| **r1.6** | Maintained | 2022-01-29 | Unmaintained <br> 2023-01-29 estimated | |
|
||||
| **r1.5** | Maintained | 2021-10-15 | Unmaintained <br> 2022-10-15 estimated | |
|
||||
| **r1.4** | Maintained | 2021-08-15 | Unmaintained <br> 2022-08-15 estimated | |
|
||||
| **r1.3** | Maintained | 2021-07-15 | Unmaintained <br> 2022-07-15 estimated | |
|
||||
| **r1.2** | Unmaintained | 2021-04-15 | End Of Life <br> 2022-04-15 estimated | |
|
||||
| **r1.1** | End Of Life | 2020-12-31 | | 2021-09-30 |
|
||||
| **r1.0** | End Of Life | 2020-09-24 | | 2021-07-30 |
|
||||
| **r0.7** | End Of Life | 2020-08-31 | | 2021-02-28 |
|
||||
| **r0.6** | End Of Life | 2020-07-31 | | 2020-12-30 |
|
||||
| **r0.5** | End Of Life | 2020-06-30 | | 2021-06-30 |
|
||||
| **r0.3** | End Of Life | 2020-05-31 | | 2020-09-30 |
|
||||
| **r0.2** | End Of Life | 2020-04-30 | | 2020-08-31 |
|
||||
| **r0.1** | End Of Life | 2020-03-28 | | 2020-06-30 |
|
||||
|
||||
## 版本说明
|
||||
## Release Notes
|
||||
|
||||
版本说明请参阅[RELEASE](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md)。
|
||||
The release notes, see our [RELEASE](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
|
||||
|
||||
## 许可证
|
||||
## License
|
||||
|
||||
[Apache License 2.0](https://gitee.com/mindspore/mindspore#/mindspore/mindspore/blob/master/LICENSE)
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ from .seed import set_seed, get_seed
|
|||
from .tensor import Tensor, RowTensor, SparseTensor, COOTensor, CSRTensor
|
||||
from .variable import Variable
|
||||
|
||||
# symbols from dtype
|
||||
# declare symbols from dtype
|
||||
__all__ = [
|
||||
"int8", "byte",
|
||||
"int16", "short",
|
||||
|
|
|
|||
Loading…
Reference in New Issue