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Apache-2.0

MindSpore标志

PyPI - Python Version PyPI Downloads DockerHub LICENSE Slack PRs Welcome

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MindSpore介绍

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。 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。

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。

MindSpore Architecture

欲了解更多详情,请查看我们的总体架构

自动微分

当前主流深度学习框架中有三种自动微分技术:

  • 基于静态计算图的转换:编译时将网络转换为静态数据流图,将链式法则应用于数据流图,实现自动微分。
  • 基于动态计算图的转换: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。
  • 基于源码的转换: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。

TensorFlow早期采用的是静态计算图,PyTorch采用的是动态计算图。静态映射可以利用静态编译技术来优化网络性能,但是构建网络或调试网络非常复杂。动态图的使用非常方便,但很难实现性能的极限优化。

MindSpore找到了另一种方法,即基于源代码转换的自动微分。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。

Automatic Differentiation

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。

自动并行

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。

Automatic Parallel

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。

安装

pip方式安装

MindSpore提供跨多个后端的构建选项:

硬件平台 操作系统 状态
Ascend 910 Ubuntu-x86 ✔️
Ubuntu-aarch64 ✔️
EulerOS-aarch64 ✔️
CentOS-x86 ✔️
CentOS-aarch64 ✔️
GPU CUDA 10.1 Ubuntu-x86 ✔️
CPU Ubuntu-x86 ✔️
Ubuntu-aarch64 ✔️
Windows-x86 ✔️

使用pip命令安装,以CPUUbuntu-x86build版本为例:

  1. 请从MindSpore下载页面下载并安装whl包。

    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
  2. 执行以下命令,验证安装结果。

    import numpy as np
    import mindspore.context as context
    import mindspore.nn as nn
    from mindspore import Tensor
    from mindspore.ops import operations as P
    
    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
    
    class Mul(nn.Cell):
        def __init__(self):
            super(Mul, self).__init__()
            self.mul = P.Mul()
    
        def construct(self, x, y):
            return self.mul(x, y)
    
    x = Tensor(np.array([1.0, 2.0, 3.0]).astype(np.float32))
    y = Tensor(np.array([4.0, 5.0, 6.0]).astype(np.float32))
    
    mul = Mul()
    print(mul(x, y))
    [ 4. 10. 18.]

使用pip方式,在不同的环境安装MindSpore,可参考以下文档。

源码编译方式安装

使用源码编译方式,在不同的环境安装MindSpore,可参考以下文档。

Docker镜像

MindSpore的Docker镜像托管在Docker Hub上。 目前容器化构建选项支持情况如下:

硬件平台 Docker镜像仓库 标签 说明
CPU mindspore/mindspore-cpu x.y.z 已经预安装MindSpore x.y.z CPU版本的生产环境。
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
runtime 提供运行时环境安装MindSpore二进制包(CPU后端)。
GPU mindspore/mindspore-gpu x.y.z 已经预安装MindSpore x.y.z GPU版本的生产环境。
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
runtime 提供运行时环境安装MindSpore二进制包(GPU CUDA10.1后端)。
Ascend 即将推出,敬请期待。

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。

  • 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:

    docker pull mindspore/mindspore-cpu:1.1.0
    docker run -it mindspore/mindspore-cpu:1.1.0 /bin/bash
  • 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:

    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
    
    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
    sudo systemctl restart docker

    编辑文件 daemon.json:

    $ vim /etc/docker/daemon.json
    {
        "runtimes": {
            "nvidia": {
                "path": "nvidia-container-runtime",
                "runtimeArgs": []
            }
        }
    }

    再次重启docker:

    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:

    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:

    import numpy as np
    import mindspore.context as context
    from mindspore import Tensor
    from mindspore.ops import functional as F
    
    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    
    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
    print(F.tensor_add(x, y))
    [[[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],
    
    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.]],
    
    [[ 2.  2.  2.  2.],
    [ 2.  2.  2.  2.],
    [ 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 repo了解详细信息。

快速入门

参考快速入门实现图片分类。

文档

有关安装指南、教程和API的更多详细信息,请参阅用户文档

社区

治理

查看MindSpore如何进行开放治理

交流

贡献

欢迎参与贡献。更多详情,请参阅我们的贡献者Wiki

分支维护策略

MindSpore的版本分支有以下几种维护阶段:

状态 持续时间 说明
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。

现有分支维护状态

分支名 当前状态 上线时间 后续状态 EOL 日期
r1.2 Maintained 2021-04-15 Unmaintained
2021-10-15 estimated
r1.1 Maintained 2020-12-31 Unmaintained
2021-06-30 estimated
r1.0 Maintained 2020-09-24 Unmaintained
2021-03-30 estimated
r0.7 Unmaintained 2020-08-31 End Of Life
2021-02-28 estimated
r0.6 End Of Life 2020-07-31 2020-12-30
r0.5 Maintained 2020-06-30 Unmaintained
2021-06-30 estimated
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

版本说明请参阅RELEASE

许可证

Apache License 2.0

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MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios. 展开 收起
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