From 1d4e012ff9d718feac5fda9501f7afd70de9f6d8 Mon Sep 17 00:00:00 2001 From: lvmingfu Date: Wed, 25 Nov 2020 17:07:26 +0800 Subject: [PATCH] update install contents in readme.md --- README.md | 71 +++++++++++++++++++++++++++++---------------- README_CN.md | 81 +++++++++++++++++++++++++++++++++------------------- 2 files changed, 99 insertions(+), 53 deletions(-) diff --git a/README.md b/README.md index d03f936805c..4ccba34c970 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,15 @@ ![MindSpore Logo](docs/MindSpore-logo.png "MindSpore logo") -============================================================ [查看中文](./README_CN.md) + + - [What Is MindSpore](#what-is-mindspore) - [Automatic Differentiation](#automatic-differentiation) - [Automatic Parallel](#automatic-parallel) - [Installation](#installation) - - [Binaries](#binaries) - - [From Source](#from-source) + - [Pip mode method installation](#pip-mode-method-installation) + - [Source code compilation installation](#source-code-compilation-installation) - [Docker Image](#docker-image) - [Quickstart](#quickstart) - [Docs](#docs) @@ -16,9 +17,13 @@ - [Governance](#governance) - [Communication](#communication) - [Contributing](#contributing) +- [Maintenance phases](#maintenance-phases) +- [Maintenance status](#maintenance-status) - [Release Notes](#release-notes) - [License](#license) + + ## What Is MindSpore MindSpore is a new open source deep learning training/inference framework that @@ -59,7 +64,7 @@ At present, MindSpore uses a fine-grained parallel strategy of splitting operato ## Installation -### Binaries +### Pip mode method installation MindSpore offers build options across multiple backends: @@ -80,7 +85,7 @@ For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an ex 1. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package. - ``` + ```bash pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl ``` @@ -109,13 +114,24 @@ For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an ex mul = Mul() print(mul(x, y)) ``` - ``` + + ```text [ 4. 10. 18.] ``` -### From Source +Use pip mode method to install MindSpore in different environments. Refer to the following documents. -[Install MindSpore](https://www.mindspore.cn/install/en). +- [Using pip mode method to install MindSpore in Ascend environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip_en.md) +- [Using pip mode method to install MindSpore in GPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip_en.md) +- [Using pip mode method to install MindSpore in CPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip_en.md) + +### Source code compilation installation + +Use the source code compilation method to install MindSpore in different environments. Refer to the following documents. + +- [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) +- [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) +- [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) ### Docker Image @@ -125,27 +141,29 @@ currently the containerized build options are supported as follows: | Hardware Platform | Docker Image Repository | Tag | Description | | :---------------- | :---------------------- | :-- | :---------- | | CPU | `mindspore/mindspore-cpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` CPU release. | -| | | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to https://www.mindspore.cn/install/en for installation details. | +| | | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to for installation details. | | | | `runtime` | Runtime environment provided to install MindSpore binary package with `CPU` backend. | | 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. | +| | | `devel` | Development environment provided to build MindSpore (with `GPU CUDA10.1` backend) from the source, refer to for installation details. | | | | `runtime` | Runtime environment provided to install MindSpore binary package with `GPU CUDA10.1` backend. | | Ascend |
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| Coming soon. | > **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 +- CPU For `CPU` backend, you can directly pull and run the latest stable image using the below command: - ``` + + ```bash docker pull mindspore/mindspore-cpu:1.0.0 docker run -it mindspore/mindspore-cpu:1.0.0 /bin/bash ``` -* GPU +- GPU 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) 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 @@ -153,8 +171,10 @@ currently the containerized build options are supported as follows: sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2 sudo systemctl restart docker ``` + Then edit the file daemon.json: - ``` + + ```bash $ vim /etc/docker/daemon.json { "runtimes": { @@ -165,18 +185,23 @@ currently the containerized build options are supported as follows: } } ``` + Restart docker again: - ``` + + ```bash sudo systemctl daemon-reload sudo systemctl restart docker ``` + Then you can pull and run the latest stable image using the below command: - ``` + + ```bash docker pull mindspore/mindspore-gpu:1.0.0 docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.0.0 /bin/bash ``` To test if the docker image works, please execute the python code below and check the output: + ```python import numpy as np import mindspore.context as context @@ -189,7 +214,8 @@ currently the containerized build options are supported as follows: y = Tensor(np.ones([1,3,3,4]).astype(np.float32)) print(F.tensor_add(x, y)) ``` - ``` + + ```text [[[ 2. 2. 2. 2.], [ 2. 2. 2. 2.], [ 2. 2. 2. 2.]], @@ -208,7 +234,7 @@ please check out [docker](docker/README.md) repo for the details. ## Quickstart -See the [Quick Start](https://www.mindspore.cn/tutorial/training/en/master/quick_start/quick_start.html) +See the [Quick Start](https://www.mindspore.cn/tutorial/training/en/master/quick_start/quick_start.html) to implement the image classification. ## Docs @@ -235,6 +261,7 @@ Welcome contributions. See our [Contributor Wiki](CONTRIBUTING.md) for more details. ## Maintenance phases + Project stable branches will be in one of the following states: | **State** | **Time frame** | **Summary** | |-------------|---------------|--------------------------------------------------| @@ -245,6 +272,7 @@ Project stable branches will be in one of the following states: | End Of Life (EOL) | N/A | Branch no longer accepting changes. | ## Maintenance status + | **Branch** | **Status** | **Initial Release Date** | **Next Phase** | **EOL Date** | |--------|--------------|----------------------|-----------------------------------|------------| | **r1.1** | Development | 2020-12-31 estimated | Maintained
2020-12-31 estimated | | @@ -256,11 +284,6 @@ Project stable branches will be in one of the following states: | **r0.2** | End Of Life | 2020-04-30 | | 2020-08-31 | | **r0.1** | End Of Life | 2020-03-28 | | 2020-06-30 | - - - - - ## Release Notes The release notes, see our [RELEASE](RELEASE.md). diff --git a/README_CN.md b/README_CN.md index bcea2bff72a..fdb87a2aa7b 100644 --- a/README_CN.md +++ b/README_CN.md @@ -1,14 +1,15 @@ ![MindSpore标志](docs/MindSpore-logo.png "MindSpore logo") -============================================================ [View English](./README.md) + + - [MindSpore介绍](#mindspore介绍) - [自动微分](#自动微分) - [自动并行](#自动并行) - [安装](#安装) - - [二进制文件](#二进制文件) - - [来源](#来源) + - [pip方式安装](#pip方式安装) + - [源码编译方式安装](#源码编译方式安装) - [Docker镜像](#docker镜像) - [快速入门](#快速入门) - [文档](#文档) @@ -16,9 +17,13 @@ - [治理](#治理) - [交流](#交流) - [贡献](#贡献) +- [分支维护策略](#分支维护策略) +- [现有分支维护状态](#现有分支维护状态) - [版本说明](#版本说明) - [许可证](#许可证) + + ## 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。 @@ -56,7 +61,7 @@ MindSpore自动并行的目的是构建数据并行、模型并行和混合并 ## 安装 -### 二进制文件 +### pip方式安装 MindSpore提供跨多个后端的构建选项： @@ -77,7 +82,7 @@ MindSpore提供跨多个后端的构建选项： 1. 请从[MindSpore下载页面](https://www.mindspore.cn/versions)下载并安装whl包。 - ``` + ```bash pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.0.0/MindSpore/cpu/ubuntu_x86/mindspore-1.0.0-cp37-cp37m-linux_x86_64.whl ``` @@ -89,29 +94,41 @@ MindSpore提供跨多个后端的构建选项： 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)) ``` - ``` + + ```text [ 4. 10. 18.] ``` -### 来源 -[MindSpore安装](https://www.mindspore.cn/install)。 +使用pip方式，在不同的环境安装MindSpore，可参考以下文档。 + +- [Ascend环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip.md) +- [GPU环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip.md) +- [CPU环境使用pip方式安装MindSpore](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip.md) + +### 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。 + +- [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) +- [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) +- [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) ### Docker镜像 @@ -121,27 +138,29 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore | 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 。 | +| | | `devel` | 提供开发环境从源头构建MindSpore（`CPU`后端）。安装详情请参考 。 | | | | `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 。 | +| | | `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 。 | | | | `runtime` | 提供运行时环境安装MindSpore二进制包（`GPU CUDA10.1`后端）。 | | Ascend |
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| 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 +- 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： - ``` + + ```bash docker pull mindspore/mindspore-cpu:1.0.0 docker run -it mindspore/mindspore-cpu:1.0.0 /bin/bash ``` -* GPU +- 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： - ``` + + ```bash 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 @@ -149,8 +168,10 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2 sudo systemctl restart docker ``` + 编辑文件 daemon.json: - ``` + + ```bash $ vim /etc/docker/daemon.json { "runtimes": { @@ -161,18 +182,23 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore } } ``` + 再次重启docker: - ``` + + ```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： - ``` + + ```bash docker pull mindspore/mindspore-gpu:1.0.0 docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.0.0 /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： + ```python import numpy as np import mindspore.context as context @@ -185,7 +211,8 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore y = Tensor(np.ones([1,3,3,4]).astype(np.float32)) print(F.tensor_add(x, y)) ``` - ``` + + ```text [[[ 2. 2. 2. 2.], [ 2. 2. 2. 2.], [ 2. 2. 2. 2.]], @@ -205,7 +232,6 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore 参考[快速入门](https://www.mindspore.cn/tutorial/training/zh-CN/master/quick_start/quick_start.html)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](https://gitee.com/mindspore/docs)。 @@ -228,6 +254,7 @@ MindSpore的Docker镜像托管在[Docker Hub](https://hub.docker.com/r/mindspore 欢迎参与贡献。更多详情，请参阅我们的[贡献者Wiki](CONTRIBUTING.md)。 ## 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** | |-------------|---------------|--------------------------------------------------| @@ -238,6 +265,7 @@ MindSpore的版本分支有以下几种维护阶段： | 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** | |--------|--------------|----------------------|-----------------------------------|------------| | **r1.1** | Development | 2020-12-31 estimated | Maintained
2020-12-31 estimated | | @@ -249,11 +277,6 @@ MindSpore的版本分支有以下几种维护阶段： | **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](RELEASE.md)。