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1![MindSpore Logo](https://gitee.com/mindspore/mindspore/raw/master/docs/MindSpore-logo.png "MindSpore logo")
2
3[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mindspore.svg)](https://pypi.org/project/mindspore)
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10
11[查看中文](./README_CN.md)
12
13<!-- TOC -->
14
15- [What Is MindSpore](#what-is-mindspore)
16    - [Automatic Differentiation](#automatic-differentiation)
17    - [Automatic Parallel](#automatic-parallel)
18- [Installation](#installation)
19    - [Pip mode method installation](#pip-mode-method-installation)
20    - [Source code compilation installation](#source-code-compilation-installation)
21    - [Docker Image](#docker-image)
22- [Quickstart](#quickstart)
23- [Docs](#docs)
24- [Community](#community)
25    - [Governance](#governance)
26    - [Communication](#communication)
27- [Contributing](#contributing)
28- [Maintenance phases](#maintenance-phases)
29- [Maintenance status](#maintenance-status)
30- [Release Notes](#release-notes)
31- [License](#license)
32
33<!-- /TOC -->
34
35## What Is MindSpore
36
37MindSpore is a new open source deep learning training/inference framework that
38could be used for mobile, edge and cloud scenarios. MindSpore is designed to
39provide development experience with friendly design and efficient execution for
40the data scientists and algorithmic engineers, native support for Ascend AI
41processor, and software hardware co-optimization. At the meantime MindSpore as
42a global AI open source community, aims to further advance the development and
43enrichment of the AI software/hardware application ecosystem.
44
45<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/MindSpore-architecture.png" alt="MindSpore Architecture" width="600"/>
46
47For more details please check out our [Architecture Guide](https://www.mindspore.cn/docs/programming_guide/en/r1.5/architecture.html).
48
49### Automatic Differentiation
50
51There are currently three automatic differentiation techniques in mainstream deep learning frameworks:
52
53- **Conversion based on static compute graph**: Convert the network into a static data flow graph at compile time, then turn the chain rule into a data flow graph to implement automatic differentiation.
54- **Conversion based on dynamic compute graph**: 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.
55- **Conversion based on source code**: 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.
56
57TensorFlow adopted static calculation diagrams in the early days, whereas PyTorch used dynamic calculation diagrams. Static maps can utilize static compilation technology to optimize network performance, however, building a network or debugging it is very complicated. The use of dynamic graphics is very convenient, but it is difficult to achieve extreme optimization in performance.
58
59But MindSpore finds another way, automatic differentiation based on source code conversion. 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.
60
61<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
62
63The 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.
64
65### Automatic Parallel
66
67The 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.
68
69<img src="https://gitee.com/mindspore/mindspore/raw/r1.5/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
70
71At 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.
72
73## Installation
74
75### Pip mode method installation
76
77MindSpore offers build options across multiple backends:
78
79| Hardware Platform | Operating System | Status |
80| :---------------- | :--------------- | :----- |
81| Ascend910 | Ubuntu-x86 | ✔️ |
82|  | Ubuntu-aarch64 | ✔️ |
83|  | EulerOS-aarch64 | ✔️ |
84|  | CentOS-x86 | ✔️ |
85|  | CentOS-aarch64 | ✔️ |
86| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
87| CPU | Ubuntu-x86 | ✔️ |
88|  | Ubuntu-aarch64 | ✔️ |
89|  | Windows-x86 | ✔️ |
90
91For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
92
931. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
94
95    ```bash
96    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
97    ```
98
992. Run the following command to verify the install.
100
101    ```python
102    import numpy as np
103    import mindspore.context as context
104    import mindspore.nn as nn
105    from mindspore import Tensor
106    from mindspore.ops import operations as P
107
108    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
109
110    class Mul(nn.Cell):
111        def __init__(self):
112            super(Mul, self).__init__()
113            self.mul = P.Mul()
114
115        def construct(self, x, y):
116            return self.mul(x, y)
117
118    x = Tensor(np.array([1.0, 2.0, 3.0]).astype(np.float32))
119    y = Tensor(np.array([4.0, 5.0, 6.0]).astype(np.float32))
120
121    mul = Mul()
122    print(mul(x, y))
123    ```
124
125    ```text
126    [ 4. 10. 18.]
127    ```
128
129Use pip mode method to install MindSpore in different environments. Refer to the following documents.
130
131- [Using pip mode method to install MindSpore in Ascend environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip_en.md)
132- [Using pip mode method to install MindSpore in GPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip_en.md)
133- [Using pip mode method to install MindSpore in CPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip_en.md)
134
135### Source code compilation installation
136
137Use the source code compilation method to install MindSpore in different environments. Refer to the following documents.
138
139- [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)
140- [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)
141- [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)
142
143### Docker Image
144
145MindSpore docker image is hosted on [Docker Hub](https://hub.docker.com/r/mindspore),
146currently the containerized build options are supported as follows:
147
148| Hardware Platform | Docker Image Repository | Tag | Description |
149| :---------------- | :---------------------- | :-- | :---------- |
150| CPU | `mindspore/mindspore-cpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` CPU release. |
151|  |  | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to <https://www.mindspore.cn/install/en> for installation details. |
152|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `CPU` backend. |
153| GPU | `mindspore/mindspore-gpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` GPU release. |
154|  |  | `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. |
155|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `GPU CUDA10.1` backend. |
156
157> **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.
158
159- CPU
160
161    For `CPU` backend, you can directly pull and run the latest stable image using the below command:
162
163    ```bash
164    docker pull mindspore/mindspore-cpu:1.1.0
165    docker run -it mindspore/mindspore-cpu:1.1.0 /bin/bash
166    ```
167
168- GPU
169
170    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for `Ubuntu` users:
171
172    ```bash
173    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
174    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
175    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
176
177    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
178    sudo systemctl restart docker
179    ```
180
181    Then edit the file daemon.json:
182
183    ```bash
184    $ vim /etc/docker/daemon.json
185    {
186        "runtimes": {
187            "nvidia": {
188                "path": "nvidia-container-runtime",
189                "runtimeArgs": []
190            }
191        }
192    }
193    ```
194
195    Restart docker again:
196
197    ```bash
198    sudo systemctl daemon-reload
199    sudo systemctl restart docker
200    ```
201
202    Then you can pull and run the latest stable image using the below command:
203
204    ```bash
205    docker pull mindspore/mindspore-gpu:1.1.0
206    docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.1.0 /bin/bash
207    ```
208
209    To test if the docker image works, please execute the python code below and check the output:
210
211    ```python
212    import numpy as np
213    import mindspore.context as context
214    from mindspore import Tensor
215    from mindspore.ops import functional as F
216
217    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
218
219    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
220    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
221    print(F.tensor_add(x, y))
222    ```
223
224    ```text
225    [[[ 2.  2.  2.  2.],
226    [ 2.  2.  2.  2.],
227    [ 2.  2.  2.  2.]],
228
229    [[ 2.  2.  2.  2.],
230    [ 2.  2.  2.  2.],
231    [ 2.  2.  2.  2.]],
232
233    [[ 2.  2.  2.  2.],
234    [ 2.  2.  2.  2.],
235    [ 2.  2.  2.  2.]]]
236    ```
237
238If you want to learn more about the building process of MindSpore docker images,
239please check out [docker](https://gitee.com/mindspore/mindspore/blob/r1.5/scripts/docker/README.md) repo for the details.
240
241## Quickstart
242
243See the [Quick Start](https://www.mindspore.cn/tutorials/en/r1.5/quick_start.html)
244to implement the image classification.
245
246## Docs
247
248More details about installation guide, tutorials and APIs, please see the
249[User Documentation](https://gitee.com/mindspore/docs).
250
251## Community
252
253### Governance
254
255Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/community/blob/master/governance.md).
256
257### Communication
258
259- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w) - Communication platform for developers.
260- IRC channel at `#mindspore` (only for meeting minutes logging purpose)
261- Video Conferencing: TBD
262- Mailing-list: <https://mailweb.mindspore.cn/postorius/lists>
263
264## Contributing
265
266Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for
267more details.
268
269## Maintenance phases
270
271Project stable branches will be in one of the following states:
272
273| **State**       | **Time frame**    | **Summary**                                          |
274|-------------|---------------|--------------------------------------------------|
275| Planning    | 1 - 3 months  | Features are under planning.                     |
276| Development | 3 months      | Features are under development.                  |
277| Maintained  | 6 - 12 months | All bugfixes are appropriate. Releases produced. |
278| Unmaintained| 0 - 3 months  | All bugfixes are appropriate. No Maintainers and No Releases produced.                                                 |
279| End Of Life (EOL) |  N/A |  Branch no longer accepting changes.    |
280
281## Maintenance status
282
283| **Branch** | **Status**   | **Initial Release Date** | **Next Phase**                         | **EOL Date**|
284|------------|--------------|--------------------------|----------------------------------------|-------------|
285| **r1.5**   | Maintained   | 2021-10-15               | Unmaintained <br> 2022-10-15 estimated |             |
286| **r1.4**   | Maintained   | 2021-08-15               | Unmaintained <br> 2022-08-15 estimated |             |
287| **r1.3**   | Maintained   | 2021-07-15               | Unmaintained <br> 2022-07-15 estimated |             |
288| **r1.2**   | Unmaintained | 2021-04-15               | End Of Life  <br> 2022-04-15 estimated |             |
289| **r1.1**   | End Of Life  | 2020-12-31               |                                        | 2021-09-30  |
290| **r1.0**   | End Of Life  | 2020-09-24               |                                        | 2021-07-30  |
291| **r0.7**   | End Of Life  | 2020-08-31               |                                        | 2021-02-28  |
292| **r0.6**   | End Of Life  | 2020-07-31               |                                        | 2020-12-30  |
293| **r0.5**   | End Of Life  | 2020-06-30               |                                        | 2021-06-30  |
294| **r0.3**   | End Of Life  | 2020-05-31               |                                        | 2020-09-30  |
295| **r0.2**   | End Of Life  | 2020-04-30               |                                        | 2020-08-31  |
296| **r0.1**   | End Of Life  | 2020-03-28               |                                        | 2020-06-30  |
297
298## Release Notes
299
300The release notes, see our [RELEASE](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
301
302## License
303
304[Apache License 2.0](https://gitee.com/mindspore/mindspore#/mindspore/mindspore/blob/master/LICENSE)
305