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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/master/docs/MindSpore-architecture.png" alt="MindSpore Architecture"/>
46
47For more details please check out our [Architecture Guide](https://www.mindspore.cn/tutorials/en/master/beginner/introduction.html).
48
49### Automatic Differentiation
50
51Currently, there are two automatic differentiation techniques in mainstream deep learning frameworks:
52
53- **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.
54- **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.
55
56PyTorch 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.
57
58MindSpore 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.
59
60<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-differentiation.png" alt="Automatic Differentiation" width="600"/>
61
62The 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.
63
64### Automatic Parallel
65
66The 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.
67
68<img src="https://gitee.com/mindspore/mindspore/raw/master/docs/Automatic-parallel.png" alt="Automatic Parallel" width="600"/>
69
70At 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.
71
72## Installation
73
74### Pip mode method installation
75
76MindSpore offers build options across multiple backends:
77
78| Hardware Platform | Operating System | Status |
79| :---------------- | :--------------- | :----- |
80| Ascend910 | Ubuntu-x86 | ✔️ |
81|  | Ubuntu-aarch64 | ✔️ |
82|  | EulerOS-aarch64 | ✔️ |
83|  | CentOS-x86 | ✔️ |
84|  | CentOS-aarch64 | ✔️ |
85| GPU CUDA 10.1 | Ubuntu-x86 | ✔️ |
86| CPU | Ubuntu-x86 | ✔️ |
87|  | Ubuntu-aarch64 | ✔️ |
88|  | Windows-x86 | ✔️ |
89
90For installation using `pip`, take `CPU` and `Ubuntu-x86` build version as an example:
91
921. Download whl from [MindSpore download page](https://www.mindspore.cn/versions/en), and install the package.
93
94    ```bash
95    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
96    ```
97
982. Run the following command to verify the install.
99
100    ```python
101    import numpy as np
102    import mindspore.context as context
103    import mindspore.nn as nn
104    from mindspore import Tensor
105    from mindspore.ops import operations as P
106
107    context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
108
109    class Mul(nn.Cell):
110        def __init__(self):
111            super(Mul, self).__init__()
112            self.mul = P.Mul()
113
114        def construct(self, x, y):
115            return self.mul(x, y)
116
117    x = Tensor(np.array([1.0, 2.0, 3.0]).astype(np.float32))
118    y = Tensor(np.array([4.0, 5.0, 6.0]).astype(np.float32))
119
120    mul = Mul()
121    print(mul(x, y))
122    ```
123
124    ```text
125    [ 4. 10. 18.]
126    ```
127
128Use pip mode method to install MindSpore in different environments. Refer to the following documents.
129
130- [Using pip mode method to install MindSpore in Ascend environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_ascend_install_pip_en.md)
131- [Using pip mode method to install MindSpore in GPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_gpu_install_pip_en.md)
132- [Using pip mode method to install MindSpore in CPU environment](https://gitee.com/mindspore/docs/blob/master/install/mindspore_cpu_install_pip_en.md)
133
134### Source code compilation installation
135
136Use the source code compilation method to install MindSpore in different environments. Refer to the following documents.
137
138- [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)
139- [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)
140- [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)
141
142### Docker Image
143
144MindSpore docker image is hosted on [Docker Hub](https://hub.docker.com/r/mindspore),
145currently the containerized build options are supported as follows:
146
147| Hardware Platform | Docker Image Repository | Tag | Description |
148| :---------------- | :---------------------- | :-- | :---------- |
149| CPU | `mindspore/mindspore-cpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` CPU release. |
150|  |  | `devel` | Development environment provided to build MindSpore (with `CPU` backend) from the source, refer to <https://www.mindspore.cn/install/en> for installation details. |
151|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `CPU` backend. |
152| GPU | `mindspore/mindspore-gpu` | `x.y.z` | Production environment with pre-installed MindSpore `x.y.z` GPU release. |
153|  |  | `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. |
154|  |  | `runtime` | Runtime environment provided to install MindSpore binary package with `GPU CUDA10.1` backend. |
155
156> **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.
157
158- CPU
159
160    For `CPU` backend, you can directly pull and run the latest stable image using the below command:
161
162    ```bash
163    docker pull mindspore/mindspore-cpu:1.1.0
164    docker run -it mindspore/mindspore-cpu:1.1.0 /bin/bash
165    ```
166
167- GPU
168
169    For `GPU` backend, please make sure the `nvidia-container-toolkit` has been installed in advance, here are some install guidelines for `Ubuntu` users:
170
171    ```bash
172    DISTRIBUTION=$(. /etc/os-release; echo $ID$VERSION_ID)
173    curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | apt-key add -
174    curl -s -L https://nvidia.github.io/nvidia-docker/$DISTRIBUTION/nvidia-docker.list | tee /etc/apt/sources.list.d/nvidia-docker.list
175
176    sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit nvidia-docker2
177    sudo systemctl restart docker
178    ```
179
180    Then edit the file daemon.json:
181
182    ```bash
183    $ vim /etc/docker/daemon.json
184    {
185        "runtimes": {
186            "nvidia": {
187                "path": "nvidia-container-runtime",
188                "runtimeArgs": []
189            }
190        }
191    }
192    ```
193
194    Restart docker again:
195
196    ```bash
197    sudo systemctl daemon-reload
198    sudo systemctl restart docker
199    ```
200
201    Then you can pull and run the latest stable image using the below command:
202
203    ```bash
204    docker pull mindspore/mindspore-gpu:1.1.0
205    docker run -it -v /dev/shm:/dev/shm --runtime=nvidia --privileged=true mindspore/mindspore-gpu:1.1.0 /bin/bash
206    ```
207
208    To test if the docker image works, please execute the python code below and check the output:
209
210    ```python
211    import numpy as np
212    import mindspore.context as context
213    from mindspore import Tensor
214    from mindspore.ops import functional as F
215
216    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
217
218    x = Tensor(np.ones([1,3,3,4]).astype(np.float32))
219    y = Tensor(np.ones([1,3,3,4]).astype(np.float32))
220    print(F.tensor_add(x, y))
221    ```
222
223    ```text
224    [[[ 2.  2.  2.  2.],
225    [ 2.  2.  2.  2.],
226    [ 2.  2.  2.  2.]],
227
228    [[ 2.  2.  2.  2.],
229    [ 2.  2.  2.  2.],
230    [ 2.  2.  2.  2.]],
231
232    [[ 2.  2.  2.  2.],
233    [ 2.  2.  2.  2.],
234    [ 2.  2.  2.  2.]]]
235    ```
236
237If you want to learn more about the building process of MindSpore docker images,
238please check out [docker](https://gitee.com/mindspore/mindspore/blob/master/scripts/docker/README.md) repo for the details.
239
240## Quickstart
241
242See the [Quick Start](https://www.mindspore.cn/tutorials/en/master/beginner/quick_start.html)
243to implement the image classification.
244
245## Docs
246
247More details about installation guide, tutorials and APIs, please see the
248[User Documentation](https://gitee.com/mindspore/docs).
249
250## Community
251
252### Governance
253
254Check out how MindSpore Open Governance [works](https://gitee.com/mindspore/community/blob/master/governance.md).
255
256### Communication
257
258- [MindSpore Slack](https://join.slack.com/t/mindspore/shared_invite/zt-dgk65rli-3ex4xvS4wHX7UDmsQmfu8w) - Communication platform for developers.
259- IRC channel at `#mindspore` (only for meeting minutes logging purpose)
260- Video Conferencing: TBD
261- Mailing-list: <https://mailweb.mindspore.cn/postorius/lists>
262
263## Contributing
264
265Welcome contributions. See our [Contributor Wiki](https://gitee.com/mindspore/mindspore/blob/master/CONTRIBUTING.md) for
266more details.
267
268## Maintenance phases
269
270Project stable branches will be in one of the following states:
271
272| **State**       | **Time frame**    | **Summary**                                          |
273|-------------|---------------|--------------------------------------------------|
274| Planning    | 1 - 3 months  | Features are under planning.                     |
275| Development | 3 months      | Features are under development.                  |
276| Maintained  | 6 - 12 months | All bugfixes are appropriate. Releases produced. |
277| Unmaintained| 0 - 3 months  | All bugfixes are appropriate. No Maintainers and No Releases produced.                                                 |
278| End Of Life (EOL) |  N/A |  Branch no longer accepting changes.    |
279
280## Maintenance status
281
282| **Branch** | **Status**   | **Initial Release Date** | **Next Phase**                         | **EOL Date**|
283|------------|--------------|--------------------------|----------------------------------------|-------------|
284| **r1.8**   | Maintained   | 2022-07-29               | Unmaintained <br> 2023-07-29 estimated |             |
285| **r1.7**   | Maintained   | 2022-04-29               | Unmaintained <br> 2023-04-29 estimated |             |
286| **r1.6**   | Maintained   | 2022-01-29               | Unmaintained <br> 2023-01-29 estimated |             |
287| **r1.5**   | Maintained   | 2021-10-15               | Unmaintained <br> 2022-10-15 estimated |             |
288| **r1.4**   | Maintained   | 2021-08-15               | Unmaintained <br> 2022-08-15 estimated |             |
289| **r1.3**   | End Of Life  | 2021-07-15               |                                        | 2022-07-15  |
290| **r1.2**   | End Of Life  | 2021-04-15               |                                        | 2022-04-29  |
291| **r1.1**   | End Of Life  | 2020-12-31               |                                        | 2021-09-30  |
292| **r1.0**   | End Of Life  | 2020-09-24               |                                        | 2021-07-30  |
293| **r0.7**   | End Of Life  | 2020-08-31               |                                        | 2021-02-28  |
294| **r0.6**   | End Of Life  | 2020-07-31               |                                        | 2020-12-30  |
295| **r0.5**   | End Of Life  | 2020-06-30               |                                        | 2021-06-30  |
296| **r0.3**   | End Of Life  | 2020-05-31               |                                        | 2020-09-30  |
297| **r0.2**   | End Of Life  | 2020-04-30               |                                        | 2020-08-31  |
298| **r0.1**   | End Of Life  | 2020-03-28               |                                        | 2020-06-30  |
299
300## Release Notes
301
302The release notes, see our [RELEASE](https://gitee.com/mindspore/mindspore/blob/master/RELEASE.md).
303
304## License
305
306[Apache License 2.0](https://gitee.com/mindspore/mindspore/blob/master/LICENSE)
307