• What Is MindSpore
• • Automatic Differentiation
  • Automatic Parallel
• Installation
• • Pip mode method installation
  • Source code compilation installation
  • Docker Image
• Quickstart
• Docs
• Community
• • Governance
  • Communication
• Contributing
• Maintenance phases
• Maintenance status
• Release Notes
• License

查看中文

• What Is MindSpore
  • Automatic Differentiation
  • Automatic Parallel
• Installation
  • Pip mode method installation
  • Source code compilation installation
  • Docker Image
• Quickstart
• Docs
• Community
  • Governance
  • Communication
• Contributing
• Maintenance phases
• Maintenance status
• Release Notes
• License

What Is MindSpore

MindSpore is a new open source deep learning training/inference framework that
could be used for mobile, edge and cloud scenarios. MindSpore is designed to
provide development experience with friendly design and efficient execution for
the data scientists and algorithmic engineers, native support for Ascend AI
processor, and software hardware co-optimization. At the meantime MindSpore as
a global AI open source community, aims to further advance the development and
enrichment of the AI software/hardware application ecosystem.

For more details please check out our Architecture Guide.

Automatic Differentiation

There are currently three automatic differentiation techniques in mainstream deep learning frameworks:

• 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.
• 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.
• 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.

TensorFlow 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.

But 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.

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.

Automatic Parallel

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.

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.

Installation

Pip mode method installation

MindSpore offers build options across multiple backends:

Hardware Platform	Operating System	Status
Ascend910	Ubuntu-x86	✔️
	Ubuntu-aarch64	✔️
	EulerOS-aarch64	✔️
	CentOS-x86	✔️
	CentOS-aarch64	✔️
GPU CUDA 10.1	Ubuntu-x86	✔️
CPU	Ubuntu-x86	✔️
	Ubuntu-aarch64	✔️
	Windows-x86	✔️

For installation using pip, take CPU and Ubuntu-x86 build version as an example:

1. Download whl from MindSpore download page, and install the package.

   pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/1.1.0/MindSpore/cpu/ubuntu_x86/mindspore-1.1.0-cp37-cp37m-linux_x86_64.whl
   

2. Run the following command to verify the install.

   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.]
   

Use pip mode method to install MindSpore in different environments. Refer to the following documents.

• Using pip mode method to install MindSpore in Ascend environment
• Using pip mode method to install MindSpore in GPU environment
• Using pip mode method to install MindSpore in CPU environment

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
• Using the source code compilation method to install MindSpore in GPU environment
• Using the source code compilation method to install MindSpore in CPU environment

Docker Image

MindSpore docker image is hosted on Docker Hub,
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.
		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.
		runtime	Runtime environment provided to install MindSpore binary package with GPU CUDA10.1 backend.
Ascend	—	—	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

  For CPU backend, you can directly pull and run the latest stable image using the below command:

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

• GPU

  For GPU backend, please make sure the nvidia-container-toolkit has been installed in advance, here are some install guidelines for Ubuntu users:

  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
  

  Then edit the file daemon.json:

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

  Restart docker again:

  sudo systemctl daemon-reload
  sudo systemctl restart docker
  

  Then you can pull and run the latest stable image using the below command:

  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
  

  To test if the docker image works, please execute the python code below and check the output:

  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.]]]
  

If you want to learn more about the building process of MindSpore docker images,
please check out docker repo for the details.

Quickstart

See the Quick Start
to implement the image classification.

Docs

More details about installation guide, tutorials and APIs, please see the
User Documentation.

Community

Governance

Check out how MindSpore Open Governance works.

Communication

• MindSpore Slack - 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

Contributing

Welcome contributions. See our Contributor Wiki for
more details.

Maintenance phases

Project stable branches will be in one of the following states:

State	Time frame	Summary
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.

Maintenance status

Branch	Status	Initial Release Date	Next Phase	EOL Date
r1.2	Development	2021-03-31 estimated	Maintained 2021-03-31 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

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0