MindSpore

What is MindSpore?

MindSpore is a new open-source, full-scenario deep learning framework launched by Huawei, applicable to edge-cloud scenarios. MindSpore boasts powerful distributed training capabilities, incorporates multiple parallel strategies, and simplifies the development of large models. It is deeply integrated with Ascend processors, fully leveraging hardware performance to shorten training time and improve inference efficiency. MindSpore supports the integration of AI and high-performance computing (HPC), meeting the needs of AI for Science scenarios. The MindSpore ecosystem is rich, providing open-source projects, case studies, and state-of-the-art (SOTA) models, facilitating rapid development and application by developers.

MindSpore’s main functions

MindSpore, a full-scenario AI framework , supports rapid deployment in the cloud, edge, and device, and is compatible with various hardware platforms such as Ascend processors, GPUs, and CPUs.
Distributed training : It has multiple built-in parallel capabilities, provides a simple and easy-to-use interface for configuring distributed strategies for large models, and supports efficient training of large models.
AI4S converged computing framework : Supports full-process programmability of AI + HPC, meeting the heterogeneous parallel acceleration needs of AI for Science scenarios.
Unleashing Hardware Potential : Unified static and dynamic programming, optimally matched with Ascend processors, helps developers shorten training time and improve inference performance.
Installation and Usage : Provides detailed installation guides and commands, and supports multiple hardware platforms.
Cloud platform support : Provides cloud platform support to help users quickly create and deploy models and manage the entire AI workflow.
Ecosystem resources : Provides open source projects, case collections, state-of-the-art models and their derivative tools, and supports multiple domain suites (such as NLP, CV, audio, etc.).

How to use MindSpore

Environmental preparation :
- Hardware environment : MindSpore supports a variety of hardware platforms, including Ascend processors, GPUs (such as NVIDIA series), and CPUs (such as Intel x86 and Arm architecture).
- Software environment :
  - Operating System : Linux (such as Ubuntu 18.04 or CentOS 7.6 and above) is recommended, but Windows and macOS are also supported.
  - Python environment : Python 3.7 or later is recommended.
Install MindSpore :
- Installation method : Select the appropriate installation command based on the hardware platform you are using. Here are some common installation methods:
  - Install CPU version :

pip < span class = “token function” > install < /span > mindspore
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- - Install the GPU version (using CUDA 11.1 as an example) :

pip < span class = “token function” > install < /span > mindspore-gpu < span class = “token operator” > == < /span >< span class = “token number” > 1.10 < /span > .0
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- - Install Ascend version :

pip < span class = “token function” > install < /span > https://ms-release.obs.cn-north-4.myhuaweicloud.com/2.6.0/MindSpore/unified/aarch64/mindspore-2.6.0-cp39-cp39-linux_aarch64.whl –trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com <span class=”token parameter variable”>-i</span> https://pypi.tuna.tsinghua.edu.cn/simple
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- Verify Installation : After installation, run the following command to verify that MindSpore is installed successfully:

< span class = “token keyword” > import < /span > mindspore
< span class = “token keyword” > print < /span >< span class = “token punctuation” >(< /span > mindspore < span class = “token punctuation” > . < /span > __version__ < span class = “token punctuation” >)< /span >
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Quick Start Guide :
- Data processing : The framework provides an efficient data processing pipeline that supports various dataset loading and preprocessing operations.

< span class = “token keyword” > import < /span > mindspore < span class = “token punctuation” > . < /span > dataset < span class = “token keyword” > as < /span > ds
< span class = “token keyword” > from < /span > mindspore < span class = “token punctuation” > . < /span > dataset < span class = “token punctuation” > . < /span > transforms < span class = “token keyword” > import < /span > Compose < span class = “token punctuation” > , < /span > ToTensor < span class = “token punctuation” > , < /span > Normalize
< span class = “token comment” ># Load the MNIST dataset</span>
dataset < span class = “token operator” > = < /span > ds < span class = “token punctuation” > . < /span > MnistDataset < span class = “token punctuation” >(< /span >< span class = “token string” > “path/to/mnist_dataset” < /span >< span class = “token punctuation” >)< /span >
transforms < span class = “token operator” > = < /span > Compose < span class = “token punctuation” >(< /span >< span class = “token punctuation” >[< /span > ToTensor < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” > , < /span > ize Normal < span class = “token punctuation” >(< /span >< span class = “token punctuation” >(< /span >< span class = “token number” > 0.1307 < /span >< span class = “token punctuation” > , < /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” > , < /span > < span class = “token punctuation” >(< /span >< span class = “token number” > 0.3081 < /span >< span class = “token punctuation” > ,< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” >]< /span >< span class = “token punctuation” >)< /span >
dataset < span class = “token operator” > = < /span > dataset < span class = “token punctuation” > . < /span >< span class = “token builtin” > map < /span >< span class = “token punctuation” >(< /span > operations < span class = “token operator” > = < /span > transforms < span class = “token punctuation” > , < /span > input_columns < span class = “token operator” > = < /span >< span class = “token punctuation” >[< /span >< span class = “token string” > “image” < /span >< span class = “token punctuation” >]< /span >< span class = “token punctuation” >)< /span >
dataset < span class = “token operator” > = < /span > dataset < span class = “token punctuation” > . < /span > batch < span class = “token punctuation” >(< /span > batch_size < span class = “token operator” > = < /span >< span class = “token number” > 64 < /span >< span class = “token punctuation” >)< /span >
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- Model building : The framework provides a rich API that supports dynamic and static graph programming.

< span class = “token keyword” > import < /span > mindspore < span class = “token punctuation” > . < /span > nn < span class = “token keyword” > as < /span > nn
< span class = “token keyword” > import < /span > mindspore < span class = “token punctuation” > . < /span > ops < span class = “token keyword” > as < /span > ops
< span class = “token keyword” > class < /span > < span class = “token class-name” > LeNet5 < /span >< span class = “token punctuation” >(< /span > nn < span class = “token punctuation” > . < /span > Cell < span class = “token punctuation” >)< /span >< span class = “token punctuation” > : < /span >
    < span class = “token keyword” > def < /span > < span class = “token function” > __init__ < /span >< span class = “token punctuation” >(< /span > self < span class = “token punctuation” >)< /span >< span class = “token punctuation” > : < /span >
        < span class = “token builtin” > super < /span >< span class = “token punctuation” >(< /span > LeNet5 < span class = “token punctuation” > , < /span > self < span class = “token punctuation” >)< /span >< span class = “token punctuation” > . < /span > __init__ < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > conv1 < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > Conv2d < span class = “token punctuation” >(< /span >< span class = “token number” > 1 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 6 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 5 < /span >< span class = “token punctuation” > , < /span > pad_mode < span class = “token operator” > = < /span >< span class = “token string” > ‘valid’ < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > conv2 < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > Conv2d < span class = “token punctuation” >(< /span >< span class = “token number” > 6 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 16 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 5 < /span >< span class = “token punctuation” > , < /span > pad_mode < span class = “token operator” > = < /span >< span class = “token string” > ‘valid’ < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > fc1 < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > Dense < span class = “token punctuation” >(< /span >< span class = “token number” > 16 < /span > < span class = “token operator” > * < /span > < span class = “token number” > 5 < /span > < span class = “token operator” > * < /span > < span class = “token number” > 5 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 120 < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > fc2 < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > Dense < span class = “token punctuation” >(< /span >< span class = “token number” > 120 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 84 < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > fc3 < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > Dense < span class = “token punctuation” >(< /span >< span class = “token number” > 84 < /span >< span class = “token punctuation” > , < /span > < span class = “token number” > 10 < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > relu < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > ReLU < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > max_pool2d < span class = “token operator” > = < /span > nn < span class = “token punctuation” > . < /span > MaxPool2d < span class = “token punctuation” >(< /span > kernel_size < span class = “token operator” > = < /span >< span class = “token number” > 2 < /span >< span class = “token punctuation” >)< /span >
        self < span class = “token punctuation” > . < /span > flatten < span class = “token operator” > = < /span > ops < span class = “token punctuation” > . < /span > Flatten < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >
    < span class = “token keyword” > def < /span > < span class = “token function” > construct < /span >< span class = “token punctuation” >(< /span > self < span class = “token punctuation” > , < /span > x < span class = “token punctuation” >)< /span >< span class = “token punctuation” > : < /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > max_pool2d < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > relu < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > conv1 < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > max_pool2d < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > relu < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > conv2 < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > flatten < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > relu < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > fc1 < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > relu < span class = “token punctuation” >(< /span > self < span class = “token punctuation” > . < /span > fc2 < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >< span class = “token punctuation” >)< /span >
        x < span class = “token operator” > = < /span > self < span class = “token punctuation” > . < /span > fc3 < span class = “token punctuation” >(< /span > x < span class = “token punctuation” >)< /span >
        < span class = “token keyword” > return < /span > x
model < span class = “token operator” > = < /span > LeNet5 < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >
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- Training the model : The framework provides Modelclasses to simplify the training and evaluation process.

< span class = “token keyword” > from < /span > mindspore < span class = “token punctuation” > . < /span > train < span class = “token keyword” > import < /span > Model
< span class = “token keyword” > from < /span > mindspore < span class = “token punctuation” > . < /span > nn < span class = “token keyword” > import < /span > SoftmaxCrossEntropyWithLogits < span class = “token punctuation” > , < /span > Momentum
< span class = “token keyword” > from < /span > mindspore < span class = “token punctuation” > . < /span > train < span class = “token punctuation” > . < /span > callback < span class = “token keyword” > import < /span > ModelCheckpoint < span class = “token punctuation” > , < /span > CheckpointConfig
< span class = “token comment” ># Define the loss function and optimizer</span>
loss_fn < span class = “token operator” > = < /span > SoftmaxCrossEntropyWithLogits < span class = “token punctuation” >(< /span > sparse < span class = “token operator” > = < /span >< span class = “token boolean” > True < /span >< span class = “token punctuation” > , < /span > reduction < span class = “token operator” > = < /span >< span class = “token string” > ‘mean’ < /span >< span class = “token punctuation” >)< /span >
optimizer < span class = “token operator” > = < /span > Momentum < span class = “token punctuation” >(< /span > model < span class = “token punctuation” > . < /span > trainable_params < span class = “token punctuation” >(< /span >< span class = “token punctuation” >)< /span >< span class = “token punctuation” > , < /span > learning_rate < span class = “token operator” > = < /span >< span class = “token number” > 0.01 < /span >< span class = “token punctuation” > , < /span > momentum < span class = “token operator” > = < /span >< span class = “token number” > 0.9 < /span >< span class = “token punctuation” >)< /span >
< span class = “token comment” ># Create a Model instance</span>
model < span class = “token operator” > = < /span > Model < span class = “token punctuation” >(< /span > model < span class = “token punctuation” > , < /span > loss_fn < span class = “token operator” > = < /span > loss_fn < span class = “token punctuation” > , < /span > optimizer < span class = “token operator” > = < /span > optimizer < span class = “token punctuation” > , < /span > metrics < span class = “token operator” > = < /span >< span class = “token punctuation” >{< /span >< span class = “token string” > ‘accuracy’ < /span >< span class = “token punctuation” >}< /span >< span class = “token punctuation” >)< /span >
# Configure checkpoint saving </span>​
config_ck < span class = “token operator” > = < /span > CheckpointConfig < span class = “token punctuation” >(< /span > save_checkpoint_steps < span class = “token operator” > = < /span >< span class = “token number” > 1875 < /span >< span class = “token punctuation” > , < /span > keep_checkpoint_max < span class = “token operator” > = < /span >< span class = “token number” > 10 < /span >< span class = “token punctuation” >)< /span >
ckpoint_cb < span class = “token operator” > = < /span > ModelCheckpoint < span class = “token punctuation” >(< /span > prefix < span class = “token operator” > = < /span >< span class = “token string” > “lenet” < /span >< span class = “token punctuation” > , < /span > config < span class = “token operator” > = < /span > config_ck < span class = “token punctuation” >)< /span >
< span class = “token comment” ># Start Training</span>
model < span class = “token punctuation” > . < /span > train < span class = “token punctuation” >(< /span >< span class = “token number” > 10 < /span >< span class = “token punctuation” > , < /span > dataset < span class = “token punctuation” > , < /span > callbacks < span class = “token operator” > = < /span >< span class = “token punctuation” >[< /span > ckpoint_cb < span class = “token punctuation” >]< /span >< span class = “token punctuation” >)< /span >
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- Model evaluation : After training, evaluate the model performance using a test set.

< span class = “token comment” ># Load test dataset</span>
test_dataset < span class = “token operator” > = < /span > ds < span class = “token punctuation” > . < /span > MnistDataset < span class = “token punctuation” >(< /span >< span class = “token string” > “path/to/mnist_test_dataset” < /span >< span class = “token punctuation” >)< /span >
test_dataset < span class = “token operator” > = < /span > test_dataset < span class = “token punctuation” > . < /span >< span class = “token builtin” > map < /span >< span class = “token punctuation” >(< /span > operations < span class = “token operator” > = < /span > transforms < span class = “token punctuation” > , < /span > input_columns < span class = “token operator” > = < /span >< span class = “token punctuation” >[< /span >< span class = “token string” > “image” < /span >< span class = “token punctuation” >]< /span >< span class = “token punctuation” >)< /span >
test_dataset < span class = “token operator” > = < /span > test_dataset < span class = “token punctuation” > . < /span > batch < span class = “token punctuation” >(< /span > batch_size < span class = “token operator” > = < /span >< span class = “token number” > 64 < /span >< span class = “token punctuation” >)< /span >
# Evaluation Model​​​
acc < span class = “token operator” > = < /span > model < span class = “token punctuation” > . < /span >< span class = “token builtin” > eval < /span >< span class = “token punctuation” >(< /span > test_dataset < span class = “token punctuation” >)< /span >
< span class = “token keyword” > print < /span >< span class = “token punctuation” >(< /span >< span class = “token string-interpolation” >< span class = “token string” > f “Accuracy: </span><span class=” token interpolation “><span class=” token punctuation “>{</span>acc<span class=” token punctuation “>[</span><span class=” token string “>’accuracy'</span><span class=” token punctuation “>]</span><span class=” token punctuation “>}</span></span><span class=” token string “>” < / /span >< /span >< span class = ” token punctuation” >)< /span >
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Application scenarios of MindSpore

Intelligent Transportation and Autonomous Driving : Used in autonomous vehicles and intelligent transportation systems for real-time target detection, path planning, and traffic flow management to improve traffic efficiency and safety.
Industrial automation and intelligent manufacturing : Deployed on industrial production lines for real-time quality inspection and equipment failure prediction, optimizing production processes and improving production efficiency.
Healthcare and Medical Imaging : Used in medical image analysis and intelligent health monitoring devices to assist doctors in diagnosing diseases and improve the level of medical services.
Scientific Research and Engineering Simulation : Combined with the AI4S suite, accelerate scientific computing tasks such as electromagnetic simulation, fluid simulation, and molecular dynamics simulation, and support scientific research innovation.
Enterprise data analytics and decision support : Used for enterprise customer behavior analysis, market trend forecasting, and risk assessment to help enterprises make more accurate decisions.

What is MindSpore?

MindSpore’s main functions

How to use MindSpore

Application scenarios of MindSpore

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