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Introduction || Quick Start || Tensor || Dataset || Transforms || Model || Autograd || Train || Save and Load || Accelerating with Static Graphs


The following describes the Huawei AI full-stack solution and the position of MindSpore in the solution. Developers who are interested in MindSpore can visit the MindSpore community and click Watch, Star, and Fork.

Introduction to MindSpore

MindSpore is a deep learning framework in all scenarios, aiming to achieve easy development, efficient execution, and unified deployment for all scenarios.

Easy development features user-friendly APIs and low debugging difficulty. Efficient execution is reflected in computing, data preprocessing, and distributed training. Unified deployment for all scenarios means that the framework supports cloud, edge, and device scenarios.

The following figure shows the overall MindSpore architecture:


  • ModelZoo: provides available deep learning algorithm networks, and more developers are welcome to contribute new networks. (ModelZoo)

  • MindSpore Extend: The MindSpore expansion package supports new domain scenarios, such as GNN, deep probabilistic programming, and reinforcement learning. More developers are expected to contribute and build the library.

  • MindSpore Science: MindScience is a scientific computing kit for various industries based on the converged MindSpore framework. It contains the industry-leading datasets, basic network structures, high-precision pre-trained models, and pre-and post-processing tools that accelerate application development of the scientific computing.

  • MindExpression: Python-based front-end expression and programming interface that supports two fusions (function/OOP programming paradigm fusion and AI+ numerical computation expression fusion) and two unifications (dynamic expression unification and single-computer distributed expression unification).

  • Third-party front-end: support for third-party multilingual front-end expression, the future plans to successively provide C/C++ and other third-party front-end docking work, and the introduction of more third-party ecology.

  • MindSpore Data: provides functions such as efficient data processing, common dataset loading and programming interfaces, and allows users to flexibly define processing registration and pipeline parallel optimization.

  • MindCompiler: The core compiler of the layer, which implements three major functions based on the unified device-cloud MindIR, including hardware-independent optimization (type derivation, automatic differentiation, and expression simplification), hardware-related optimization (automatic parallelism, memory optimization, graph kernel fusion, and pipeline execution), and optimization related to deployment and inference (quantification and pruning).

  • MindRT: MindSpore runtime system, including the runtime system on the cloud host, runtime system on the device, and lightweight runtime system of the IoT platform.

  • MindSpore Insight: MindSpore visualized debugging and tuning tool, allowing users to debug and tune the training network (More Information).

  • MindSpore Armour: For enterprise-level applications, provides enhanced functions related to security and privacy protection, such as anti-robustness, model security testing, differential privacy training, privacy leakage risk assessment, and data drift detection (More Information).

Execution Process

With an understanding of the overall architecture of MindSpore, we can look at the overall coordination relationship between the various modules, as shown in the figure:


As an all-scenario AI framework, MindSpore supports different series of hardware in the device (mobile phone and IoT device), edge (base station and routing device), and cloud (server) scenarios, including Ascend series products and NVIDIA series products, Qualcomm Snapdragon in the ARM series, and Huawei Kirin chips.

The blue box on the left is the main MindSpore framework, which mainly provides the basic API functions related to the training and verification of neural networks, and also provides automatic differentiation and automatic parallelism by default.

Below the blue box is the MindSpore Data module, which can be used for data preprocessing, including data sampling, data iteration, data format conversion, and other data operations. Many debugging and tuning problems may occur during training. Therefore, the MindSpore Insight module visualizes debugging and tuning data such as the loss curve, operator execution status, and weight parameter variables, facilitating debugging and optimization during training.

The simplest scenario to ensure AI security is from the perspective of attack and defense. For example, attackers inject malicious data in the training phase to affect the inference capability of AI models. Therefore, MindSpore launches the MindSpore Armour module to provide an AI security mechanism for MindSpore.

The content above the blue box is closer to algorithm development users, including the AI algorithm model library ModelZoo, development toolkit MindSpore DevKit for different fields, and advanced extension library MindSpore Extend. MindSciences, a scientific computing kit in MindSpore Extend, is worth mentioning. MindSpore is the first to combine scientific computing with deep learning, combine numerical computing with deep learning, and support electromagnetic simulation and drug molecular simulation through deep learning.

After the neural network model is trained, you can export the model or load the model that has been trained in MindSpore Hub. Then MindIR provides a unified IR format for the device and cloud, which defines logical network structures and operator attributes through a unified IR, and decouples model files in MindIR format from hardware platforms to implement one-time training and multiple-time deployment. As shown in the figure, the model is exported to different modules through IR to perform inference.

Design Philosophy

  • Supporting unified deployment for all scenarios

    MindSpore is derived from industry-wide best practices. It provides unified model training, inference, and export APIs for data scientists and algorithm engineers. It supports flexible deployment in different scenarios such as the device, edge, and cloud, and promotes the prosperity of domains such as deep learning and scientific computing.

  • Provideing the Python programming paradigm to simplify AI programming

    MindSpore provides a Python programming paradigm. Users can build complex neural network models using Python’s native control logic, making AI programming easy.

  • Providing a unified coding method for dynamic and static graphs

    Currently, there are two execution modes of a mainstream deep learning framework: a static graph mode (GRAPH_MODE) and a dynamic graph mode (PYNATIVE_MODE). The GRAPH mode has high training performance but is difficult to debug. On the contrary, the PYNATIVE mode is easy to debug, but is difficult to execute efficiently. MindSpore provides an encoding mode that unifies dynamic and static graphs, which greatly improves the compatibility between static and dynamic graphs. Instead of developing multiple sets of code, users can switch between the dynamic and static graph modes by changing only one line of code, which facilitates development and debugging, and improves performance experience.

    For example, set set_context(mode=PYNATIVE_MODE) to switch to the dynamic graph mode, or set set_context(mode=GRAPH_MODE) to switch to the static graph mode.

  • Using AI and scientific computing fusion programming and allowing users to focus on the mathematical native expression of model algorithms

    On the basis of support for AI model training and inference programming, it extends the support for flexible automatic differential programming capability, supports differential derivation in the case of function and control flow expression, and supports various kinds of advanced differential capabilities, such as forward differentiation and higher-order differentiation, based on which users can realize the programming expression of differential functions commonly used in scientific computation, so as to support the fusion programming and development of AI and scientific computation.

  • Distributed training native

    As a scale of neural network models and datasets continuously increases, parallel distributed training becomes a common practice of neural network training. However, the strategy selection and compilation of parallel distributed training are very complex, which severely restricts training efficiency of a deep learning model and hinders development of deep learning. MindSpore unifies the coding methods of single device and distributed training. Developers do not need to write complex distributed strategies. They can implement distributed training by adding a small amount of code to the single device code, which improves the efficiency of neural network training, greatly reduces the threshold of AI development, and enables users to quickly implement model ideas.

    For example, they can set set_auto_parallel_context(parallel_mode=ParallelMode.AUTO_PARALLEL) to automatically establish a cost model, and select an optimal parallel mode for users.

API Level Structure

MindSpore provides users with three different levels of APIs to support AI application (algorithm/model) development, from high to low: High-Level Python API, Medium-Level Python API and Low-Level Python API. The High-Level API provides better encapsulation, the Low-Level API provides better flexibility, and the Mid-Level API combines flexibility and encapsulation to meet the needs of developers in different fields and levels.

MindSpore API

  • High-Level Python API

    High-level APIs are at the first layer. Based on the medium-level API, these advanced APIs include training and inference management, mixed precision training, and debugging and optimization, enabling users to control the execution process of the entire network and implement training, inference, and optimization of the neural network. For example, by utilizing the Model API, users can specify the neural network model to be trained as well as related training settings, train the neural network model.

  • Medium-Level Python API

    Medium-level APIs are at the second layer, which encapsulates low-cost APIs and provides such modules as the network layer, optimizer, and loss function. Users can flexibly build neural networks and control execution processes through the medium-level API to quickly implement model algorithm logic. For example, users can call the Cell API to build neural network models and computing logic, add the loss function and optimization methods to the neural network model by using the loss module and Optimizer API, and use the dataset module to process data for model training and derivation.

  • Low-Level Python API

    Low-level APIs are at the third layer, including tensor definition, basic operators, and automatic differential modules, enabling users to easily define tensors and perform derivative computation. For example, users can customize tensors by using the Tensor API, and use the grad API to calculate the derivative of the function at a specified position.

Introduction to Huawei Ascend AI Full-Stack Solution

Ascend computing is a full-stack AI computing infrastructure and application based on the Ascend series processors. It includes the Ascend series chips, Atlas series hardware, CANN chip enablement, MindSpore AI framework, ModelArts, and MindX application enablement.

Huawei Atlas AI computing solution is based on Ascend series AI processors and uses various product forms such as modules, cards, edge stations, servers, and clusters to build an all-scenario AI infrastructure solution oriented to device, edge, and cloud. It covers data center and intelligent edge solutions, as well as the entire inference and training processes in the deep learning field.

Th Ascend AI full stack is shown below:

Ascend full stack

The functions of each module are described as follows:

  • Ascend Application Enablement: AI platform or service capabilities provided by Huawei major product lines based on MindSpore.

  • MindSpore: Support for device-edge-cloud-independent and collaborative unified training and inference frameworks.

  • CANN: A driver layer that enables Ascend chips (learn more).

  • Compute Resources: Ascend serialized IP, chips and servers.

For details, click Huawei Ascend official website.

Joining the Community

Welcome every developer to the MindSpore community and contribute to this all-scenario AI framework.

  • MindSpore official website: provides comprehensive MindSpore information, including installation, tutorials, documents, community, resources, and news (learn more).

  • MindSpore code:

    • MindSpore Gitee: Top 1 Gitee open-source project in 2020, where you can track the latest progress of MindSpore by clicking Watch, Star, and Fork, discuss issues, and commit code.

    • MindSpore Github: MindSpore code image of Gitee. Developers who are accustomed to using GitHub can learn MindSpore and view the latest code implementation here.

  • MindSpore forum: We are dedicated to serving every developer. You can find your voice in MindSpore, regardless of whether you are an entry-level developer or a master. Let’s learn and grow together. (Learn more)