MindSpore

Overview

MindSpore is Huawei's new open-source, full-scenario deep learning framework, purpose-built for flexible deployment across device, edge, and cloud environments. It delivers powerful distributed training capabilities, with a range of built-in parallel strategies that simplify the development of large-scale models and help teams move from prototyping to production faster.

MindSpore is deeply optimized for Ascend processors, enabling developers to fully harness hardware performance, significantly cut training time, and improve inference efficiency. The framework also natively supports converging AI with high-performance computing (HPC), delivering heterogeneous parallel acceleration tailored to AI for Science scenarios.

Backed by a rich ecosystem of open-source projects, curated case studies, and state-of-the-art models, MindSpore offers an extensive range of resources that help developers get started quickly, explore best practices, and apply the framework across a wide variety of real-world AI workloads. Whether you are an individual developer or an enterprise team, MindSpore provides the tools and support needed to accelerate AI innovation.

Core capabilities

All-scenario AI framework

Supports rapid deployment across cloud, edge, and device, and works on multiple hardware platforms including Ascend, GPU, and CPU.

Distributed training

Built-in parallel capabilities with an easy-to-use configuration interface for large model distributed strategies, enabling efficient large-scale training.

AI4S converged computing framework

Supports end-to-end programmable AI + HPC, meeting heterogeneous parallel acceleration needs for AI for Science scenarios.

Hardware potential unleashed

Unified static and dynamic programming that perfectly matches Ascend processors, helping developers shorten training time and boost inference performance.

Installation and usage

Provides detailed setup guides and commands, supporting multiple hardware platforms.

Cloud platform support

Offers cloud platform support to help users quickly create and deploy models, and manage the full AI workflow lifecycle.

Ecosystem resources

Provides open-source projects, case collections, SOTA models, and derived tools, supporting domain-specific suites such as NLP, CV, and Audio.

Use cases

Train ResNet50 for image classification on the ImageNet dataset

Implement real-time object detection using YOLOv3

Complete text classification tasks with the BERT model

Solve physics simulation problems with differential equations in scientific computing

Quantize models with MindSpore Lite for mobile deployment

Train collaboratively on device data to protect privacy via federated learning

Predict click-through rates with DeepFM in recommender systems