下载量超 1300 万，昇思 MindSpore：AI 框架迈入“超节点时代”

Core Insights - The MindSpore community has achieved significant growth, with over 13 million cumulative downloads, more than 52,000 core contributors, and over 120,000 code contributions, serving users in over 150 countries and regions [2] - MindSpore has developed three core capabilities in AI frameworks, focusing on collaboration with training acceleration libraries, model communities, and evaluation tools [3] - The rise of large language models has shifted computational paradigms from single-machine to cluster-based approaches, leading to the development of various parallelization techniques [4] Group 1 - MindSpore supports over 25 model types, providing a comprehensive out-of-the-box capability for script development, parallel training, fine-tuning, and deployment [3] - The framework has achieved over 15% performance improvement in large model inference scenarios through seamless integration with the vLLM community [3] - MindSpore's HyperParallel architecture treats supernodes as a single supercomputer, enhancing programming and scheduling capabilities [6] Group 2 - The HyperParallel architecture introduces key technologies such as Hyperoffload, which separates computation and state to alleviate storage bottlenecks, improving training performance by approximately 20% and increasing sequence length support by about 70% in inference scenarios [4] - MindSpore's native support for ultra-large-scale cluster parallelism can cover tens of thousands of computing nodes and support trillion-parameter models [5] - The framework has been deployed across a wide range of devices, from data center servers to small terminals, establishing itself as a foundational AI capability for numerous smart devices [5] Group 3 - The official version of the HyperParallel architecture and associated acceleration suites for multimodal and reinforcement learning will be released in the first half of next year [7] - Future developments in the MindSpore community will focus on edge intelligence, open architecture, and industry enablement, covering large models and agent acceleration [7] - The introduction of HyperMPMD and Hypershard aims to enhance resource utilization and reduce parallelization modification time significantly [11]