Day0迁移、一键部署，华为开源的昇思MindSpore成为大模型开发的“万能钥匙”

Core Viewpoint - The consensus in the AI large model era is that no single large model can dominate the market, leading to challenges for developers in navigating various mainstream models and AI technologies [1][2]. Group 1: MindSpore Overview - Huawei's open-source MindSpore offers a solution for developers to experience and migrate mainstream state-of-the-art (SOTA) large models with minimal code changes, ensuring precision and performance remain intact [3][4]. - The training to inference process is fully automated, allowing over 20 mainstream large models to be deployed out of the box, with loading times for models with billions of parameters under 30 seconds [5][19]. Group 2: Migration and Deployment Features - MindSpore's "translation tool" MSAdapter enables seamless migration of code from other frameworks to MindSpore, achieving nearly zero loss during the transition [8][10]. - The tool can automatically convert over 95% of interfaces, maintaining a user-friendly experience similar to the original framework [10]. Group 3: Technical Enhancements - MindSpore employs several unique techniques to accelerate training and debugging, including multi-stage processing of operators, JIT compilation for efficient code execution, and automatic strategy optimization, which improved performance by 9.5% in specific training scenarios [11][13][16]. - The code modification required for distributed task initiation is minimal, with Python script changes being less than 1% and automated through patch tools [14]. Group 4: Inference Deployment - The vLLM-MindSpore plugin allows for rapid deployment of HuggingFace models, achieving service readiness in under 30 minutes [18][23]. - For large models, MindSpore has restructured the inference process, achieving a throughput of 1020 tokens per second with a latency of less than 100ms for specific models [19]. Group 5: Performance Improvements - The loading time for model weights has been reduced by 80%, with billion-parameter models loading in under 30 seconds, and graph compilation delays minimized to the millisecond range [23].