Boosting LLM Development and Training Efficiency: Automated Parallelization with MindSpore

Explore automated parallelization techniques for boosting large language model (LLM) development and training efficiency using MindSpore in this 44-minute conference talk by Yiren Xing from Huawei. Learn about an innovative approach that allows developers to focus on algorithm research without the need for intrusive model modifications. Discover how distributed training on large-scale clusters can be achieved through simple strategy configurations. Gain insights into MindSpore's hyperparameter search model for automatically finding optimal parallelization strategies, which can achieve 90%-110% of expert tuning performance. Understand how this method significantly reduces model modification time while efficiently accelerating LLM training. The presentation covers both the challenges of large-scale distributed parallel training and the solutions offered by automated parallelization, making it valuable for AI researchers and developers working with large language models.