community/working-groups/research/Topic10_AutoML.md

• Topic10：AutoML
• • Motivation：
  • Target：
  • Method：
  • How To Join：

Topic10：AutoML

Motivation：

​ Nowadays, training a model that meets the accuracy requirements often requires rich expert knowledge and repeated iterative attempts. Although there is AutoML technology, there are still problems of difficult search space setting and long training time for large search spaces. If you can combine the iterative history of user training and analyze historical data, a lightweight hyperparameter recommendation method can be realized, which can greatly improve the developer experience.

​ Similarly, for performance tuning, there are similar problems. In different heterogeneous hardware, models, and data processing scenarios, expert knowledge is required for tuning. Therefore, we aim to reduce the performance tuning threshold by automatically identifying system performance bottlenecks and recommending the best code path.

Target：

​ Reduce model development cost, set up thresholds through automatic hyper-parameter configuration and performance optimization paths, and improve model debugging and optimization efficiency.

Method：

​ We expect the applicant can conduct AutoML research based on MindSpore, and hope to get your valuable suggestions to MindSpore in the process. We will do our best to improve the capabilities of the MindSpore framework and provide you with the most powerful technical support.

How To Join：

1. Submit an issue/PR based on community discussion for consultation or claim on related topics
2. Submit your proposal to us by email xxx@huawei.com