# Topic10：Automatic model optimization recommendation

## Motivation：
* Nowadays, training a high accuracy and high performance model often requires rich expert knowledge and repeated iterative attempts. AutoML makes it easier to apply and reduce the demand for experienced human experts, however, there are still some difficulties in setting search space which lead to large search spaces and long training time. If we can combine the iterative history of user training and analyze historical training data, a lite hyper-parameter recommendation method can be realized, which can greatly improve the developer experience.
* Meanwhile, there are similar problems for model performance tuning, In different heterogeneous hardware, models, and data processing scenarios, expert knowledge is also required. Therefore, we aim to reduce the performance tuning threshold by automatically identifying system performance bottlenecks and recommending the best code path.
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## Target：
This feature automatically recommends optimized hyper-parameter configurations and performance optimization paths, reducing the threshold for model development and use and improving the model debugging and optimization efficiency.
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## Method：
​We expect the applicant can conduct Automatic model optimization recommendation 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：
* Submit an issue/PR based on community discussion for consultation or claim on related topics
* Submit your proposal to us by email roc.wangyunpeng@huawei.com