Amazon Technologies, Inc.

Xingjian Shi

Qiang Zhou

Lee Pike

Zhi Chen

Yaojie Hu

Haichen Shen

Tuning result records indicating tuned schedules for machine learning tuning tasks are stored at a data store accessed from a tuning service. A given schedule indicates at least an order in which sub-operations of a tuning task are to be executed. In response to determining that the data store does not include a result record whose tuning task meets a similarity criterion to a tuning task determined from a tuning request, performance predictions for a set of candidate schedules are obtained using a performance prediction model, without running code which executes the candidate schedules. In a response to the tuning request, executable code corresponding to a preferred schedule identified from the candidate schedules using the performance predictions is included.

Tuned executable code service for machine learning models

Techniques for determining buggy code are described. An encoder/decoder-based (e.g., transformer-based) model approach is described. In some embodiments, a service receives request to perform transformer-based bug fixing on code, performs bug fixing inference to the code by applying a trained encoder/decoder-based model, and reports out a result of the inference, wherein the output includes an indication of a location of a potential edit to be made in the code and the potential edit in the code.

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Sunnyvale, CA, United States of America

Xingjian Shi