Machine learning libraries such as TensorFlow and PyTorch simplify model implementation. However, researchers are still required to perform a non-trivial amount of manual tasks such as GPU allocation, training status tracking, and comparison of models with different hyperparameter settings. We propose a system to handle these tasks and help researchers focus on models. We present the requirements of the system based on a collection of discussions from an online study group comprising 25k members. These include automatic GPU allocation, learning status visualization, handling model parameter snapshots as well as hyperparameter modification during learning, and comparison of performance metrics between models via a leaderboard. We describe the system architecture that fulfills these requirements and present a proof-of-concept implementation, NAVER Smart Machine Learning (NSML). We test the system and confirm substantial efficiency improvements for model development.

我们提出了一个名为NAVER Smart Machine Learning (NSML)的系统，用于处理机器学习任务中的GPU分配、可视化学习状态、处理模型参数快照、超参数修改以及性能指标比较等任务，通过实验确认其能够提升模型开发效率。

NSML：一种机器学习平台，使您专注于您的模型