Despite the proliferation of open source tools like Databricks’ AutoML Toolkit, Salesforce’s TransfogrifAI, and IBM’s Watson Studio AutoAI, tuning machine learning algorithms at scale remains a challenge. Finding the right hyperparameters — variables in the algorithms that help control the overall model’s performance — often involves time-consuming ancillary tasks like job scheduling and keeping track of parameters and their effects. That’s why scientists at LG’s Advanced AI division developed Auptimizer, an open source hyperparameter optimization framework intended to aid with AI model tweaking and bookkeeping. It’s available from GitHub.

As the team explains in a paper describing their work, Auptimizer simplifies the process of configuring a volume of models with a variety of configurations, with reproducibility. Like all hyperparameter algorithms, it…

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