Dynamically Typed

Model Search for TensorFlow

Google has released Model Search, an open-source AutoML platform for the TensorFlow ecosystem. The pitch: “Model Search is domain agnostic, flexible and is capable of finding the appropriate architecture that best fits a given dataset and problem, while minimizing coding time, effort and compute resources.” It can run on a single machine or in a distributed setting, and uses a reinforcement learning-inspired “explore & exploit” methodology to find a model architecture that optimizes for user-specified metrics. For efficiency, Model Search also uses knowledge distillation and weight sharing between experiments runs. It’s available on GitHub at google/model_search.