ray-serve (conda): Ray is a fast and simple framework for building and running distributed applications. It is split into ray-core, ray-default, ray-dashboard, ray-serve, ray-rllib, ray-k8s, ray-data, ray-tune, ray-train, ray-air and ray-all packages.
Keywords: automl|data-science|deep-learning|deployment|distributed|hyperparameter-optimization|hyperparameter-search|java|machine-learning|model-selection|optimization|parallel|python|pytorch|ray|reinforcement-learning|rllib|serving|tensorflow