Supervised learning models are increasingly used in algorithmic decision-making. The traditional assumption on the training and testing data being independently and identically distributed is often violated in practical learning settings, due to distribution shifts. To mitigate the effects of such nonstationarities, risk-sensitive learning is proposed to train models under different (risk) functionals beyond the expected loss. For example, learning under the conditional value-at-risk of the losses is equivalent to training a model under a particular type of worst-case distribution shift. While many risk functionals and learning procedures have been proposed, their implementations are either nonexistent or in individualized repositories. With no common implementations and baseline test beds, it is difficult to decide which risk functionals and learning procedures to use. To address this, we introduce a library (RiskyZoo) for risk-sensitive supervised learning. The library contains implementations of risk-sensitive learning objectives and optimization procedures that can be used as add-ons to the PyTorch library. We also provide datasets to compare these learning methods. We demonstrate usage of our library through comparing models learned under different risk objectives, optimization performances of different methods for a single objective, and risk assessments of pretrained ImageNet models.