Rep. Pat Harrigan is leading a bill to phase out Chinese-manufactured drones from U.S. law enforcement, citing national security concerns tied to the Chinese Communist Party.

Osprey is a tool for hyperparameter optimization of machine learning algorithms in Python. Hyperparameter optimization can often be an onerous process for researchers, due to time-consuming experimental replicates, non-convex objective functions, and constant tension between exploration of global parameter space and local optimization (Jones, Schonlau, and Welch 1998). We've designed Osprey to provide scientists with a practical, easy-to-use way of finding optimal model parameters. The software works seamlessly with scikit-learn estimators (Pedregosa et al. 2011) and supports many different search strategies for choosing the next set of parameters with which to evaluate a given model, including gaussian processes (GPy 2012), tree-structured Parzen estimators (Yamins, Tax, and Bergstra 2013), as well as random and grid search. As hyperparameter optimization is an embarrassingly parallel problem, Osprey can easily scale to hundreds of concurrent processes by executing a simple command-line program multiple times.