Parameter servers (PSs) ease the implementation of distributed machine learning systems, but their performance can fall behind that of single machine baselines due to communication overhead. We demonstrate Lapse, an open source PS with dynamic parameter allocation . Previous work has shown that dynamic parameter allocation can improve PS performance by up to two orders of magnitude and lead to near-linear speed-ups over single machine baselines. This demonstration illustrates how Lapse is used and why it can provide order-of-magnitude speed-ups over other PSs. To do so, this demonstration interactively analyzes and visualizes how dynamic parameter allocation looks like in action.