Careful placement of a distributed computational application within a target device cluster is critical for achieving low application completion time. The problem is challenging due to its NP-hardness and combinatorial nature. In recent years, learning-based approaches have been proposed to learn a placement policy that can be applied to unseen applications, motivated by the problem of placing a neural network across cloud servers. These approaches, however, generally assume the device cluster is fixed, which is not the case in mobile or edge computing settings, where heterogeneous devices move in and out of range for a particular application. To address the challenge of scaling to different-sized device clusters and adapting to the addition of new devices, we propose a new learning approach called GiPH, which learns policies that generalize to dynamic device clusters via a novel graph representation gpNet that efficiently encodes the information needed for choosing a good placement, and a scalable graph neural network (GNN) that learns a summary of the gpNet information. GiPH turns the placement problem into that of finding a sequence of placement improvements, learning a policy for selecting this sequence that scales to problems of arbitrary size. We evaluate GiPH with a wide range of task graphs and device clusters and show that our learned policy rapidly finds good placements for new problem instances. GiPH finds placements that achieve up to 30.5% better makespan, searching up to 3 times faster than other search-based placement policies.