Cost-optimal planning has not seen many successful approaches that work well across all domains. Some cost-optimal planners excel on some domains, while exhibiting less exciting performance on others. For a particular domain, however, there is often a cost-optimal planner that works extremely well. For that reason, portfolio-based techniques have recently become popular. These either decide offline on a particular resource allocation scheme for a given collection of planners or try to perform an online classification of a given planning task to select a planner to be applied to solving the task at hand. Our planner Delfi is an online portfolio planner. In contrast to existing techniques, Delfi exploits deep learning techniques to learn a model that predicts which of the planners in the portfolio can solve a given planning task within the imposed time and memory bounds. Delfi uses graphical representations of a planning task which allows exploiting existing tools for image convolution. In this planner abstract, we describe the techniques used to create our portfolio planner.