The more we know about the resource usage patterns of workloads, the better we can allocate resources. Here we present a methodology to discover resource usage behaviors of containers training Deep Learning (DL) models. From monitoring, we can observe repeating patterns and similitude of resource usage among containers training different DL models. The repeating patterns observed can be leveraged by the scheduler or the resource autoscaler to reduce resource fragmentation and overall resource utilization in a dedicated DL cluster. Specifically, our approach combines Conditional Restricted Boltzmann Machines (CRBMs) and clustering techniques to discover common sequences of behaviors (phases) of containers running the DL training workloads in clusters providing IBM Deep Learning Services. By studying the resource usage pattern at each phase and the typical sequences of phases among different containers, we discover a reduced set of prototypical executions representing the majority of executions. We use statistical information from each phase to refine resource provisioning by dynamically tuning the amount of resource each container requires at each phase. Evaluation of our method shows that by leveraging typical resource usage patterns, we can auto-scale containers to reduce CPU and Memory allocation by 30\% compared to statistics based reactive policies, which is close to having a-priori knowledge of resource usage while fulfilling resource demand over 95\% of the time.