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Accommodating long-running deep learning (DL) training and inference jobs is challenging on GPU clusters that use traditional batch schedulers, such as Slurm. Given fixed wall clock time limits, DL researchers usually need to run a sequence of batch jobs and experience long interruptions on overloaded machines. Such interruptions significantly lower the research productivity and QoS for services that are deployed in production. To mitigate the issues from interruption, we propose the design of a proactive provisioner and investigate a set of statistical learning and reinforcement learning (RL) techniques, including random forest, xgboost, Deep Q-Network, and policy gradient. Using production job traces from three GPU clusters, we train each model using a subset of the trace and then evaluate their generality using the remaining validation subset. We introduce Mirage, a Slurm-compatible resource provisioner that integrates the candidate ML methods. Our experiments show that the Mirage can reduce interruption by 17--100% and safeguard 23%-76% of jobs with zero interruption across varying load levels on the three clusters.