For job allocation decision, current batch schedulers have access to and use only information on the number of nodes and runtime because it is readily available at submission time from user job scripts. User-provided runtimes are typically inaccurate because users overestimate or lack understanding of job resource requirements. Beyond the number of nodes and runtime, other system resources, including IO and network, are not available but play a key role in system performance. In this talk we tackle the need for automatic, general, and scalable tools that provide accurate resource usage information to schedulers with our tool for Predicting Runtime and IO using Neural Networks and GPUs (PRIONN). PRIONN automates prediction of per-job runtime and IO resource usage, enabling IO-aware scheduling on HPC systems. The novelty of our tool is the input of whole job scripts into deep learning models that allows complete automation of runtime and IO resource predictions.