Abstract
Deep learning researchers are increasingly using Jupyter notebooks to implement interactive, reproducible workflows with embedded visualization, steering and documentation. Such solutions are typically deployed on small-scale (e.g. single server) computing systems. However, as the sizes and complexities of datasets and associated neural network models increase, high-performance distributed systems become important for training and evaluating models in a feasible amount of time. In this paper we describe our vision for Jupyter notebook solutions to deploy deep learning workloads onto high-performance computing systems. We demonstrate the effectiveness of notebooks for distributed training and hyper-parameter optimization of deep neural networks with efficient, scalable backends.
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Acknowledgements
This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. This work was in part supported by the NERSC Big Data Center; we acknowledge Cray for their funding support.
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Farrell, S. et al. (2018). Interactive Distributed Deep Learning with Jupyter Notebooks. In: Yokota, R., Weiland, M., Shalf, J., Alam, S. (eds) High Performance Computing. ISC High Performance 2018. Lecture Notes in Computer Science(), vol 11203. Springer, Cham. https://doi.org/10.1007/978-3-030-02465-9_49
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DOI: https://doi.org/10.1007/978-3-030-02465-9_49
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