  Emerging Deep Learning (DL) applications introduce heavy I/O workloads on
computer clusters. The inherent long lasting, repeated, and random file access
pattern can easily saturate the metadata and data service and negatively impact
other users. In this paper, we present FanStore, a transient runtime file
system that optimizes DL I/O on existing hardware/software stacks. FanStore
distributes datasets to the local storage of compute nodes, and maintains a
global namespace. With the techniques of system call interception, distributed
metadata management, and generic data compression, FanStore provides a
POSIX-compliant interface with native hardware throughput in an efficient and
scalable manner. Users do not have to make intrusive code changes to use
FanStore and take advantage of the optimized I/O. Our experiments with
benchmarks and real applications show that FanStore can scale DL training to
512 compute nodes with over 90\% scaling efficiency.
