  This paper focuses on mitigating the impact of stragglers in distributed
learning system. Unlike the existing results designed for a fixed number of
stragglers, we developed a new scheme called Adaptive Gradient Coding(AGC) with
flexible tolerance of various number of stragglers. Our scheme gives an optimal
tradeoff between computation load, straggler tolerance and communication cost.
In particular, it allows to minimize the communication cost according to the
real-time number of stragglers in the practical environments. Implementations
on Amazon EC2 clusters using Python with mpi4py package verify the flexibility
in several situations.
