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Distributed-Mnist

Distributed version of mnist tensorflow training

Instructions

  1. Simple Usage:
  • for Parameter Server 0, python distributed_mnist.py --job_name='ps' --task_index=0
  • for Parameter Server 1, python distributed_mnist.py --job_name='ps' --task_index=1
  • for Client 0 , python distributed-mnist.py --job_name='worker' --task_index=0
  • for Client 1 , python distributed-mnist.py --job_name='worker' --task_index=1

Remember to edit flag ps_hosts/worker_hosts inside the python sript before running th program
Inside the script, flag num_workers/num_parameter_servers should be consistent with length of flag ps_hosts/worker_hosts

  1. Optional Arguments:
  • --ps_hosts: list of [hostname:port] for ps jobs
  • --worker_hosts: list of [hostname:port] for ps jobs
  • --num_workers: length of workers_hosts
  • --num_parameter_servers: length of ps_hosts
  • --train_steps: number of training steps to perform
  • --batch_size: training batch size

Also see python distributed_mnist.py -h for more info

Dependency