Distributed-TensorFlow-Using-MPI

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Distributed-TensorFlow-Using-MPI

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Template for Deploying Distributed TensorFlow on Clusters Using MPI

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# Distributed-TensorFlow-Using-MPI
Template for Deploying Distributed TensorFlow on Clusters Using MPI

## Brief Description
Scripts in this repository can be used on dynamically allocated clusters. It combines mpi4py with the typical distributed TensorFlow cluster settings (see the template at https://www.tensorflow.org/deploy/distributed). The basic idea is to use MPI to traverse among the nodes and set up the TensorFlow cluster.

## Contents
1. distributed_tensorflow.sh: the PBS script you can use while submitting jobs
2. cluster_specs.py: get the nodes you acquired for this job and convert this information into the corresponding ps_hosts and worker_hosts in TensorFlow
3. cluster_dispatch.py: specify job_name and task_index and deploy the job
4. example.py: a simple example script for testing. Note that the functions in this example may be depracated soon and you should design your model based on the template at https://www.tensorflow.org/deploy/distributed. Also note that this example script may have a different license from the one for this repository.

## Arguments
Most of the arguments should be modified in distributed_tensorflow.sh. However, you can always change the stdout outputs (the contents in print()) in cluster_dispath.py.

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