README.md

do remeber to transfer the numpy.ndarray to numpy.float64 before push them to MPI send/receive buffer, otherwise there will be some data type transfer issues between numpy and MPI.

To use it on single machine:

python single_machine.py --dataset=MNIST/Cifar10 --network=LeNet/Resnet --batch-size=${BATCH_SIZE}

To use it on distributed cluster:

mpirun -n ${NUM_WORKERS} --hostfile=${HOST_DIR} python distributed_nn.py --dataset=MNIST/Cifar10 --network=LeNet/Resnet --batch-size=${BATCH_SIZE}

Run the whole thing automatically

The first thing you need do is to launch AWS EC2 instances, you can do that using tools/pytorch_ec2.py by running the following command:

python pytorch_ec2.py launch

After the launch command are executed and all instances are initialized (this may cost several minutes), you need to fetch the host addresses:

python pytorch_ec2.py get_hosts

Then, copying essential configuration files and hosts files using the public address of master node (the first address in the host file):

sh local_script.sh ${MASTER_PUB_ADDR}

After that, launch to master node manually, running the remote script under $HOME dir:

sh remote_script.sh

This script will do the cluster setup and data preparation works for you.