Distributed training for pytorch
Implementing Google's DistBelief paper.
You'll want to create a python3 virtualenv first by running
make setup, after which, you should run
You'll then be able to use distbelief by importing distbelief
from distbelief.optim import DownpourSGD optimizer = DownpourSGD(net.parameters(), lr=0.1, n_push=5, n_pull=5, model=net)
As an example, you can see our implementation running by using the script provided in
To run a 2-training node setup locally, open up three terminal windows, source the
venv and then run
make second, and
This will begin training AlexNet on CIFAR10 locally with all default params.
NOTE: we graph the train/test accuracy of each node, hence node1, node2, node3. A better comparison would be to evaluate the parameter server's params and use that value. However we can see that the accuracy between the three nodes is fairly consistent, and adding an evaluator might put too much stress on our server.
We scale the learning rate of the nodes to be learning_rate/freq (.03) .
We used AWS c4.xlarge instances to compare the CPU runs, and a GTX 1060 for the GPU run.
DownpourSGD for PyTorch
Here 2 and 3 happen concurrently.
You can read more about our implementation here.
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