dasayan05

# filename 'ptdist.py'
import torch
import torch.distributed as dist
def main(rank, world):
    if rank == 0:
        x = torch.tensor([1., -1.]) # Tensor of interest
        dist.send(x, dst=1)
        print('Rank-0 has sent the following tensor to Rank-1')
        print(x)
    else:

dasayan05

def main(rank, world):
    if rank == 0:
        x = torch.tensor([1.])
    elif rank == 1:
        x = torch.tensor([2.])
    elif rank == 2:
        x = torch.tensor([-3.])
    
    dist.all_reduce(x, op=dist.reduce_op.SUM)
    print('Rank {} has {}'.format(rank, x))

dasayan05

model = LeNet()
# first synchronization of initial weights
sync_initial_weights(model, rank, world_size)

optimizer = optim.SGD(model.parameters(), lr=1e-3, momentum=0.85)

model.train()
for epoch in range(1, epochs + 1):
    for data, target in train_loader:
        optimizer.zero_grad()

dasayan05

def sync_initial_weights(model, rank, world_size):
    for param in model.parameters():
        if rank == 0:
            # Rank 0 is sending it's own weight
            # to all it's siblings (1 to world_size)
            for sibling in range(1, world_size):
                dist.send(param.data, dst=sibling)
        else:
            # Siblings must recieve the parameters
            dist.recv(param.data, src=0)

dasayan05

def sync_gradients(model, rank, world_size):
    for param in model.parameters():
        dist.all_reduce(param.grad.data, op=dist.reduce_op.SUM)

dasayan05

import pyro, torch, numpy as np
import pyro.distributions as dist
import pyro.optim as optim
import pyro.infer as infer
import matplotlib.pyplot as plt
plt.style.use('ggplot')
from scipy.stats import norm
plt.ioff()

def getdata(N, mean1=2.0, mean2=-1.0, std1=0.5, std2=0.5):