I tried to play around with the tutorial example as follows, if I run it on CPU (use_cuda = False) the elapsed time is rough 53s but It takes 153s running on GPU (use_cuda = True), so what’s wrong with my implementation? Thanks.
pyro version 0.1.2
pytorch version 0.3
import torch
from torch.autograd import Variable
import pyro
import pyro.distributions as dist
import pyro.optim as optim
import pyro.infer as infer
import time
pyro.clear_param_store()
data = []
for _ in range(60):
data.append(1)
for _ in range(4):
data.append(0)
use_cuda = False
if use_cuda == True:
data = Variable(torch.Tensor(data)).view(-1, 1).cuda()
else:
data = Variable(torch.Tensor(data)).view(-1, 1)
def model(data):
α = Variable(torch.Tensor([10.0])).type_as(data)
β = Variable(torch.Tensor([10.0])).type_as(data)
# prior distribution
θ = pyro.sample("θ", dist.beta, α, β)
# generating observations
with pyro.iarange("observed data", use_cuda=use_cuda):
pyro.sample("obs", dist.bernoulli, θ, obs=data)
def guide(data):
log_α = Variable(torch.Tensor([2.7]), requires_grad=True)
log_β = Variable(torch.Tensor([2.7]), requires_grad=True)
log_α = pyro.param("log alpha", log_α).type_as(data)
log_β = pyro.param("log beta", log_β).type_as(data)
α, β = torch.exp(log_α), torch.exp(log_β)
pyro.sample("θ", dist.beta, α, β)
adam_params = {"lr": 0.001}
optimizer = optim.Adam(adam_params)
svi = infer.SVI(model, guide, optimizer, loss="ELBO", num_particles=5)
tic = time.time()
n_steps = 4000
for step in range(n_steps):
loss = svi.step(data)
if step % 100 == 0:
print("loss = {}".format(loss))
print("time passed {}".format(time.time()-tic))
α = torch.exp(pyro.param("log alpha")).data.numpy()[0]
β = torch.exp(pyro.param("log beta")).data.numpy()[0]
print(α, β)
print(α / (α + β))