My code runs mcmc sampling multiple times for few datasets. The issue I am having is that R ends up using GBs of memory and brings the system to halt.
I am using the development version of greta.
I created a similar code to show what happens,
library(greta)
library(titanic)
library(pryr)
# function to run mcmc and get samples
drawsample = function(X, y, w_mean, w_sigma){
# Prior
w = multivariate_normal(t(w_mean), w_sigma, n_realisations = 1)
# define distribution over output
linear = X %*% t(w)
p = ilogit(linear)
distribution(y) = bernoulli(p)
# define model
m = model(w)
# draw samples
draws <- mcmc(m, n_samples = 1000, chains = 1)
return(draws)
}
cols = c('Age', 'Parch', 'Fare', 'Survived')
data = titanic_train[cols]
data = data[complete.cases(data), ]
X = data[c('Age', 'Parch', 'Fare')]
y = data$Survived
num_features = 3
w_mean = zeros(num_features)
w_sigma = diag(num_features)
for(iter in 1:5){
print(mem_change(drawsample(X,y, w_mean, w_sigma)))
print(mem_used())
}
The output I get is like following,
warmup ====================================== 1000/1000 | eta: 0s
sampling ====================================== 1000/1000 | eta: 0s
169 kB
191 MB
warmup ====================================== 1000/1000 | eta: 0s
sampling ====================================== 1000/1000 | eta: 0s
816 kB
192 MB
warmup ====================================== 1000/1000 | eta: 0s
sampling ====================================== 1000/1000 | eta: 0s
588 kB
192 MB
warmup ====================================== 1000/1000 | eta: 0s
sampling ====================================== 1000/1000 | eta: 0s
775 kB
193 MB
warmup ====================================== 1000/1000 | eta: 0s
sampling ====================================== 1000/1000 | eta: 0s
1.18 MB
193 MB
What is the possible issue here?
EDIT: Upon further investigation, it seems to me that all the greta variables I define are leaking memory, and not just the mcmc function.
