That’s a bit scary, but suddenly all of my models that were working previously, started to crush once the sampling is done:

`Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)`

How can I solve it??

That’s a bit scary, but suddenly all of my models that were working previously, started to crush once the sampling is done:

`Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)`

How can I solve it??

Update - I checked it on three different machines. Models weren’t changed. Different models crush in all three machines.

Update 2 - for models that do run, I get the following error after sampling:

```
/Users/venv/lib/python3.6/site-packages/pystan/diagnostics.py:213: RuntimeWarning: invalid value encountered in true_divide
numer = ((np.diff(energies, axis=0)**2).sum(axis=0)) / chain_len
/Users/venv/lib/python3.6/site-packages/numpy/core/fromnumeric.py:3584: RuntimeWarning: Degrees of freedom <= 0 for slice
**kwargs)
/Users/venv/lib/python3.6/site-packages/numpy/core/_methods.py:186: RuntimeWarning: invalid value encountered in true_divide
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
/Users/venv/lib/python3.6/site-packages/numpy/core/_methods.py:207: RuntimeWarning: invalid value encountered in true_divide
ret, rcount, out=ret, casting='unsafe', subok=False)
WARNING:pystan:Truncated summary with the 'fit.__repr__' method. For the full summary use 'print(fit)'
```

When I hit print(fit), I get

`ValueError: Stan model anon_model_c827ea328eaaf47fe42cabc2262ba4a1 contains no samples.`

All model worked just fine before.

Update 3 (feeling really stupid) - so all models crush when I run then with 1 chain and small number of iterations (500). All goes back to normal when I increase both (4 chains and 1500 iterations). Have no idea why this is, but all works again.

Don’t feel stupid, that’s definitely not supposed to crash. Can you show the exact code that crashes?

1 Like

this is one of them (the one we discussed in a separate thread)

```
functions {
real comp_error(real c, real Delta, real sigma){
real out;
out = 0.5 * (erfc((c-Delta)/(sqrt(2)*sigma)) + erfc((c+Delta)/(sqrt(2)*sigma)));
return out;
}
}
data {
int<lower=1> N;
int<lower=1> P;
int<lower=1> T_max;
int<lower=0> Tr[N];
int<lower=0> Nb[N, T_max];
int<lower=0> Tar[N, T_max];
real<lower=0> Delta[N, T_max];
int<lower=-1, upper=1> choice_cd[N, T_max];
}
parameters {
// Hyper(group)-parameters
vector[P] mu_prior;
vector<lower=0>[P] tau;
// Subject-level raw parameters
vector[N] c_pr;
vector[N] sigma_pr;
}
transformed parameters {
vector<lower=0>[N] c;
vector<lower=0>[N] sigma;
c = exp(c_pr);
sigma = exp(sigma_pr);
}
model {
real f;
real h;
//priors
mu_prior ~ normal(0,5); //weakly informative priors on the regression coefficients
tau ~ cauchy(0,2.5); //weakly informative priors, see section 6.9 in STAN user guide
c_pr ~ normal(mu_prior[1],tau[1]);
sigma_pr ~ normal(mu_prior[2],tau[2]);
for (n in 1:N) {
f = comp_error(c[n], 0, sigma[n]);
for (t in 1:Tr[n]) {
if (choice_cd[n,t] == 1 && Delta[n,t] > 0) {
h = comp_error(c[n], Delta[n,t], sigma[n]);
target += log(1 - (pow((1-h), Tar[n,t]) * pow((1-f),(Nb[n,t]-Tar[n,t]))));
}
else if (choice_cd[n,t] == 0 && Delta[n,t] > 0) {
h = comp_error(c[n], Delta[n,t], sigma[n]);
target += log(pow((1-h),Tar[n,t]) * pow((1-f),(Nb[n,t]-Tar[n,t])));
}
else if (choice_cd[n,t] == 1 && Delta[n,t] == 0) {
target += log(1 - pow((1-f),Nb[n,t]));
}
else if (choice_cd[n,t] == 0 && Delta[n,t] == 0) {
target += log(pow((1-f),Nb[n,t]));
}
}
}
}
```

Hey, @ahartikainen, PyStan devs might want to know that

```
model.sampling(iter=500,warmup=500)
```

segfaults. That seems kind of bad.

@nerpa note that `iter`

includes warmup. If you want 500 warmup and 500 post-warmup samples you need to call

```
model.sampling(iter=1000,warmup=500,chains=1)
```

yeah! Learning it the hard way. I had

`fit = stan.sampling(data=data, iter=500, chains=1, warmup=500, thin=5)`

Thanks again!

It doesn’t explain why it segfaults, but calling with `sampling(iter=500, warmup=500)`

explains the zero division errors from Python – even if it didn’t crash there wouldn’t be any samples available.

Good to know. It did that some point with Windows.

I think we should add a check for that.

@ariddell I think this is already fixed in pystan3