Hi everyone,

I am new in Julia. My goal is to solve stiff ODE systems (N>1000) representing chemical reaction networks up to steady-state conditions. Since the Python Scipy implementation is too slow for the purpose, I just started learning Julia as DifferentialEquations.jl allows GPU acceleration. I wrote the following piece of code where I call Julia from a Python method:

```
def integrate_jl(self, y0):
from julia.api import Julia
jl = Julia()
from julia import Main
Main.y0 = y0 # (Ny,1) vector of Float64
Main.v = self.v_dense # (Ny,NR) Matrix of Int8
Main.vf = self.v_forward_dense # (NR,Ny) Matrix of Int8
Main.vb = self.v_backward_dense # (NR,Ny) matrix of Int8
Main.kd, Main.kr = self.kd, self.kr # (NR,1) vector of Float64
Main.gas_mask = self.gas_mask # (Ny, 1) vector of Bool
Main.eval("""
using CUDA
using DifferentialEquations
CUDA.allowscalar(true)
y0 = CuArray{Float64}(y0)
v = CuArray{Int8}(v)
kd = CuArray{Float64}(kd)
kr = CuArray{Float64}(kr)
vf = CuArray{Int8}(vf)
vb = CuArray{Int8}(vb)
gas_mask = CuArray{Bool}(gas_mask)
p = (v = v, kd = kd, kr = kr, gas_mask = gas_mask, vf = vf, vb = vb)
function ode_pfr!(du, u, p, t)
rate = p.kd .* vec(prod((u .^ p.vf')', dims=2)) .- p.kr .* vec(prod((u .^ p.vb')', dims=2))
du = vec(p.v * net_rate)
du[p.gas_mask] .= 0.0
end
prob = SteadyStateProblem(ode_pfr!, y0, p)
sol = solve(prob, DynamicSS(QNDF()))
CUDA.allowscalar(false)
""")
...
```

However, I keep getting the following `'ArgumentError: cannot take the CPU address of a CuArray{Float64, 2, CUDA.Mem.DeviceBuffer}'`

.

I managed to make it work on the CPU, but on the GPU I am struggling a lot.