JuliaCall Update: Automated Julia Installation for R Packages
January 18 2021 in Differential Equations, Julia, Mathematics, Programming, R | Tags: devops, differentialequations, installation, julia, juliacall, modelingtoolkit, r | Author: Christopher Rackauckas
Some sneakily cool features made it into the JuliaCall v0.17.2 CRAN release. With the latest version there is now an install_julia function for automatically installing Julia. This makes Julia a great high performance back end for R packages. For example, the following is an example from the diffeqr package that will work, even without Julia installed:
install.packages("diffeqr")
library(diffeqr)
de <- diffeqr::diffeq_setup()
lorenz <- function (u,p,t){
du1 = p[1]*(u[2]-u[1])
du2 = u[1]*(p[2]-u[3]) - u[2]
du3 = u[1]*u[2] - p[3]*u[3]
c(du1,du2,du3)
}
u0 <- c(1.0,1.0,1.0)
tspan <- c(0.0,100.0)
p <- c(10.0,28.0,8/3)
prob <- de$ODEProblem(lorenz,u0,tspan,p)
fastprob <- diffeqr::jitoptimize_ode(de,prob)
sol <- de$solve(fastprob,de$Tsit5(),saveat=0.01)Under the hood it’s using the DifferentialEquations.jl package and the SciML stack, but it’s abstracted from users so much that Julia is essentially an alternative to Rcpp with easier interactive development. The following example really brings the seamless integration home:
install.packages(diffeqr)
library(diffeqr)
de <- diffeqr::diffeq_setup()
degpu <- diffeqr::diffeqgpu_setup()
lorenz <- function (u,p,t){
du1 = p[1]*(u[2]-u[1])
du2 = u[1]*(p[2]-u[3]) - u[2]
du3 = u[1]*u[2] - p[3]*u[3]
c(du1,du2,du3)
}
u0 <- c(1.0,1.0,1.0)
tspan <- c(0.0,100.0)
p <- c(10.0,28.0,8/3)
prob <- de$ODEProblem(lorenz,u0,tspan,p)
fastprob <- diffeqr::jitoptimize_ode(de,prob)
prob_func <- function (prob,i,rep){
de$remake(prob,u0=runif(3)*u0,p=runif(3)*p)
}
ensembleprob = de$EnsembleProblem(fastprob, prob_func = prob_func, safetycopy=FALSE)
sol <- de$solve(ensembleprob,de$Tsit5(),degpu$EnsembleGPUArray(),trajectories=10000,saveat=0.01)This example does the following:
- Automatically installs Julia
- Automatically installs DifferentialEquations.jl
- Automatically installs CUDA (via CUDA.jl)
- Automatically installs ModelingToolkit.jl and DiffEqGPU.jl
- JIT transpiles the R function to Julia via ModelingToolkit
- Uses KernelAbstractions (in DiffEqGPU) to generate a CUDA kernel from the Julia code
- Solves the ODE 10,000 times with different parameters 350x over deSolve
What a complicated code! Well maybe it would shock you to know that the source code for the diffeqr package is only 150 lines of code. Of course, it’s powered by a lot of Julia magic in the backend, and so can your next package. For more details, see the big long post about differential equation solving in R with Julia.
Christopher Rackauckas
says:Looks like this may have gone away? For more details see https://github.com/SciML/diffeqr/issues/33
David Katz
says:Sure, I will do that.
David Katz
says:Tried your sample code in Windows 10 but got errors. Any help appreciated.
R version 4.0.3 (2020-10-10) — “Bunny-Wunnies Freak Out”
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (64-bit)
…
Errors:
package ‘JuliaCall’ successfully unpacked and MD5 sums checked
package ‘diffeqr’ successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\dkatz\AppData\Local\Temp\RtmpyuuAyP\downloaded_packages
> library(diffeqr)
> de
Christopher Rackauckas
says:Oh no! Could you open an issue at https://github.com/SciML/diffeqr and share the full trace? It got cut off here so I can’t quite see the what you pasted. I’d like to ping my collaborators and get them on board and helping too. This automated installation is all brand new so we probably do need to work out a few hiccups still and bug reports are the only way!