Some thoughts on Turing.jl
Turing is a cool probabilistic programming new language written on top of Julia. Mostly I just wanted to play around with a different probabilistic programming language, and discard the low-probability hypothesis that things that I am currently doing in Squiggle could be better implemented in it.
My thoughts after downloading it and playing with it a tiny bit are as follows:
1. Installation is annoying: The program is pretty heavy, and it requires several steps (you have to install Julia and then Turing as a package, which is annoying (e.g., I had to figure out where in the filesystem to put the Julia binaries).
- Node.js installations can also be pretty gnarly (though there is nvm), but Turing doesn’t have an equivalent online playground. My sense is that running Julia online would also be pretty annoying (?).
2. Compilation and running the thing is slow; 9 seconds until I get an error (I hadn’t installed a necessary package), and then 1 min 26 seconds to run their simplest example (!!)
using Turing using StatsPlots # Define a simple Normal model with unknown mean and variance. @model function gdemo(x, y) s² ~ InverseGamma(2, 3) m ~ Normal(0, sqrt(s²)) x ~ Normal(m, sqrt(s²)) y ~ Normal(m, sqrt(s²)) end # Run sampler, collect results chn = sample(gdemo(1.5, 2), HMC(0.1, 5), 1000) # Summarise results describe(chn) # Plot and save results p = plot(chn) savefig("gdemo-plot.png")
This seems like this is a problem with Julia more generally. Btw, the Julia webpage mentions that Julia “feels like a scripting language”, which seems like a bold-faced lie.
A similar but not equivalent 1 model in Squiggle would run in seconds, and allow for the fast iteration that I know and love:
s = (0.1 to 1)^(1/2) // squiggle doesn't have the inverse gamma function yet m = normal(0, s) x = normal(m, s) y = normal(m, s)
3. Turing is able to do Bayesian inference over parameters, which seems cool & intend to learn more about.
It’s probably kind of weird that Squiggle, as a programming language that manipulates distributions, doesn’t allow for Bayesian inference.
4. Turing seems pretty integrated with Julia, and the documentation seems to assume familiarity with Julia. This can have pros and cons, but made it difficult to just grasp what they are doing.
- The pros are that it can use all the Julia libraries, and this looks like it is very powerful
- The cons are that it requires familiarity with Julia.
5. Turing seems like it could drive some hardcore setups. E.g., here is a project using it to generate election forecasts.
Overall, I dislike the slowness and, as an outsider, the integration with Julia, but I respect the effort. It’s possible but not particularly likely that we may want to first script models in Squiggle and then translate them to a more powerful languages like Turing when speed is not a concern and we need capabilities not natively present in Squiggle (like Baysian inference).
- Even if Squiggle had the inverse gamma function, it’s not clear to me that the two programs are doing the same thing, because Turing could be doing something trickier even in that simple example (?). E.g., Squiggle is drawing samples whereas Turing is (?) representing the space of distributions with those pararmeters. This is something I didn’t understand from the documentation.↩