when I try to run the DynamicNUTS example from the documentation upon loading the libraries (and having installed current versions) the below issues occur:
Warning: Error requiring LogDensityProblems from Turing:
UndefVarError: AbstractLogDensityProblem not defined
as well as:
Warning: Error requiring DynamicHMC from Turing:
UndefVarError: NUTS_init_tune_mcmc not defined
This occured on two separate computers (both running windows 10) on a fresh install of julia.
Am I doing something wrong or did something break?
All the best and thanks,
Tamas updated DynamicHMC a little while ago, and it broke Turing’s interface to it. I thought we had prevented these kinds of errors, though – could you print out the results of
] status so I can see the version of everything?
As a side note, Turing uses the excellent AdvancedHMC as it’s default HMC backend. It provides NUTS, HMCDA, and HMC samplers, which you can use via
chain = sample(model, HMC(0.01, 7), 1000)
chain = sample(model, NUTS(), 1000)
chain = sample(model, HMCDA(200, 0.65, 0.3), 1000)
thanks for the quick reply.
I didn’t know about the AdvancedHMC stuff, I think that wasn’t there when I last looked at Turing. I’ll give it a shot right away
Here’s the status print:
[c52e3926] Atom v0.11.3
[bbc10e6e] DynamicHMC v2.1.0
[e5e0dc1b] Juno v0.7.2
[6fdf6af0] LogDensityProblems v0.9.1
[ce6b1742] RDatasets v0.6.4
[f3b207a7] StatsPlots v0.12.0
[fce5fe82] Turing v0.7.2
[e88e6eb3] Zygote v0.3.4
All changes to the DynamicHMC API follow SemVer; if there is one that didn’t please open an issue.
@Tamas_Papp would you be willing to have a look at the current inference implemented between Turing and DynamicHMC? I’m sure you will be much fast than anyone from the Turing team to figure out what has to be done.
I think it would be great if we could continue supporting DynamicHMC as a backend.
but the Turing test suite seems to be broken, so I could not run all the tests. The DynamicHMC part works now.