store only one PBmodel in HybridProblem for n_batch sites

and use create_nsite_applicator to create the ones for n_site.
Additionally, adjust docu to use is_testmode=true in calls to calls that eventually invoke the ML model

Author: bgctw

Project.toml

@@ -1,7 +1,7 @@
 name = "HybridVariationalInference"
 uuid = "a108c475-a4e2-4021-9a84-cfa7df242f64"
 authors = ["Thomas Wutzler <[email protected]> and contributors"]
-version = "1.0.0-DEV"
+version = "0.2"
 
 [deps]
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"

dev/doubleMM.jl

@@ -560,7 +560,8 @@ end
 end
 
 #ζi = first(eachrow(Array(chain)))
-f_allsites = get_hybridproblem_PBmodel(prob0; scenario, use_all_sites = true)
+f = get_hybridproblem_PBmodel(probc; scenario)
+f_allsites = create_nsite_applicator(f, n_site)
 #ζs = mapreduce(ζi -> transposeMs(ζi, intm_PMs_gen, true), hcat, eachrow(Array(chain)));
 ζsP = Array(chain)[:,1:n_θP]'
 ζsMst = reshape(Array(chain)[:,(n_θP+1) : end], n_sample_NUTS, n_site, n_θM)

docs/_quarto.yml

@@ -3,6 +3,10 @@ project:
   render: 
     - src/tutorials/basic_cpu.qmd
     - src/tutorials/*.qmd
+# julia:
+#     # workaround for quarto not pciking up the correct environment variable
+#     # https://github.com/quarto-dev/quarto-cli/issues/13416#issuecomment-3333700286
+#     env: ["JULIA_DEPOT_PATH=/User/homes/twutz/scratch/twutz/julia_gpu_depots"]
 
 
 

docs/src/tutorials/basic_cpu.md

@@ -27,7 +27,8 @@ The example process based model (PBM) predicts a double-monod constrained rate
 for different substrate concentrations, `S1`, and `S2`.
 
 $$
-y = r_0+ r_1 \frac{S_1}{K_1 + S_1} \frac{S_2}{K_2 + S_2}$$
+y = r_0+ r_1 \frac{S_1}{K_1 + S_1} \frac{S_2}{K_2 + S_2}
+$$
 
 ``` julia
 function f_doubleMM(θc::CA.ComponentVector{ET}, x) where ET
@@ -229,10 +230,10 @@ given a vector of global parameters, and a matrix of site parameters to
 invocation of the process based model (PBM), defined at the beginning.
 
 ``` julia
-f_batch = f_allsites = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1])
+f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1])
 
 prob = HybridProblem(θP, θM, g_chain_scaled, ϕg0, 
-    f_batch, f_allsites, priors_dict, py,
+    f_batch, priors_dict, py,
     transM, transP, train_dataloader, n_covar, n_site, n_batch)
 ```
 
@@ -241,6 +242,11 @@ prob = HybridProblem(θP, θM, g_chain_scaled, ϕg0,
 Eventually, having assembled all the moving parts of the HVI, we can perform
 the inversion.
 
+``` julia
+# silence warning of no GPU backend found (because we did not import CUDA here)
+ENV["MLDATADEVICES_SILENCE_WARN_NO_GPU"] = 1
+```
+
 ``` julia
 using OptimizationOptimisers
 import Zygote
@@ -313,8 +319,7 @@ The HVI Problem needs to be updated with this new applicatior.
 
 ``` julia
 f_batch = PBMPopulationApplicator(f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1])
-f_allsites = PBMPopulationApplicator(f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1])
-probo_sites = HybridProblem(probo; f_batch, f_allsites)
+probo_sites = HybridProblem(probo; f_batch)
 ```
 
 For numerical efficiency, the number of sites within one batch is part of the
@@ -345,8 +350,7 @@ module `Main` to allow for easier reloading with JLD2.
 
 ``` julia
 f_batch = PBMPopulationApplicator(DoubleMM.f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1])
-f_allsites = PBMPopulationApplicator(DoubleMM.f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1])
-probo2 = HybridProblem(probo; f_batch, f_allsites)
+probo2 = HybridProblem(probo; f_batch)
 ```
 
 ``` julia

docs/src/tutorials/basic_cpu.qmd

@@ -36,7 +36,8 @@ The example process based model (PBM) predicts a double-monod constrained rate
 for different substrate concentrations, `S1`, and `S2`.
 
 $$
-y = r_0+ r_1 \frac{S_1}{K_1 + S_1} \frac{S_2}{K_2 + S_2}$$
+y = r_0+ r_1 \frac{S_1}{K_1 + S_1} \frac{S_2}{K_2 + S_2}
+$$
 
 ```{julia}
 function f_doubleMM(θc::CA.ComponentVector{ET}, x) where ET
@@ -239,10 +240,10 @@ given a vector of global parameters, and a matrix of site parameters to
 invocation of the process based model (PBM), defined at the beginning.
 
 ```{julia}
-f_batch = f_allsites = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1])
+f_batch = PBMSiteApplicator(f_doubleMM; θP, θM, θFix, xPvec=xP[:,1])
 
 prob = HybridProblem(θP, θM, g_chain_scaled, ϕg0, 
-    f_batch, f_allsites, priors_dict, py,
+    f_batch, priors_dict, py,
     transM, transP, train_dataloader, n_covar, n_site, n_batch)
 ```
 
@@ -265,7 +266,7 @@ y1 = f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))[2]
     #using Cthulhu
     #@descend_code_warntype f_batch(CA.getdata(θP), CA.getdata(θMs), CA.getdata(x_batch))
     prob0 = HVI.DoubleMM.DoubleMMCase()
-    f_batch0 = get_hybridproblem_PBmodel(prob0; use_all_sites = false)
+    f_batch0 = get_hybridproblem_PBmodel(prob0)
     y1f = f_batch0(θP, θMs, x_batch)[2]
     y1 .- y1f  # equal
 end
@@ -276,6 +277,11 @@ end
 Eventually, having assembled all the moving parts of the HVI, we can perform 
 the inversion.
 
+```{julia}
+# silence warning of no GPU backend found (because we did not import CUDA here)
+ENV["MLDATADEVICES_SILENCE_WARN_NO_GPU"] = 1
+```
+
 ```{julia}
 using OptimizationOptimisers
 import Zygote
@@ -349,8 +355,7 @@ The HVI Problem needs to be updated with this new applicatior.
 
 ```{julia}
 f_batch = PBMPopulationApplicator(f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1])
-f_allsites = PBMPopulationApplicator(f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1])
-probo_sites = HybridProblem(probo; f_batch, f_allsites)
+probo_sites = HybridProblem(probo; f_batch)
 ```
 
 For numerical efficiency, the number of sites within one batch is part of the 
@@ -380,8 +385,7 @@ module `Main` to allow for easier reloading with JLD2.
 
 ```{julia}
 f_batch = PBMPopulationApplicator(DoubleMM.f_doubleMM_sites, n_batch; θP, θM, θFix, xPvec=xP[:,1])
-f_allsites = PBMPopulationApplicator(DoubleMM.f_doubleMM_sites, n_site; θP, θM, θFix, xPvec=xP[:,1])
-probo2 = HybridProblem(probo; f_batch, f_allsites)
+probo2 = HybridProblem(probo; f_batch)
 ```
 
 ```{julia}
@@ -397,4 +401,4 @@ end
 #| eval: false
 #| echo: false
 probo = load(fname, "probo"; iotype = IOStream);
-```
+```