tutorial on using gpu and LUX

and setup quarto project

Author: bgctw

docs/.gitignore

@@ -0,0 +1 @@
+/.quarto/

docs/_quarto.yml

@@ -0,0 +1,9 @@
+project:
+  title: "HybridVariationInference documentation"
+  render: 
+    - src/tutorials/basic_cpu.qmd
+    - src/tutorials/*.qmd
+    
+
+
+

docs/make.jl

@@ -25,6 +25,7 @@ makedocs(;
         "How to" => [
             ".. model independent parameters" => "tutorials/blocks_corr.md",
             ".. model site-global corr" => "tutorials/corr_site_global.md",
+            ".. use GPU" => "tutorials/lux_gpu.md",
         ],
         "Explanation" => [
             #"Theory" => "explanation/theory_hvi.md", TODO activate when paper is published

docs/src/tutorials/Project.toml

@@ -1,11 +1,13 @@
 [deps]
 Bijectors = "76274a88-744f-5084-9051-94815aaf08c4"
+CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 CairoMakie = "13f3f980-e62b-5c42-98c6-ff1f3baf88f0"
 CommonSolve = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
 DistributionFits = "45214091-1ed4-4409-9bcf-fdb48a05e921"
 HybridVariationalInference = "a108c475-a4e2-4021-9a84-cfa7df242f64"
 JLD2 = "033835bb-8acc-5ee8-8aae-3f567f8a3819"
+Lux = "b2108857-7c20-44ae-9111-449ecde12c47"
 MLUtils = "f1d291b0-491e-4a28-83b9-f70985020b54"
 OptimizationOptimisers = "42dfb2eb-d2b4-4451-abcd-913932933ac1"
 PairPlots = "43a3c2be-4208-490b-832a-a21dcd55d7da"
@@ -14,3 +16,4 @@ SimpleChains = "de6bee2f-e2f4-4ec7-b6ed-219cc6f6e9e5"
 StableRNGs = "860ef19b-820b-49d6-a774-d7a799459cd3"
 StatsFuns = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
+cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"

docs/src/tutorials/basic_cpu.md

@@ -327,7 +327,7 @@ epochs of the optimization.
 ``` julia
 (; probo) = solve(probo_sites, solver; rng,
     callback = callback_loss(100), # output during fitting
-    epochs = 10,
+    epochs = 20,
     #is_inferred = Val(true), # activate type-checks 
 );
 ```

docs/src/tutorials/basic_cpu.qmd

@@ -7,7 +7,7 @@ execute:
   daemon: 3600  
 format:
   commonmark:
-    variant: -raw_html
+    variant: -raw_html+tex_math_dollars
     wrap: preserve    
 bibliography: twutz_txt.bib
 ---

docs/src/tutorials/blocks_corr_files/figure-commonmark/cell-11-output-1.png

@@ -14,11 +14,8 @@ HVI requires the site-parameter blocks in the correlation matrix to be
 independent of the global-parameter block.
 
 However, correlations between site and global parameters can be modeled
-by predicting different (transformed) site parameters,
-$\zeta_{Ms}$
-, given the sampled
-(transformed) global parameters,
-$\zeta_P$
+by predicting different (transformed) site parameters, $\zeta_{Ms}$
+, given the sampled (transformed) global parameters, $\zeta_P$.
 
 $$
 p(\zeta_{Ms}, \zeta_P) = p(\zeta_{Ms} | \zeta_P) p(\zeta_P)$$
@@ -69,7 +66,7 @@ pbm_covars = (:K2,)
 
 The ML model needs to be adapted to consume more inputs than the site covariates.
 Note the changed `n_input` specification compared to the
-[Basic workflow without GPU](@ref) tutorial.
+[Basic workflow without GPU](@ref) turorial.
 
 ``` julia
 n_out = length(prob.θM) # number of individuals to predict