@@ -7,6 +7,27 @@ using StableRNGs: StableRNG
77
88rng = StableRNG (23 )
99
10+ function print_results (results_table)
11+ table_matrix = hcat (Iterators. map (collect, zip (results_table... ))... )
12+ header = [
13+ " Model" ,
14+ " Dim" ,
15+ " AD Backend" ,
16+ " VarInfo" ,
17+ " Linked" ,
18+ " t(eval)/t(ref)" ,
19+ " t(grad)/t(eval)" ,
20+ ]
21+ return pretty_table (
22+ table_matrix;
23+ column_labels= header,
24+ backend= :text ,
25+ formatters= [fmt__printf (" %.1f" , [6 , 7 ])],
26+ fit_table_in_display_horizontally= false ,
27+ fit_table_in_display_vertically= false ,
28+ )
29+ end
30+
1031# Create DynamicPPL.Model instances to run benchmarks on.
1132smorgasbord_instance = Models. smorgasbord (randn (rng, 100 ), randn (rng, 100 ))
1233loop_univariate1k, multivariate1k = begin
@@ -82,17 +103,9 @@ for (model_name, model, varinfo_choice, adbackend, islinked) in chosen_combinati
82103 relative_ad_eval_time,
83104 ),
84105 )
106+ println (" Results so far:" )
107+ print_results (results_table)
85108end
86109
87- table_matrix = hcat (Iterators. map (collect, zip (results_table... ))... )
88- header = [
89- " Model" , " Dim" , " AD Backend" , " VarInfo" , " Linked" , " t(eval)/t(ref)" , " t(grad)/t(eval)"
90- ]
91- pretty_table (
92- table_matrix;
93- column_labels= header,
94- backend= :text ,
95- formatters= [fmt__printf (" %.1f" , [6 , 7 ])],
96- fit_table_in_display_horizontally= false ,
97- fit_table_in_display_vertically= false ,
98- )
110+ println (" Final results:" )
111+ print_results (results_table)
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