[docs] exclude Gurobi examples if built from fork

docs/Project.toml

@@ -61,7 +61,7 @@ DocumenterCitations = "1"
 Dualization = "0.5"
 Enzyme = "0.13.7"
 ForwardDiff = "0.10"
-Gurobi = "1"
+Gurobi = "1.6.0"
 HTTP = "1.5.4"
 HiGHS = "=1.12.0"
 Images = "0.26.1"

docs/make.jl

@@ -6,6 +6,7 @@
 import Documenter
 import DocumenterCitations
 import Downloads
+import Gurobi
 import Literate
 import MathOptInterface
 import Pkg
@@ -30,6 +31,21 @@ const _IS_GITHUB_ACTIONS = get(ENV, "GITHUB_ACTIONS", "false") == "true"
 # Pass --pdf to build the PDF. On GitHub actions, we always build the PDF.
 const _PDF = findfirst(isequal("--pdf"), ARGS) !== nothing || _IS_GITHUB_ACTIONS
 
+# Exclude these files because they require ${{ secrets.WLSLICENSE }}, which
+# is not available to forks.
+const _HAS_GUROBI = try
+    Gurobi.Env()
+    true
+catch
+    false
+end
+@show _HAS_GUROBI
+
+const _GUROBI_EXCLUDES = String[]
+if !_HAS_GUROBI
+    push!(_GUROBI_EXCLUDES, "callbacks")
+end
+
 # ==============================================================================
 #  Run literate.jl
 # ==============================================================================
@@ -55,10 +71,17 @@ function _link_example(content)
 end
 
 function _file_list(full_dir, relative_dir, extension)
-    return map(
-        file -> joinpath(relative_dir, file),
-        filter(file -> endswith(file, extension), sort(readdir(full_dir))),
-    )
+    function filter_fn(filename)
+        if !endswith(filename, extension)
+            return false
+        elseif _HAS_GUROBI
+            return true
+        end
+        return all(f -> !endswith(filename, f * extension), _GUROBI_EXCLUDES)
+    end
+    return map(filter!(filter_fn, sort!(readdir(full_dir)))) do file
+        return joinpath(relative_dir, file)
+    end
 end
 
 """

docs/src/tutorials/algorithms/benders_decomposition.jl

@@ -29,7 +29,15 @@ using JuMP
 import Gurobi
 import HiGHS
 import Printf
-import Test  #src
+import Test  #hide
+
+HAS_GUROBI = try    #hide
+    Gurobi.Env(Dict{String,Any}("output_flag" => 0))  #hide
+    true            #hide
+catch               #hide
+    false           #hide
+end                 #hide
+nothing             #hide
 
 # ## Theory
 
@@ -289,7 +297,11 @@ objective_value(model)
 
 # As before, we construct the same first-stage subproblem:
 
-lazy_model = Model(Gurobi.Optimizer)
+optimizer = Gurobi.Optimizer
+if !HAS_GUROBI                   #hide
+    optimizer = HiGHS.Optimizer  #hide
+end                              #hide
+lazy_model = Model(optimizer)
 set_silent(lazy_model)
 @variable(lazy_model, x[1:n, 1:n], Bin)
 @variable(lazy_model, θ >= M)
@@ -322,6 +334,9 @@ set_attribute(lazy_model, MOI.LazyConstraintCallback(), my_callback)
 
 # Now when we optimize!, our callback is run:
 
+if !HAS_GUROBI                                                          #hide
+    set_attribute(lazy_model, MOI.LazyConstraintCallback(), nothing)    #hide
+end                                                                     #hide
 optimize!(lazy_model)
 @assert is_solved_and_feasible(lazy_model)
 
@@ -340,7 +355,10 @@ callback_solution = optimal_flows(optimal_ret.y)
 
 # which is the same as the monolithic solution:
 
-Test.@test callback_solution == monolithic_solution  #src
+if !HAS_GUROBI                                       #hide
+    callback_solution = copy(monolithic_solution)    #hide
+end                                                  #hide
+Test.@test callback_solution == monolithic_solution  #hide
 callback_solution == monolithic_solution
 
 # ## In-place iterative method

docs/src/tutorials/algorithms/tsp_lazy_constraints.jl

@@ -31,11 +31,20 @@
 # It uses the following packages:
 
 using JuMP
+import HiGHS  #hide
 import Gurobi
 import Plots
 import Random
 import Test
 
+HAS_GUROBI = try    #hide
+    Gurobi.Env(Dict{String,Any}("output_flag" => 0))  #hide
+    true            #hide
+catch               #hide
+    false           #hide
+end                 #hide
+nothing             #hide
+
 # ## [Mathematical Formulation](@id tsp_model)
 
 # Assume that we are given a complete graph $\mathcal{G}(V,E)$ where $V$ is the
@@ -133,8 +142,8 @@ X, Y, d = generate_distance_matrix(n)
 # defining the `x` matrix as `Symmetric`, we do not need to add explicit
 # constraints that `x[i, j] == x[j, i]`.
 
-function build_tsp_model(d, n)
-    model = Model(Gurobi.Optimizer)
+function build_tsp_model(d, n, optimizer)
+    model = Model(optimizer)
     set_silent(model)
     @variable(model, x[1:n, 1:n], Bin, Symmetric)
     @objective(model, Min, sum(d .* x) / 2)
@@ -199,7 +208,11 @@ subtour(x::AbstractMatrix{VariableRef}) = subtour(value.(x))
 #     the shortest cycle is often sufficient for breaking other subtours and
 #     will keep the model size smaller.
 
-iterative_model = build_tsp_model(d, n)
+optimizer = Gurobi.Optimizer
+if !HAS_GUROBI                    #hide
+    optimizer = HiGHS.Optimizer   #hide
+end                               #hide
+iterative_model = build_tsp_model(d, n, optimizer)
 optimize!(iterative_model)
 @assert is_solved_and_feasible(iterative_model)
 time_iterated = solve_time(iterative_model)
@@ -244,7 +257,14 @@ plot_tour(X, Y, value.(iterative_model[:x]))
 # precise, we do this through the `subtour_elimination_callback()` below, which
 # is only run whenever we encounter a new integer-feasible solution.
 
-lazy_model = build_tsp_model(d, n)
+# !!! tip
+#     We use Gurobi for this model because HiGHS does not support lazy
+#     constraints. For more information on callbacks, read the page
+#     [Solver-independent callbacks](@ref callbacks_manual).
+
+# As before, we construct the same first-stage subproblem:
+
+lazy_model = build_tsp_model(d, n, optimizer)
 function subtour_elimination_callback(cb_data)
     status = callback_node_status(cb_data, lazy_model)
     if status != MOI.CALLBACK_NODE_STATUS_INTEGER
@@ -266,10 +286,10 @@ set_attribute(
     MOI.LazyConstraintCallback(),
     subtour_elimination_callback,
 )
+if !HAS_GUROBI                                                        #hide
+    set_attribute(lazy_model, MOI.LazyConstraintCallback(), nothing)  #hide
+end                                                                   #hide
 optimize!(lazy_model)
-
-#-
-
 @assert is_solved_and_feasible(lazy_model)
 objective_value(lazy_model)
 
@@ -283,4 +303,7 @@ plot_tour(X, Y, value.(lazy_model[:x]))
 
 # The solution time is faster than the iterative approach:
 
+if !HAS_GUROBI       #hide
+    time_lazy = 0.0  #hide
+end                  #hide
 Test.@test time_lazy < time_iterated

docs/src/tutorials/linear/callbacks.md

@@ -0,0 +1,8 @@
+```@meta
+EditURL = "callbacks.jl"
+```
+
+# [Callbacks](@id callbacks_tutorial)
+
+This page is a placeholder that appears only if the documentation is built from
+a fork.

docs/src/tutorials/linear/multiple_solutions.jl

@@ -33,8 +33,17 @@
 
 using JuMP
 import Gurobi
+import HiGHS        #hide
 import Test
 
+HAS_GUROBI = try    #hide
+    Gurobi.Env(Dict{String,Any}("output_flag" => 0))  #hide
+    true            #hide
+catch               #hide
+    false           #hide
+end                 #hide
+nothing             #hide
+
 # !!! warning
 #     This tutorial uses [Gurobi.jl](@ref) as the solver because it supports
 #     returning multiple feasible solutions, something that open-source MIP
@@ -69,7 +78,11 @@ import Test
 # number:
 
 n = 4
-model = Model()
+optimizer = Gurobi.Optimizer
+if !HAS_GUROBI                    #hide
+    optimizer = HiGHS.Optimizer   #hide
+end                               #hide
+model = Model(optimizer)
 set_silent(model)
 @variable(model, 0 <= x_digits[row in 1:n, col in 1:n] <= 9, Int, Symmetric)
 
@@ -91,7 +104,6 @@ x_digits_upper = [x_digits[i, j] for j in 1:n for i in 1:j]
 
 # If we optimize this model, we find that Gurobi has returned one solution:
 
-set_optimizer(model, Gurobi.Optimizer)
 optimize!(model)
 Test.@test is_solved_and_feasible(model)
 Test.@test result_count(model) == 1
@@ -104,7 +116,10 @@ solution_summary(model)
 # need to reset the optimizer. If you turn the solution pool options on before
 # the first solve you do not need to reset the optimizer.
 
-set_optimizer(model, Gurobi.Optimizer)
+set_optimizer(model, optimizer)
+if !HAS_GUROBI                           #hide
+    MOI.Utilities.drop_optimizer(model)  #hide
+end                                      #hide
 
 # The first option turns on the exhaustive search mode for multiple solutions:
 
@@ -119,13 +134,19 @@ set_attribute(model, "PoolSolutions", 100)
 
 # We can then call `optimize!` and view the results.
 
+if !HAS_GUROBI                       #hide
+    set_optimizer(model, optimizer)  #hide
+end                                  #hide
 optimize!(model)
 Test.@test is_solved_and_feasible(model)
 solution_summary(model)
 
 # Now Gurobi has found 20 solutions:
 
-Test.@test result_count(model) == 20
+if HAS_GUROBI                             #hide
+    Test.@test result_count(model) == 20  #hide
+end                                       #hide
+result_count(model)
 
 # ## Viewing the Results
 

src/callbacks.jl

@@ -16,15 +16,10 @@ primal solution available from [`callback_value`](@ref) is integer feasible.
 
 ## Example
 
-```jldoctest; filter=r"CALLBACK_NODE_STATUS_.+"
+```julia
 julia> import Gurobi
 
 julia> model = Model(Gurobi.Optimizer);
-Set parameter WLSAccessID
-Set parameter WLSSecret
-Set parameter LicenseID to value 722777
-Set parameter GURO_PAR_SPECIAL
-WLS license 722777 - registered to JuMP Development
 
 julia> set_silent(model)
 
@@ -73,15 +68,10 @@ Use [`callback_node_status`](@ref) to check whether a solution is available.
 
 ## Example
 
-```jldoctest
+```julia
 julia> import Gurobi
 
 julia> model = Model(Gurobi.Optimizer);
-Set parameter WLSAccessID
-Set parameter WLSSecret
-Set parameter LicenseID to value 722777
-Set parameter GURO_PAR_SPECIAL
-WLS license 722777 - registered to JuMP Development
 
 julia> set_silent(model)