diff --git a/src/julia/src/MIPLearn.jl b/src/julia/src/MIPLearn.jl
index 8603ed4..d0fe206 100644
--- a/src/julia/src/MIPLearn.jl
+++ b/src/julia/src/MIPLearn.jl
@@ -2,5 +2,108 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+__precompile__(false)
 module MIPLearn
+
+using JuMP
+using Gurobi
+using PyCall
+using MathOptInterface
+const MOI = MathOptInterface
+
+miplearn = pyimport("miplearn")
+Instance = miplearn.Instance
+LearningSolver = miplearn.LearningSolver
+InternalSolver = miplearn.solvers.internal.InternalSolver
+
+@pydef mutable struct JuMPSolver <: InternalSolver
+    function add_constraint(self, constraint)
+    end
+
+    function set_warm_start(self, solution)
+    end
+
+    function clear_warm_start(self)
+    end
+
+    function fix(self, solution)
+    end
+
+    function set_instance(self, instance, model)
+        self.instance = instance
+        self.model = model
+    end
+
+    function solve(self; tee=false)
+        JuMP.set_optimizer(self.model, Gurobi.Optimizer)
+        JuMP.optimize!(self.model)
+
+        primal_bound = JuMP.objective_value(self.model)
+        dual_bound = JuMP.objective_bound(self.model)
+
+        if JuMP.objective_sense(self.model) == MOI.MIN_SENSE
+            sense = "min"
+            lower_bound = dual_bound
+            upper_bound = primal_bound
+        else
+            sense = "max"
+            lower_bound = primal_bound
+            upper_bound = dual_bound
+        end
+
+        @show primal_bound, dual_bound
+
+        return Dict("Lower bound" => lower_bound,
+                    "Upper bound" => upper_bound,
+                    "Sense" => sense)
+    end
+
+    function solve_lp(self; tee=false)
+    end
+
+    function get_solution(self)
+        return Dict(JuMP.name(var) => JuMP.value(var)
+                    for var in JuMP.all_variables(self.model))
+    end
+
+    function set_gap_tolerance(self, gap_tolerance)
+    end
+
+    function set_node_limit(self)
+    end
+
+    function set_threads(self, threads)
+    end
+
+    function set_time_limit(self, time_limit)
+    end
+end
+
+@pydef mutable struct KnapsackInstance <: Instance
+    function __init__(self, weights, prices, capacity)
+        self.weights = weights
+        self.prices = prices
+        self.capacity = capacity
+    end
+
+    function to_model(self)
+        model = Model()
+        n = length(self.weights)
+        @variable(model, x[1:n], Bin)
+        @objective(model, Max, sum(x[i] * self.prices[i] for i in 1:n))
+        @constraint(model, sum(x[i] * self.weights[i] for i in 1:n) <= self.capacity)
+        return model
+    end
+
+    function get_instance_features(self)
+        return [0.]
+    end
+
+    function get_variable_features(self, var, index)
+        return [0.]
+    end
+end
+
+export JuMPSolver, KnapsackInstance
+
 end # module
diff --git a/src/julia/test/runtests.jl b/src/julia/test/runtests.jl
index cac36ff..2ac8082 100644
--- a/src/julia/test/runtests.jl
+++ b/src/julia/test/runtests.jl
@@ -2,136 +2,38 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-using MIPLearn
 using Test
-using JuMP
-using Gurobi
-using PyCall
-using MathOptInterface
-const MOI = MathOptInterface
-
-miplearn = pyimport("miplearn")
-
-Instance = miplearn.Instance
-LearningSolver = miplearn.LearningSolver
-InternalSolver = miplearn.solvers.internal.InternalSolver
-
-@pydef mutable struct JuMPSolver <: InternalSolver
-    function add_constraint(self, constraint)
-    end
-
-    function clear_warm_start(self)
-    end
-
-    function fix(self, solution)
-    end
-
-    function set_gap_tolerance(self, gap_tolerance)
-    end
-
-    function set_instance(self, instance, model)
-        self.instance = instance
-        self.model = model
-    end
-
-    function set_node_limit(self)
-    end
-
-    function set_threads(self, threads)
-    end
-
-    function set_time_limit(self, time_limit)
-    end
-
-    function set_warm_start(self, solution)
-    end
-
-    function solve(self; tee=false)
-        JuMP.set_optimizer(self.model, Gurobi.Optimizer)
-        JuMP.optimize!(self.model)
-
-        primal_bound = JuMP.objective_value(self.model)
-        dual_bound = JuMP.objective_bound(self.model)
-
-        if JuMP.objective_sense(self.model) == MOI.MIN_SENSE
-            sense = "min"
-            lower_bound = dual_bound
-            upper_bound = primal_bound
-        else
-            sense = "max"
-            lower_bound = primal_bound
-            upper_bound = dual_bound
-        end
-
-        @show primal_bound, dual_bound
-
-        return Dict("Lower bound" => lower_bound,
-                    "Upper bound" => upper_bound,
-                    "Sense" => sense)
-    end
-
-    function solve_lp(self; tee=false)
-    end
-
-    function get_solution(self)
-        return Dict(JuMP.name(var) => JuMP.value(var)
-                    for var in JuMP.all_variables(self.model))
-    end
-end
-
-@pydef mutable struct KnapsackInstance <: Instance
-    function __init__(self, weights, prices, capacity)
-        self.weights = weights
-        self.prices = prices
-        self.capacity = capacity
-    end
-
-    function to_model(self)
-        model = Model()
-        n = length(self.weights)
-        @variable(model, x[1:n], Bin)
-        @objective(model, Max, sum(x[i] * self.prices[i] for i in 1:n))
-        @constraint(model, sum(x[i] * self.weights[i] for i in 1:n) <= instance.capacity)
-        return model
-    end
-
-    function get_instance_features(self)
-        return [0.]
-    end
-
-    function get_variable_features(self, var, index)
-        @show var
-        return [0.]
-    end
-end
-
-instance = KnapsackInstance([23., 26., 20., 18.],
-                            [505., 352., 458., 220.],
-                            67.0)
-model = instance.to_model()
-
-solver = JuMPSolver()
-solver.set_instance(instance, model)
-stats = solver.solve()
-# assert len(stats["Log"]) > 100
-@test stats["Lower bound"] == 1183.0
-@test stats["Upper bound"] == 1183.0
-@test stats["Sense"] == "max"
-# @test isinstance(stats["Wallclock time"], float)
-# @test isinstance(stats["Nodes"], int)
-
-solution = solver.get_solution()
-@test solution["x[1]"] == 1.0
-@test solution["x[2]"] == 0.0
-@test solution["x[3]"] == 1.0
-@test solution["x[4]"] == 1.0
-
-# stats = solver.solve_lp()
-# @test round(stats["Optimal value"], 3) == 1287.923
-#
-# solution = solver.get_solution()
-# @test round(solution["x"][0], 3) == 1.000
-# @test round(solution["x"][1], 3) == 0.923
-# @test round(solution["x"][2], 3) == 1.000
-# @test round(solution["x"][3], 3) == 0.000
+using MIPLearn
 
+@testset "MIPLearn" begin
+    instance = KnapsackInstance([23., 26., 20., 18.],
+                                [505., 352., 458., 220.],
+                                67.0)
+    model = instance.to_model()
+
+    solver = JuMPSolver()
+    solver.set_instance(instance, model)
+    stats = solver.solve()
+
+    # assert len(stats["Log"]) > 100
+    @test stats["Lower bound"] == 1183.0
+    @test stats["Upper bound"] == 1183.0
+    @test stats["Sense"] == "max"
+    # @test isinstance(stats["Wallclock time"], float)
+    # @test isinstance(stats["Nodes"], int)
+
+    solution = solver.get_solution()
+    @test solution["x[1]"] == 1.0
+    @test solution["x[2]"] == 0.0
+    @test solution["x[3]"] == 1.0
+    @test solution["x[4]"] == 1.0
+
+    # stats = solver.solve_lp()
+    # @test round(stats["Optimal value"], 3) == 1287.923
+    #
+    # solution = solver.get_solution()
+    # @test round(solution["x"][0], 3) == 1.000
+    # @test round(solution["x"][1], 3) == 0.923
+    # @test round(solution["x"][2], 3) == 1.000
+    # @test round(solution["x"][3], 3) == 0.000
+end
\ No newline at end of file