diff --git a/miplearn/components/warmstart.py b/miplearn/components/warmstart.py
index 25dc8b9..55b111e 100644
--- a/miplearn/components/warmstart.py
+++ b/miplearn/components/warmstart.py
@@ -14,6 +14,7 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import cross_val_score
 from sklearn.neighbors import KNeighborsClassifier
 from tqdm.auto import tqdm
+import pyomo.environ as pe
 import logging
 logger = logging.getLogger(__name__)
 
@@ -140,6 +141,12 @@ class WarmStartComponent(Component):
         self.is_warm_start_available = False
     
     def before_solve(self, solver, instance, model):
+        # Solve linear relaxation
+        lr_solver = pe.SolverFactory("gurobi")
+        lr_solver.options["threads"] = 4
+        lr_solver.options["relax_integrality"] = 1
+        lr_solver.solve(model, tee=solver.tee)
+        
         # Build x_test
         x_test = CombinedExtractor([UserFeaturesExtractor(),
                                     SolutionExtractor(),
diff --git a/miplearn/solvers.py b/miplearn/solvers.py
index 8ec7ca0..5c42699 100644
--- a/miplearn/solvers.py
+++ b/miplearn/solvers.py
@@ -37,6 +37,7 @@ class LearningSolver:
         self.threads = threads
         self.time_limit = time_limit
         self.gap_limit = gap_limit
+        self.tee = False
         
         if self.components is not None:
             assert isinstance(self.components, dict)
@@ -63,12 +64,7 @@ class LearningSolver:
         
     def solve(self, instance, tee=False):
         model = instance.to_model()
-        
-#         # Solve linear relaxation (TODO: use solver provided by user)
-#         lr_solver = pe.SolverFactory("gurobi")
-#         lr_solver.options["threads"] = 4
-#         lr_solver.options["relax_integrality"] = 1
-#         lr_solver.solve(model)
+        self.tee = tee
 
         self._create_solver()
         if self.is_persistent: