diff --git a/python-package/xgboost/dask.py b/python-package/xgboost/dask.py
index 75f8c10bbffb..1db49804546e 100644
--- a/python-package/xgboost/dask.py
+++ b/python-package/xgboost/dask.py
@@ -1009,10 +1009,8 @@ def _infer_predict_output(
     test_sample = rng.randn(1, features)
     if inplace:
         # clear the state to avoid gpu_id, gpu_predictor
-        booster_config = booster.save_config()
-        booster.set_param({"predictor": "cpu_predictor", "gpu_id": -1})
+        booster = Booster(model_file=booster.save_raw())
         test_predt = booster.inplace_predict(test_sample, **kwargs)
-        booster.load_config(booster_config)
     else:
         m = DMatrix(test_sample)
         test_predt = booster.predict(m, **kwargs)
diff --git a/src/learner.cc b/src/learner.cc
index 5d062b32e8dd..cb0618295d93 100644
--- a/src/learner.cc
+++ b/src/learner.cc
@@ -354,7 +354,6 @@ class LearnerConfiguration : public Learner {
 
   void LoadConfig(Json const& in) override {
     CHECK(IsA<Object>(in));
-    std::lock_guard<std::mutex> guard(config_lock_);
     Version::Load(in);
 
     auto const& learner_parameters = get<Object>(in["learner"]);
@@ -420,7 +419,6 @@ class LearnerConfiguration : public Learner {
   }
 
   void SetParam(const std::string& key, const std::string& value) override {
-    std::lock_guard<std::mutex> guard(config_lock_);
     this->need_configuration_ = true;
     if (key == kEvalMetric) {
       if (std::find(metric_names_.cbegin(), metric_names_.cend(),