diff --git a/bayesnet/network/Network.cc b/bayesnet/network/Network.cc
index 0458d3a..b399782 100644
--- a/bayesnet/network/Network.cc
+++ b/bayesnet/network/Network.cc
@@ -190,8 +190,11 @@ namespace bayesnet {
         const double n_samples = static_cast<double>(samples.size(1));
         auto worker = [&](std::pair<const std::string, std::unique_ptr<Node>>& node, int i) {
             std::string threadName = "FitWorker-" + std::to_string(i);
+#if defined(__linux__)
             pthread_setname_np(pthread_self(), threadName.c_str());
-            semaphore.acquire();
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             double numStates = static_cast<double>(node.second->getNumStates());
             double smoothing_factor = 0.0;
             switch (smoothing) {
@@ -212,6 +215,7 @@ namespace bayesnet {
             };
         int i = 0;
         for (auto& node : nodes) {
+            semaphore.acquire();
             threads.emplace_back(worker, std::ref(node), i++);
         }
         for (auto& thread : threads) {
@@ -236,8 +240,11 @@ namespace bayesnet {
         result = torch::zeros({ samples.size(1), classNumStates }, torch::kFloat64);
         auto worker = [&](const torch::Tensor& sample, int i) {
             std::string threadName = "PredictWorker-" + std::to_string(i);
+#if defined(__linux__)
             pthread_setname_np(pthread_self(), threadName.c_str());
-            semaphore.acquire();
+#else
+            pthread_setname_np(threadName.c_str());
+#endif
             auto psample = predict_sample(sample);
             auto temp = torch::tensor(psample, torch::kFloat64);
             {
@@ -247,6 +254,7 @@ namespace bayesnet {
             semaphore.release();
             };
         for (int i = 0; i < samples.size(1); ++i) {
+            semaphore.acquire();
             const torch::Tensor sample = samples.index({ "...", i });
             threads.emplace_back(worker, sample, i);
         }