diff --git a/include/flucoma/algorithms/public/KMeans.hpp b/include/flucoma/algorithms/public/KMeans.hpp
index 3302e888..ed58a93f 100644
--- a/include/flucoma/algorithms/public/KMeans.hpp
+++ b/include/flucoma/algorithms/public/KMeans.hpp
@@ -232,6 +232,7 @@ class KMeans
   index size() const { return mMeans.rows(); }
   index getK() const { return mMeans.rows(); }
   index nAssigned() const { return mAssignments.size(); }
+  index nEmpty() const { return std::count(mEmpty.begin(), mEmpty.end(), true); }
 
   void getAssignments(FluidTensorView<index, 1> out) const
   {
@@ -295,7 +296,6 @@ class KMeans
       }
       if (kAssignment.size() == 0)
       {
-        std::cout << "Warning: empty cluster" << std::endl;
         mEmpty[asUnsigned(k)] = true;
         return;
       }
diff --git a/include/flucoma/clients/nrt/KMeansClient.hpp b/include/flucoma/clients/nrt/KMeansClient.hpp
index 45a2906a..1ffab321 100644
--- a/include/flucoma/clients/nrt/KMeansClient.hpp
+++ b/include/flucoma/clients/nrt/KMeansClient.hpp
@@ -85,11 +85,9 @@ class KMeansClient : public FluidBaseClient,
     if (dataSet.size() == 0) return Error<IndexVector>(EmptyDataSet);
     if (k <= 1) return Error<IndexVector>(SmallK);
     if(mTracker.changed(k)) mAlgorithm.clear();
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
-    return getCounts(assignments, k);
+    auto [result, _] =
+        train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());
+    return result; 
   }
 
   MessageResult<IndexVector> fitPredict(InputDataSetClientRef  datasetClient,
@@ -106,13 +104,11 @@ class KMeansClient : public FluidBaseClient,
     if (k <= 1) return Error<IndexVector>(SmallK);
     if (maxIter <= 0) maxIter = 100;
     if(mTracker.changed(k)) mAlgorithm.clear();
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
+    auto [result, assignments] =
+        train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());
     StringVectorView ids = dataSet.getIds();
     labelsetClientPtr->setLabelSet(getLabels(ids, assignments));
-    return getCounts(assignments, k);
+    return result;   
   }
 
   MessageResult<IndexVector> predict(InputDataSetClientRef  datasetClient,
@@ -175,12 +171,10 @@ class KMeansClient : public FluidBaseClient,
     if (dataSet.size() == 0) return Error<IndexVector>(EmptyDataSet);
     if (k <= 1) return Error<IndexVector>(SmallK);
     if (maxIter <= 0) maxIter = 100;
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
+    auto [result, _] =
+        train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());
     transform(srcClient, dstClient);
-    return getCounts(assignments, k);
+    return result; 
   }
 
   MessageResult<index> predictPoint(InputBufferPtr data) const
@@ -263,6 +257,25 @@ class KMeansClient : public FluidBaseClient,
 
 
 private:
+  using DataSet = FluidDataSet<std::string, double, 1>;
+
+  std::pair<MessageResult<IndexVector>, IndexVector>
+  train(DataSet const& dataSet, index k, index maxIter , index initMethod, index randomSeed)
+  {
+    mAlgorithm.train(
+        dataSet, k, maxIter,
+        static_cast<algorithm::KMeans::InitMethod>(initMethod), randomSeed);
+    IndexVector assignments(dataSet.size());
+    mAlgorithm.getAssignments(assignments);
+    auto training_result = MessageResult<IndexVector>(getCounts(assignments,k)); 
+    if(mAlgorithm.nEmpty() > 0)
+    {
+      training_result.set(Result::Status::kWarning); 
+      training_result.addMessage("There were empty clusters; perhaps numClusters is too high."); 
+    }
+    return {training_result, assignments}; 
+  }
+
   IndexVector getCounts(IndexVector assignments, index k) const
   {
     IndexVector counts(k);
diff --git a/include/flucoma/clients/nrt/SKMeansClient.hpp b/include/flucoma/clients/nrt/SKMeansClient.hpp
index f6b2f41c..afd776d2 100644
--- a/include/flucoma/clients/nrt/SKMeansClient.hpp
+++ b/include/flucoma/clients/nrt/SKMeansClient.hpp
@@ -84,11 +84,8 @@ class SKMeansClient : public FluidBaseClient,
     if (dataSet.size() == 0) return Error<IndexVector>(EmptyDataSet);
     if (k <= 1) return Error<IndexVector>(SmallK);
     if(mTracker.changed(k)) mAlgorithm.clear();
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
-    return getCounts(assignments, k);
+    auto [result, _] = train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());
+    return result;
   }
 
 
@@ -106,13 +103,10 @@ class SKMeansClient : public FluidBaseClient,
     if (k <= 1) return Error<IndexVector>(SmallK);
     if (maxIter <= 0) maxIter = 100;
     if(mTracker.changed(k)) mAlgorithm.clear();
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
+auto [result, assignments] = train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());
     StringVectorView ids = dataSet.getIds();
     labelsetClientPtr->setLabelSet(getLabels(ids, assignments));
-    return getCounts(assignments, k);
+    return result;
   }
 
 
@@ -178,12 +172,9 @@ class SKMeansClient : public FluidBaseClient,
     if (k <= 1) return Error<IndexVector>(SmallK);
     if (maxIter <= 0) maxIter = 100;
     if(mTracker.changed(k)) mAlgorithm.clear();
-    mAlgorithm.train(dataSet, k, maxIter, static_cast<InitMethod>(get<kInit>()),
-                     get<kRandomSeed>());
-    IndexVector assignments(dataSet.size());
-    mAlgorithm.getAssignments(assignments);
+auto [result, _] = train(dataSet, k, maxIter, get<kInit>(), get<kRandomSeed>());    
     encode(srcClient, dstClient);
-    return getCounts(assignments, k);
+    return result;
   }
 
   MessageResult<index> predictPoint(BufferPtr data) const
@@ -266,6 +257,25 @@ class SKMeansClient : public FluidBaseClient,
 
 
 private:
+  using DataSet = FluidDataSet<std::string, double, 1>;
+
+  std::pair<MessageResult<IndexVector>, IndexVector>
+  train(DataSet const& dataSet, index k, index maxIter , index initMethod, index randomSeed)
+  {
+    mAlgorithm.train(
+        dataSet, k, maxIter,
+        static_cast<algorithm::KMeans::InitMethod>(initMethod), randomSeed);
+    IndexVector assignments(dataSet.size());
+    mAlgorithm.getAssignments(assignments);
+    auto training_result = MessageResult<IndexVector>(getCounts(assignments,k)); 
+    if(mAlgorithm.nEmpty() > 0)
+    {
+      training_result.set(Result::Status::kWarning); 
+      training_result.addMessage("There were empty clusters; perhaps numClusters is too high."); 
+    }
+    return {training_result, assignments}; 
+  }
+
   IndexVector getCounts(IndexVector assignments, index k) const
   {
     IndexVector counts(k);