diff --git a/GPy/core/sparse_gp_mpi.py b/GPy/core/sparse_gp_mpi.py
index e9bd770d..e8779f51 100644
--- a/GPy/core/sparse_gp_mpi.py
+++ b/GPy/core/sparse_gp_mpi.py
@@ -47,7 +47,7 @@ class SparseGP_MPI(SparseGP):
         
         if variational_prior is not None:
             self.link_parameter(variational_prior)
-
+            
         self.mpi_comm = mpi_comm
         # Manage the data (Y) division
         if mpi_comm != None:
@@ -60,7 +60,6 @@ class SparseGP_MPI(SparseGP):
             mpi_comm.Bcast(self.param_array, root=0)
         self.update_model(True)
 
-
     def __getstate__(self):
         dc = super(SparseGP_MPI, self).__getstate__()
         dc['mpi_comm'] = None
diff --git a/GPy/models/bayesian_gplvm.py b/GPy/models/bayesian_gplvm.py
index d363fb7a..fca97e96 100644
--- a/GPy/models/bayesian_gplvm.py
+++ b/GPy/models/bayesian_gplvm.py
@@ -25,8 +25,6 @@ class BayesianGPLVM(SparseGP_MPI):
                  Z=None, kernel=None, inference_method=None, likelihood=None,
                  name='bayesian gplvm', mpi_comm=None, normalizer=None,
                  missing_data=False, stochastic=False, batchsize=1):
-        self.mpi_comm = mpi_comm
-        self.__IN_OPTIMIZATION__ = False
 
         self.logger = logging.getLogger(self.__class__.__name__)
         if X is None:
diff --git a/GPy/models/sparse_gp_regression.py b/GPy/models/sparse_gp_regression.py
index 5a56bb7d..49c3914c 100644
--- a/GPy/models/sparse_gp_regression.py
+++ b/GPy/models/sparse_gp_regression.py
@@ -9,6 +9,7 @@ from .. import likelihoods
 from .. import kern
 from ..inference.latent_function_inference import VarDTC
 from ..core.parameterization.variational import NormalPosterior
+from GPy.inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
 
 class SparseGPRegression(SparseGP_MPI):
     """
@@ -30,7 +31,7 @@ class SparseGPRegression(SparseGP_MPI):
 
     """
 
-    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None):
+    def __init__(self, X, Y, kernel=None, Z=None, num_inducing=10, X_variance=None, normalizer=None, mpi_comm=None):
         num_data, input_dim = X.shape
 
         # kern defaults to rbf (plus white for stability)
@@ -48,8 +49,14 @@ class SparseGPRegression(SparseGP_MPI):
 
         if not (X_variance is None):
             X = NormalPosterior(X,X_variance)
+            
+        if mpi_comm is not None:
+            from ..inference.latent_function_inference.var_dtc_parallel import VarDTC_minibatch
+            infr = VarDTC_minibatch(mpi_comm=mpi_comm)
+        else:
+            infr = VarDTC()
 
-        SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=VarDTC(), normalizer=normalizer)
+        SparseGP_MPI.__init__(self, X, Y, Z, kernel, likelihood, inference_method=infr, normalizer=normalizer, mpi_comm=mpi_comm)
 
     def parameters_changed(self):
         from ..inference.latent_function_inference.var_dtc_parallel import update_gradients_sparsegp,VarDTC_minibatch
diff --git a/GPy/testing/mpi_tests.py b/GPy/testing/mpi_tests.py
index 4848a6ec..45777eb1 100644
--- a/GPy/testing/mpi_tests.py
+++ b/GPy/testing/mpi_tests.py
@@ -21,6 +21,7 @@ comm = MPI.COMM_WORLD
 N = 100
 x = np.linspace(-6., 6., N)
 y = np.sin(x) + np.random.randn(N) * 0.05
+comm.Bcast(y)
 data = np.vstack([x,y])
 infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
 m = GPy.models.BayesianGPLVM(data.T,1,mpi_comm=comm)
@@ -39,9 +40,43 @@ if comm.rank==0:
             (stdout, stderr) = p.communicate()
             L1 =  float(stdout.splitlines()[-2])
             L2 =  float(stdout.splitlines()[-1])
-            self.assertAlmostEqual(L1, L2)
+            self.assertTrue(np.allclose(L1,L2))
             import os
             os.remove('mpi_test__.py')
+            
+        def test_SparseGPRegression_MPI(self):
+            code = """
+import numpy as np
+import GPy
+from mpi4py import MPI
+np.random.seed(123456)
+comm = MPI.COMM_WORLD
+N = 100
+x = np.linspace(-6., 6., N)
+y = np.sin(x) + np.random.randn(N) * 0.05
+comm.Bcast(y)
+data = np.vstack([x,y])
+#infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
+m = GPy.models.SparseGPRegression(data[:1].T,data[1:2].T,mpi_comm=comm)
+m.optimize(max_iters=10)
+if comm.rank==0:
+    print float(m.objective_function())
+    m.inference_method.mpi_comm=None
+    m.mpi_comm=None
+    m._trigger_params_changed()
+    print float(m.objective_function())
+            """
+            with open('mpi_test__.py','w') as f:
+                f.write(code)
+                f.close()
+            p = subprocess.Popen('mpirun -n 4 python mpi_test__.py',stdout=subprocess.PIPE,shell=True)
+            (stdout, stderr) = p.communicate()
+            L1 =  float(stdout.splitlines()[-2])
+            L2 =  float(stdout.splitlines()[-1])
+            self.assertTrue(np.allclose(L1,L2))
+            import os
+            os.remove('mpi_test__.py')
+
 
 except:
     pass