diff --git a/GPy/core/sparse_gp_mpi.py b/GPy/core/sparse_gp_mpi.py
index cecbe667..e7faf7a8 100644
--- a/GPy/core/sparse_gp_mpi.py
+++ b/GPy/core/sparse_gp_mpi.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 from sparse_gp import SparseGP
+from numpy.linalg.linalg import LinAlgError
 from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
 
 import logging
@@ -83,11 +84,7 @@ class SparseGP_MPI(SparseGP):
         if self.mpi_comm != None:
             if self._IN_OPTIMIZATION_ and self.mpi_comm.rank==0:
                 self.mpi_comm.Bcast(np.int32(1),root=0)
-            self.mpi_comm.Bcast(p, root=0)
-            
-        from ..util.debug import checkFinite
-        checkFinite(p, 'optimizer_array')
-        
+            self.mpi_comm.Bcast(p, root=0)        
         SparseGP.optimizer_array.fset(self,p)
         
     def optimize(self, optimizer=None, start=None, **kwargs):
@@ -103,7 +100,13 @@ class SparseGP_MPI(SparseGP):
             while True:
                 self.mpi_comm.Bcast(flag,root=0)
                 if flag==1:
-                    self.optimizer_array = x
+                    try:
+                        self.optimizer_array = x
+                        self._fail_count = 0
+                    except (LinAlgError, ZeroDivisionError, ValueError):
+                        if self._fail_count >= self._allowed_failures:
+                            raise
+                        self._fail_count += 1
                 elif flag==-1:
                     break
                 else:
diff --git a/GPy/inference/latent_function_inference/var_dtc_parallel.py b/GPy/inference/latent_function_inference/var_dtc_parallel.py
index 53b31dab..b9ecbb5c 100644
--- a/GPy/inference/latent_function_inference/var_dtc_parallel.py
+++ b/GPy/inference/latent_function_inference/var_dtc_parallel.py
@@ -167,18 +167,12 @@ class VarDTC_minibatch(LatentFunctionInference):
         # Compute Common Components
         #======================================================================
         
-        from ...util.debug import checkFullRank
-
         Kmm = kern.K(Z).copy()
         diag.add(Kmm, self.const_jitter)
-        r1 = checkFullRank(Kmm,name='Kmm')
         KmmInv,Lm,LmInv,_ = pdinv(Kmm)
         
         LmInvPsi2LmInvT = LmInv.dot(psi2_full).dot(LmInv.T)
         Lambda = np.eye(Kmm.shape[0])+LmInvPsi2LmInvT
-        r2 = checkFullRank(Lambda,name='Lambda')
-#         if (not r1) or (not r2):
-#             raise
         LInv,LL,LLInv,logdet_L = pdinv(Lambda)
         b = LLInv.dot(LmInv.dot(psi1Y_full.T))
         bbt = np.square(b).sum()
@@ -203,7 +197,6 @@ class VarDTC_minibatch(LatentFunctionInference):
         # Compute dL_dKmm
         #======================================================================
         
-#         dL_dKmm =  -(output_dim*np.einsum('md,od->mo',KmmInvPsi2LLInvT,KmmInvPsi2LLInvT) + vvt)/2.
         dL_dKmm =  dL_dpsi2R - output_dim*KmmInv.dot(psi2_full).dot(KmmInv)/2.
 
         #======================================================================
@@ -336,13 +329,7 @@ def update_gradients(model, mpi_comm=None):
         Y = model.Y_local
         X = model.X[model.N_range[0]:model.N_range[1]]
 
-    try:
-        model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
-    except Exception:
-        if model.mpi_comm is None or model.mpi_comm.rank==0:
-            import time
-            model.pickle('model_'+str(int(time.time()))+'.pickle')
-        raise
+    model._log_marginal_likelihood, dL_dKmm, model.posterior = model.inference_method.inference_likelihood(model.kern, X, model.Z, model.likelihood, Y)
     
     het_noise = model.likelihood.variance.size > 1