diff --git a/GPy/inference/SGD.py b/GPy/inference/SGD.py
index 4c7f6be2..13a325b0 100644
--- a/GPy/inference/SGD.py
+++ b/GPy/inference/SGD.py
@@ -4,8 +4,7 @@ import scipy.sparse
 from optimization import Optimizer
 from scipy import linalg, optimize
 import pylab as plt
-import copy
-import sys
+import copy, sys, pickle
 
 class opt_SGD(Optimizer):
     """
@@ -19,7 +18,7 @@ class opt_SGD(Optimizer):
 
     """
 
-    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, **kwargs):
+    def __init__(self, start, iterations = 10, learning_rate = 1e-4, momentum = 0.9, model = None, messages = False, batch_size = 1, self_paced = False, center = True, iteration_file = None, **kwargs):
         self.opt_name = "Stochastic Gradient Descent"
 
         self.model = model
@@ -33,6 +32,7 @@ class opt_SGD(Optimizer):
         self.self_paced = self_paced
         self.center = center
         self.param_traces = [('noise',[])]
+        self.iteration_file = iteration_file
         # if len([p for p in self.model.kern.parts if p.name == 'bias']) == 1:
         #     self.param_traces.append(('bias',[]))
         # if len([p for p in self.model.kern.parts if p.name == 'linear']) == 1:
@@ -271,8 +271,18 @@ class opt_SGD(Optimizer):
 
             # self.model.Youter = np.dot(Y, Y.T)
             self.trace.append(self.f_opt)
+            if self.iteration_file is not None:
+                f = open(self.iteration_file + "iteration%d.pickle" % it, 'w')
+                data = [self.x_opt, self.fopt_trace, self.param_traces]
+                pickle.dump(data, f)
+                f.close()
+
             if self.messages != 0:
                 sys.stdout.write('\r' + ' '*len(status)*2 + '  \r')
                 status = "SGD Iteration: {0: 3d}/{1: 3d}  f: {2: 2.3f}\n".format(it+1, self.iterations, self.f_opt)
                 sys.stdout.write(status)
                 sys.stdout.flush()
+
+
+
+