diff --git a/tutorial/python/1-Flat.py b/tutorial/python/1-Flat.py
index 7099e8db6..6e90dc3f1 100644
--- a/tutorial/python/1-Flat.py
+++ b/tutorial/python/1-Flat.py
@@ -4,7 +4,9 @@
 # This source code is licensed under the BSD+Patents license found in the
 # LICENSE file in the root directory of this source tree.
 
+from __future__ import print_function
 import numpy as np
+
 d = 64                           # dimension
 nb = 100000                      # database size
 nq = 10000                       # nb of queries
@@ -16,16 +18,14 @@ xq[:, 0] += np.arange(nq) / 1000.
 
 import faiss                   # make faiss available
 index = faiss.IndexFlatL2(d)   # build the index
-print index.is_trained
+print(index.is_trained)
 index.add(xb)                  # add vectors to the index
-print index.ntotal
+print(index.ntotal)
 
 k = 4                          # we want to see 4 nearest neighbors
 D, I = index.search(xb[:5], k) # sanity check
-print I
-print D
+print(I)
+print(D)
 D, I = index.search(xq, k)     # actual search
-print I[:5]                    # neighbors of the 5 first queries
-print I[-5:]                   # neighbors of the 5 last queries
-
-
+print(I[:5])                   # neighbors of the 5 first queries
+print(I[-5:])                  # neighbors of the 5 last queries
diff --git a/tutorial/python/2-IVFFlat.py b/tutorial/python/2-IVFFlat.py
index b47188483..feed5defa 100644
--- a/tutorial/python/2-IVFFlat.py
+++ b/tutorial/python/2-IVFFlat.py
@@ -4,7 +4,9 @@
 # This source code is licensed under the BSD+Patents license found in the
 # LICENSE file in the root directory of this source tree.
 
+from __future__ import print_function
 import numpy as np
+
 d = 64                           # dimension
 nb = 100000                      # database size
 nq = 10000                       # nb of queries
@@ -20,15 +22,15 @@ nlist = 100
 k = 4
 quantizer = faiss.IndexFlatL2(d)  # the other index
 index = faiss.IndexIVFFlat(quantizer, d, nlist, faiss.METRIC_L2)
-       # here we specify METRIC_L2, by default it performs inner-product search
+# here we specify METRIC_L2, by default it performs inner-product search
+
 assert not index.is_trained
 index.train(xb)
 assert index.is_trained
 
 index.add(xb)                  # add may be a bit slower as well
 D, I = index.search(xq, k)     # actual search
-print I[-5:]                   # neighbors of the 5 last queries
+print(I[-5:])                  # neighbors of the 5 last queries
 index.nprobe = 10              # default nprobe is 1, try a few more
 D, I = index.search(xq, k)
-print I[-5:]                   # neighbors of the 5 last queries
-
+print(I[-5:])                  # neighbors of the 5 last queries
diff --git a/tutorial/python/3-IVFPQ.py b/tutorial/python/3-IVFPQ.py
index 78a115806..636f20286 100644
--- a/tutorial/python/3-IVFPQ.py
+++ b/tutorial/python/3-IVFPQ.py
@@ -4,7 +4,9 @@
 # This source code is licensed under the BSD+Patents license found in the
 # LICENSE file in the root directory of this source tree.
 
+from __future__ import print_function
 import numpy as np
+
 d = 64                           # dimension
 nb = 100000                      # database size
 nq = 10000                       # nb of queries
@@ -25,10 +27,8 @@ index = faiss.IndexIVFPQ(quantizer, d, nlist, m, 8)
 index.train(xb)
 index.add(xb)
 D, I = index.search(xb[:5], k) # sanity check
-print I
-print D
+print(I)
+print(D)
 index.nprobe = 10              # make comparable with experiment above
 D, I = index.search(xq, k)     # search
-print I[-5:]
-
-
+print(I[-5:])
diff --git a/tutorial/python/4-GPU.py b/tutorial/python/4-GPU.py
new file mode 100644
index 000000000..25e94c0df
--- /dev/null
+++ b/tutorial/python/4-GPU.py
@@ -0,0 +1,38 @@
+# Copyright (c) 2015-present, Facebook, Inc.
+# All rights reserved.
+#
+# This source code is licensed under the BSD+Patents license found in the
+# LICENSE file in the root directory of this source tree.
+
+from __future__ import print_function
+import numpy as np
+
+d = 64                           # dimension
+nb = 100000                      # database size
+nq = 10000                       # nb of queries
+np.random.seed(1234)             # make reproducible
+xb = np.random.random((nb, d)).astype('float32')
+xb[:, 0] += np.arange(nb) / 1000.
+xq = np.random.random((nq, d)).astype('float32')
+xq[:, 0] += np.arange(nq) / 1000.
+
+import faiss                   # make faiss available
+
+print("number of GPUs:", faiss.get_num_gpus())
+
+index = faiss.IndexFlatL2(d)   # build the index
+
+res = faiss.StandardGpuResources()
+
+index = faiss.index_cpu_to_gpu(res, 0, index)
+
+index.add(xb)                  # add vectors to the index
+print(index.ntotal)
+
+k = 4                          # we want to see 4 nearest neighbors
+D, I = index.search(xb[:5], k) # sanity check
+print(I)
+print(D)
+D, I = index.search(xq, k)     # actual search
+print(I[:5])                   # neighbors of the 5 first queries
+print(I[-5:])                  # neighbors of the 5 last queries