diff --git a/README.md b/README.md
index d9103d757..349f6070a 100644
--- a/README.md
+++ b/README.md
@@ -38,15 +38,6 @@ Before you can run the Python example programs you must compile dlib. Type:
 python setup.py install
 ```
 
-or type
-
-```bash
-python setup.py install --yes USE_AVX_INSTRUCTIONS
-```
-
-if you have a CPU that supports AVX instructions, since this makes some things run faster.  
-
-
 
 ## Running the unit test suite
 
diff --git a/python_examples/cnn_face_detector.py b/python_examples/cnn_face_detector.py
index b7037dab5..5748a7530 100755
--- a/python_examples/cnn_face_detector.py
+++ b/python_examples/cnn_face_detector.py
@@ -23,10 +23,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS --yes DLIB_USE_CUDA
-#   if you have a CPU that supports AVX instructions, you have an Nvidia GPU
-#   and you have CUDA installed since this makes things run *much* faster.
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/correlation_tracker.py b/python_examples/correlation_tracker.py
index 8d902cc77..39a7f6ad8 100755
--- a/python_examples/correlation_tracker.py
+++ b/python_examples/correlation_tracker.py
@@ -22,10 +22,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_alignment.py b/python_examples/face_alignment.py
index 8de2043d0..a1bf1f2df 100755
--- a/python_examples/face_alignment.py
+++ b/python_examples/face_alignment.py
@@ -10,11 +10,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
-#   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_clustering.py b/python_examples/face_clustering.py
index f4769b11e..1f249c5b8 100755
--- a/python_examples/face_clustering.py
+++ b/python_examples/face_clustering.py
@@ -17,11 +17,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
-#   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_detector.py b/python_examples/face_detector.py
index 0d48fb822..f27ef67df 100755
--- a/python_examples/face_detector.py
+++ b/python_examples/face_detector.py
@@ -27,10 +27,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_jitter.py b/python_examples/face_jitter.py
index a729e7dda..c01cd8f14 100755
--- a/python_examples/face_jitter.py
+++ b/python_examples/face_jitter.py
@@ -14,11 +14,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
-#   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_landmark_detection.py b/python_examples/face_landmark_detection.py
index e7bda3412..3a108656e 100755
--- a/python_examples/face_landmark_detection.py
+++ b/python_examples/face_landmark_detection.py
@@ -35,10 +35,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/face_recognition.py b/python_examples/face_recognition.py
index c7f5437a7..5ddec74ad 100755
--- a/python_examples/face_recognition.py
+++ b/python_examples/face_recognition.py
@@ -29,11 +29,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  This code will also use CUDA if you have CUDA and cuDNN
-#   installed.
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/find_candidate_object_locations.py b/python_examples/find_candidate_object_locations.py
index aee46e356..5febb0805 100755
--- a/python_examples/find_candidate_object_locations.py
+++ b/python_examples/find_candidate_object_locations.py
@@ -21,10 +21,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/max_cost_assignment.py b/python_examples/max_cost_assignment.py
index 8e284e6c8..8c18fbaef 100755
--- a/python_examples/max_cost_assignment.py
+++ b/python_examples/max_cost_assignment.py
@@ -12,10 +12,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/opencv_webcam_face_detection.py b/python_examples/opencv_webcam_face_detection.py
index be0e322b8..ebd6f8dee 100755
--- a/python_examples/opencv_webcam_face_detection.py
+++ b/python_examples/opencv_webcam_face_detection.py
@@ -25,10 +25,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
@@ -56,4 +52,4 @@ while True:
     cv2.imshow('my webcam', img)
     if cv2.waitKey(1) == 27:
         break  # esc to quit
-cv2.destroyAllWindows()
\ No newline at end of file
+cv2.destroyAllWindows()
diff --git a/python_examples/sequence_segmenter.py b/python_examples/sequence_segmenter.py
index 335e475f7..aca28db86 100755
--- a/python_examples/sequence_segmenter.py
+++ b/python_examples/sequence_segmenter.py
@@ -17,10 +17,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/svm_binary_classifier.py b/python_examples/svm_binary_classifier.py
index d114c815a..0941662d1 100755
--- a/python_examples/svm_binary_classifier.py
+++ b/python_examples/svm_binary_classifier.py
@@ -14,10 +14,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/svm_rank.py b/python_examples/svm_rank.py
index dad642274..01c555f0c 100755
--- a/python_examples/svm_rank.py
+++ b/python_examples/svm_rank.py
@@ -21,10 +21,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/svm_struct.py b/python_examples/svm_struct.py
index 7f0004ccd..6b658b9d6 100755
--- a/python_examples/svm_struct.py
+++ b/python_examples/svm_struct.py
@@ -21,10 +21,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/train_object_detector.py b/python_examples/train_object_detector.py
index c9f324fa6..570a08cca 100755
--- a/python_examples/train_object_detector.py
+++ b/python_examples/train_object_detector.py
@@ -15,10 +15,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the
diff --git a/python_examples/train_shape_predictor.py b/python_examples/train_shape_predictor.py
index e07f8299a..822a901db 100755
--- a/python_examples/train_shape_predictor.py
+++ b/python_examples/train_shape_predictor.py
@@ -23,10 +23,6 @@
 #   Alternatively, if you want to compile dlib yourself then go into the dlib
 #   root folder and run:
 #       python setup.py install
-#   or
-#       python setup.py install --yes USE_AVX_INSTRUCTIONS
-#   if you have a CPU that supports AVX instructions, since this makes some
-#   things run faster.  
 #
 #   Compiling dlib should work on any operating system so long as you have
 #   CMake installed.  On Ubuntu, this can be done easily by running the