Removed notes about using --yes USE_AVX_INSTRUCTIONS when building python

interface since AVX availability is now detected automatically by cmake.

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
 

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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