Using optimized settings and tools to speed up your code!

Table of Contents

1. Channel First Imgae Data Format for CNN
2. Installing Optimized MXNet Binaries
3. Using MXNet Profiler

1 Config Channle First Image Data Format for CNN

Modify the ~/.keras.json and setting are:

   backend: mxnet
   image_data_format: channels_first

Channles_first is optimal for training on NVIDIA GPUs with cuDNN.

NOTE: It is recommended pratice to pass the input_shape based on the shape field of your input tensor (that means first non-input layer).

2 Install Optimized MXNet

   $ pip install mxnet-cu80mkl/mxnet-cu90mkl/mxnet-mkl

3 Using MXNet Profiler

MXNet (V0.9.1+) has a build-in profiler that gives detailed information about execution time at the symbol level. This feature complements general profiling tools like nvprof and gprof by summarizing at the operator level, instead of a function, kernel, or instruction level.

Speed Up!