Before revision:  Second, the optimal configuration of hyper-parameters for one DL framework (e.g., TensorFlow) often does not work well for another DL framework (e.g., Caffe or Torch) under the same CPU-GPU runtime environment .
After revision:  Second, to the best of our knowledge, this study is the first to identify the opportunities for improving the training time performance and the accuracy of DL frameworks by configuring parallel computing libraries and tuning individual and multiple hyper-parameters for one DL framework (e.g., TensorFlow) often does not work well for another DL framework (e.g., Caffe or Torch) under the same CPU-GPU runtime environment .