Ada-Boundary: Accelerating DNN Training via Adaptive Boundary Batch Selection

Publication
Song, H., Kim, S., Kim, M., and Lee, J., "Ada-Boundary: Accelerating DNN Training via Adaptive Boundary Batch Selection," Machine Learning (ECML-PKDD Journal Track), Sep. 2020.

1. Requirement

• Python 3
• tensorflow-gpu 2.1.0
• tensorpack libracy //use "pip install tensorpack"

2. Description

• We provide the training/evaluation of all compared algorithms in the paper.
• Please do not change the structure of directories:
  • Folder src provides all the code for evaluation with compared methods.
  • Folder src/dataset contains benchmark datasets (FMNIST, CIFAR-10). Due to the lack of space, the other data will be uploaded soon. Moreover, .bin format is used for the synthetic data because they can be loaded at once in main memory.

3. Datasets and Weight Sharing

• Datasets and Models (for weight sharing) can be downloaded in https://bit.ly/2YhrTsR
• Please locate the two folder (dataset and init_weight ) in the src folder.

4. Tutorial for Evaluation.

• Training Configuration

   # All the hyperparameters of baseline methods were set to the same value described in our paper.
   # Source code provides a tutorial to train DensNet or WideResNet using a simple command.

• Necessary Parameters

   - 'gpu_id': GPU number which you want to use (only support single gpu).
   - 'data_name': {MNIST, CIFAR-10}. # others will be supported later
   - 'model_name': {DenseNet-25-12, WideResNet16-8}
   - 'method_name': {Ada-Hard, Ada-Uniform, Ada-Boundary}.
   - 'optimizer': {sgd, momentum}
   - 'weight_sharing': {true, false} # if true, all the method share the same parameters 
                                     # until 10 epoch (see Section 5.1 for details)
   - 'log_dir': log directory to save (1) mini-batch loss/error, (2) training loss/error,
                and (3) test loss/error.

• Running Command

   python main.py 'gpu_id' 'data_name' 'model_name' 'method_name' 'optimizer' 'weight_sharing' 'log_dir'
   
   # e.g., train on Fashion-MNIST using Ada-Boundary with weight sharing and sgd.
   # python main.py '0' 'MNIST' 'DenseNet-25-12' 'Ada-Boundary' 'sgd' 'true' 'log'

• Detail of Log File

   # convergence_log.csv
   # format: epoch, elapsed time, lr, mini-batch loss, mini-batch error, trainng loss, 
   #         training error, test loss, test error