[GitHub] [singa] nudles commented on issue #651: [WIP ]Simply example APIs

[GitBox <gi...@apache.org>]

nudles commented on issue #651: [WIP ]Simply example APIs
URL: https://github.com/apache/singa/pull/651#issuecomment-609045570
 
 
   I suggest to merge the examples under `examples/autograd` into the following scheme
   
   ```
   autograd
       train.py 
       train_mpi.py
       train_multiprocess.py
       data   # define the data loading and preprocessing
           cifar10.py
           mnist.py
       model   # define the model
           cnn.py
           resnet.py
           xception.py
   ```
   
   ```python
   # train.py
   def run(max_epoch, rank, num_workers, model, data, sgd):
       if model == 'resnet':
          from model import resnet
            model = resnet.create_model()
       elif model == 'cnn':
            model = cnn.create_model()    
       ....
       if data == 'cifar10':
           from data import cifar10:
              train_x, train_y, val_x, val_y = cifar10.load()
       elif data == 'mnist':
            ....
       
       train_x, train_y, val_x, val_y = partition(rank, num_workers, train_x, train_y, val_x, val_y)
      
       # bp and sgd
   
   if __name__ == '__main__':
      # use argparse to get command config: max_epoch, model, data, etc. for single gpu training
       sgd = # create sgd
       run(0, 1, ..., sgd)
   
   # train_mpi.py
   if __name__ == '__main__':
      # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
       sgd = # create sgd
       dist_sgd = DistOpt(sgd...)
       run(dist_sgd.rank, dist_sgd.world_size, ... dist_sgd)
   
   # train_multiprocess.py
   def run(rank, num_gpu, ...):
       sgd = ...
       dist_sgd = DistOpt(sgd)
       train.run(rank, num_gpu, ... dist_sgd)
   
   if __name__ == '__main__':
      # use argparse to get command args: max_epoch, model, data, etc. for multi-gpu training
       nccl_id = ...
       process = []
       for gpu_num in range(0, gpu_per_node):        
           process.append(multiprocessing.Process(target=run, args=(gpu_num, ... nccl_id))) 
   
       for p in process:
           p.start()
   ```

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