chrishkchris edited a comment on issue #651: [WIP ]Simply example APIs URL: https://github.com/apache/singa/pull/651#issuecomment-609049842 > I suggest to merge the examples under `examples/autograd` into the follow structure. > > ``` > 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 > ``` > > The pseudo code of each file: > > ```python > # train.py > def run(max_epoch, worker_id, 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(worker_id, 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 worker_id in range(0, gpu_per_node): > process.append(multiprocessing.Process(target=run, args=(worker_id, ... nccl_id))) > > for p in process: > p.start() > ``` thanks! I am working on it. There may be three issues that need to think about: (i) maybe difficult to include graph module (please see resnet_module.py to see the different between layer model and module model). In this case, we may consider (ii) don't know how to show this example in the doc dist-train.md. So maybe in the doc we can still use doc_dist_train.py for examples
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