parameters: (parser)
return_statement: return train
docstring: parser : argparse.ArgumentParser
      return a parser added with args required by fit
docstring_summary: parser : argparse.ArgumentParser
      return a parser added with args required by fit
docstring_tokens: parser : argparse . ArgumentParser return a parser added with args required by fit
function: def add_fit_args(parser):
      """
      parser : argparse.ArgumentParser
      return a parser added with args required by fit
      """
      train = parser.add_argument_group('Training', 'model training')
      train.add_argument('--network', type=str,
                         help='the neural network to use')
      train.add_argument('--num-layers', type=int,
                         help='number of layers in the neural network, \
                               required by some networks such as resnet')
      train.add_argument('--gpus', type=str,
                         help='list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu')
      train.add_argument('--kv-store', type=str, default='device',
                         help='key-value store type')
      train.add_argument('--num-epochs', type=int, default=100,
                         help='max num of epochs')
      train.add_argument('--lr', type=float, default=0.1,
                         help='initial learning rate')
      train.add_argument('--lr-factor', type=float, default=0.1,
                         help='the ratio to reduce lr on each step')
      train.add_argument('--lr-step-epochs', type=str,
                         help='the epochs to reduce the lr, e.g. 30,60')
      train.add_argument('--initializer', type=str, default='default',
                         help='the initializer type')
      train.add_argument('--optimizer', type=str, default='sgd',
                         help='the optimizer type')
      train.add_argument('--mom', type=float, default=0.9,
                         help='momentum for sgd')
      train.add_argument('--wd', type=float, default=0.0001,
                         help='weight decay for sgd')
      train.add_argument('--batch-size', type=int, default=128,
                         help='the batch size')
      train.add_argument('--disp-batches', type=int, default=20,
                         help='show progress for every n batches')
      train.add_argument('--model-prefix', type=str,
                         help='model prefix')
      parser.add_argument('--monitor', dest='monitor', type=int, default=0,
                          help='log network parameters every N iters if larger than 0')
      train.add_argument('--load-epoch', type=int,
                         help='load the model on an epoch using the model-load-prefix')
      train.add_argument('--top-k', type=int, default=0,
                         help='report the top-k accuracy. 0 means no report.')
      train.add_argument('--loss', type=str, default='',
                         help='show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss')
      train.add_argument('--test-io', type=int, default=0,
                         help='1 means test reading speed without training')
      train.add_argument('--dtype', type=str, default='float32',
                         help='precision: float32 or float16')
      train.add_argument('--gc-type', type=str, default='none',
                         help='type of gradient compression to use, \
                               takes `2bit` or `none` for now')
      train.add_argument('--gc-threshold', type=float, default=0.5,
                         help='threshold for 2bit gradient compression')
      # additional parameters for large batch sgd
      train.add_argument('--macrobatch-size', type=int, default=0,
                         help='distributed effective batch size')
      train.add_argument('--warmup-epochs', type=int, default=5,
                         help='the epochs to ramp-up lr to scaled large-batch value')
      train.add_argument('--warmup-strategy', type=str, default='linear',
                         help='the ramping-up strategy for large batch sgd')
      return train
function_tokens: def add_fit_args ( parser ) : train = parser . add_argument_group ( 'Training' , 'model training' ) train . add_argument ( '--network' , type = str , help = 'the neural network to use' ) train . add_argument ( '--num-layers' , type = int , help = 'number of layers in the neural network, \
                               required by some networks such as resnet' ) train . add_argument ( '--gpus' , type = str , help = 'list of gpus to run, e.g. 0 or 0,2,5. empty means using cpu' ) train . add_argument ( '--kv-store' , type = str , default = 'device' , help = 'key-value store type' ) train . add_argument ( '--num-epochs' , type = int , default = 100 , help = 'max num of epochs' ) train . add_argument ( '--lr' , type = float , default = 0.1 , help = 'initial learning rate' ) train . add_argument ( '--lr-factor' , type = float , default = 0.1 , help = 'the ratio to reduce lr on each step' ) train . add_argument ( '--lr-step-epochs' , type = str , help = 'the epochs to reduce the lr, e.g. 30,60' ) train . add_argument ( '--initializer' , type = str , default = 'default' , help = 'the initializer type' ) train . add_argument ( '--optimizer' , type = str , default = 'sgd' , help = 'the optimizer type' ) train . add_argument ( '--mom' , type = float , default = 0.9 , help = 'momentum for sgd' ) train . add_argument ( '--wd' , type = float , default = 0.0001 , help = 'weight decay for sgd' ) train . add_argument ( '--batch-size' , type = int , default = 128 , help = 'the batch size' ) train . add_argument ( '--disp-batches' , type = int , default = 20 , help = 'show progress for every n batches' ) train . add_argument ( '--model-prefix' , type = str , help = 'model prefix' ) parser . add_argument ( '--monitor' , dest = 'monitor' , type = int , default = 0 , help = 'log network parameters every N iters if larger than 0' ) train . add_argument ( '--load-epoch' , type = int , help = 'load the model on an epoch using the model-load-prefix' ) train . add_argument ( '--top-k' , type = int , default = 0 , help = 'report the top-k accuracy. 0 means no report.' ) train . add_argument ( '--loss' , type = str , default = '' , help = 'show the cross-entropy or nll loss. ce strands for cross-entropy, nll-loss stands for likelihood loss' ) train . add_argument ( '--test-io' , type = int , default = 0 , help = '1 means test reading speed without training' ) train . add_argument ( '--dtype' , type = str , default = 'float32' , help = 'precision: float32 or float16' ) train . add_argument ( '--gc-type' , type = str , default = 'none' , help = 'type of gradient compression to use, \
                               takes `2bit` or `none` for now' ) train . add_argument ( '--gc-threshold' , type = float , default = 0.5 , help = 'threshold for 2bit gradient compression' ) # additional parameters for large batch sgd train . add_argument ( '--macrobatch-size' , type = int , default = 0 , help = 'distributed effective batch size' ) train . add_argument ( '--warmup-epochs' , type = int , default = 5 , help = 'the epochs to ramp-up lr to scaled large-batch value' ) train . add_argument ( '--warmup-strategy' , type = str , default = 'linear' , help = 'the ramping-up strategy for large batch sgd' ) return train