def setup_args():
parser = train_args()
profile = parser.add_argument_group('Profiler Arguments')
profile.add_argument('--use-nvprof',
type='bool',
default=False,
help='If True, uses nvprof (might incur high overhead'
' and assumes that the whole process is running '
'inside nvprof), otherwise uses a custom '
'CPU-only profiler (with negligible overhead). '
'Default: False.')
profile.add_argument('--trace-path',
type=str,
default=None,
help='A path of the CUDA checkpoint. If specified, it'
' will be left unmodified after profiling '
'finishes, so it can be opened and inspected in '
'nvvp. Otherwise it will be created in a '
'temporary directory and removed after reading '
'the results.')
profile.add_argument('--debug',
type='bool',
default=False,
help='If true, enter debugger at end of run.')
return parser
def setup_args(parser=None):
if parser is None:
parser = ParlaiParser(True, True)
parser = train_args(parser)
profile = parser.add_argument_group('Profiler Arguments')
profile.add_argument('--torch', type='bool', default=False,
help='If true, use the torch profiler. Otherwise use cProfile.')
profile.add_argument('--torch-cuda', type='bool', default=False,
help='If true, use the torch cuda profiler. Otherwise use cProfile.')
profile.add_argument('--debug', type='bool', default=False,
help='If true, enter debugger at end of run.')
return parser
def setup_args():
parser = train_args()
profile = parser.add_argument_group('Profiler Arguments')
profile.add_argument('--torch',
type='bool',
default=False,
help='If true, use the torch profiler. Otherwise use '
'use cProfile.')
profile.add_argument('--debug',
type='bool',
default=False,
help='If true, enter debugger at end of run.')
return parser