def __init__(self, model, **kwargs):
super(ClassifyTrainerPyTorch, self).__init__()
self.clip = float(kwargs.get('clip', 5))
self.labels = model.labels
self.optimizer = OptimizerManager(model, **kwargs)
self.crit = model.create_loss().cuda()
self.model = torch.nn.DataParallel(model).cuda()
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
def __init__(self, model, **kwargs):
super(LanguageModelTrainerPyTorch, self).__init__()
self.model = model
self.clip = float(kwargs.get('clip', 5))
self.gpu = not bool(kwargs.get('nogpu', False))
self.crit = model.create_loss()
if self.gpu:
self.model = self.model.cuda()
self.crit.cuda()
self.nsteps = kwargs.get('nsteps', 500)
self.optimizer = OptimizerManager(self.model, **kwargs)
def __init__(self, model, **kwargs):
super(Seq2SeqTrainerPyTorch, self).__init__()
self.gpu = bool(kwargs.get('gpu', True))
self.clip = float(kwargs.get('clip', 5))
self.model = model
self.optimizer = OptimizerManager(self.model, **kwargs)
self._input = model.make_input
self._predict = model.predict
self.crit = model.create_loss()
self.tgt_rlut = kwargs['tgt_rlut']
if self.gpu:
self.model = torch.nn.DataParallel(model).cuda()
self.crit.cuda()
self.nsteps = kwargs.get('nsteps', 500)
def __init__(self, model, **kwargs):
super(TaggerTrainerPyTorch, self).__init__()
self.gpu = not bool(kwargs.get('nogpu', False))
# By default support IOB1/IOB2
self.span_type = kwargs.get('span_type', 'iob')
self.verbose = kwargs.get('verbose', False)
logger.info('Setting span type %s', self.span_type)
self.model = model
self.idx2label = revlut(self.model.labels)
self.clip = float(kwargs.get('clip', 5))
self.optimizer = OptimizerManager(self.model, **kwargs)
if self.gpu:
self.model = model.to_gpu()
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
def __init__(self, model, **kwargs):
super(ClassifyTrainerPyTorch, self).__init__()
self.clip = float(kwargs.get('clip', 5))
self.labels = model.labels
self.gpus = int(kwargs.get('gpus', 1))
if self.gpus == -1:
self.gpus = len(
os.getenv('CUDA_VISIBLE_DEVICES', os.getenv('NV_GPU',
'0')).split(','))
self.optimizer = OptimizerManager(model, **kwargs)
self.model = model
if self.gpus > 0:
self.crit = model.create_loss().cuda()
if self.gpus > 1:
self.model = torch.nn.DataParallel(model).cuda()
else:
self.model.cuda()
else:
logger.warning("Requested training on CPU. This will be slow.")
self.crit = model.create_loss()
self.model = model
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)
def __init__(self, model, **kwargs):
super(TaggerTrainerPyTorch, self).__init__()
self.gpus = int(kwargs.get('gpus', 1))
# By default support IOB1/IOB2
self.span_type = kwargs.get('span_type', 'iob')
self.verbose = kwargs.get('verbose', False)
logger.info('Setting span type %s', self.span_type)
self.model = model
self.idx2label = revlut(self.model.labels)
self.clip = float(kwargs.get('clip', 5))
self.optimizer = OptimizerManager(self.model, **kwargs)
if self.gpus > 1:
logger.info(
"Trainer for PyTorch tagger currently doesnt support multiple GPUs. Setting to 1"
)
self.gpus = 1
if self.gpus > 0:
self.model = model.to_gpu()
else:
logger.warning("Requested training on CPU. This will be slow.")
self.nsteps = kwargs.get('nsteps', six.MAXSIZE)