def __init__(self, **kwargs):
super().__init__(**kwargs)
torch.set_num_threads(int(os.environ.get("OMP_NUM_THREADS", 1)))
torch.backends.cudnn.benchmark = True
self.avg_loss = AvgMeter(50)
self.dtype = kwargs.get("dtype", None) # just for test for now
def __init__(self, **kwargs):
super().__init__(**kwargs)
hvd.init()
torch.set_num_threads(int(os.environ.get("OMP_NUM_THREADS", 1)))
torch.cuda.set_device(hvd.local_rank())
torch.backends.cudnn.benchmark = True
self.avg_loss = AvgMeter(50)
def __init__(self, **kwargs):
super().__init__(**kwargs)
torch.set_num_threads(int(os.environ.get("OMP_NUM_THREADS", 1)))
torch.backends.cudnn.benchmark = True
self.avg_loss = AvgMeter(50)
self.dtype = kwargs.get("dtype", None) # just for test for now
self.model_name_temp = get_model_name()
print("Models and tensorboard events files are saved to:\n ", self.model_name_temp)
def __init__(self, **kwargs):
super().__init__(**kwargs)
# torch.set_num_threads(int(os.environ.get("OMP_NUM_THREADS", 1)))
if torch.cuda.is_available():
torch.cuda.set_device("cuda:0")
else:
torch.cuda.set_device("cpu")
torch.backends.cudnn.benchmark = True
self.avg_loss = AvgMeter(50)
self.dtype = kwargs.get("dtype", None) # just for test for now
self.writer = SummaryWriter()