While going out of memory may necessitate reducing batch size, one can do certain checks to ensure that memory usage is optimal. Tracking Memory Usage with GPUtil One way to track GPU usage is by monitoring memory usage in a console with the nvidia-smi command. The problem with this approach...
_, _, kH, kW = self.weight.size() self.mask.fill_(1) self.mask[:, :, kH //2, kW //2+ (mask_type =='B'):] =0self.mask[:, :, kH //2+1:] =0defforward(self, x): self.weight.data *= self.maskreturnsuper(MaskedConv2d, self).forward(x) ...
While going out of memory may necessitate reducing batch size, one can do certain checks to ensure that memory usage is optimal. Tracking Memory Usage with GPUtil One way to track GPU usage is by monitoring memory usage in a console with the nvidia-smi command. The problem with this approach...
train_loss=test_model(model,train_dataloader) val_acc,val_loss=test_model(model,val_dataloader) #Check memory usage. handle = nvidia_smi.nvmlDeviceGetHandleByIndex(0) info = nvidia_smi.nvmlDevice
model.eval() will notify all your layers that you are in eval mode, that way, batchnorm or dropout layers will work in eval model instead of training mode. torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up computations but you won’...
torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, sampler=None, batch_sampler=None, num_workers=0, collate_fn=None, pin_memory=False, drop_last=False, timeout=0, worker_init_fn=None, multiprocessing_context=None) 功能:构建可迭代的数据装载器 ...
(sum of fwd, bwd and step latency) world size: 1 data parallel size: 1 model parallel size: 1 batch size per GPU: 80 params per gpu: 336.23 M params of model = params per GPU * mp_size: 336.23 M fwd MACs per GPU: 3139.93 G fwd flops per GPU: 6279.86 G fwd flops of model ...
Changing batch size from 256 to 4096 indeed changed both the memory usage (now, it is changing around ~1.8gb to ~2.8gb but still not constant, and that was around 160mb when it was on cpu) and the time (around 3.4s, constant throughout different epochs). However, the time it takes...
model outputs.size: torch.Size([16, 3]) CUDA_VISIBLE_DEVICES :0,1 device_count :2 下面的代码是根据 GPU 剩余内存来排序。 def get_gpu_memory(): import platform if 'Windows' != platform.system(): import os os.system('nvidia-smi-q -d Memory | grep -A4 GPU | grep Free > tmp.txt...
importtorch# hyperparameters which you can changebatch_size=1024h0=1536h1=2048h2=3072h3=4096# some variables associated with recordingma,mma,mr,mmr=0,0,0,0ma_gap=0num_bytes_fp32,num_bytes_long=4,8# tensor sizeINPUT_BYTES=batch_size*h0*num_bytes_fp32A1_BYTES=batch_size*h1*num_bytes_...