🐛 Bug The manpage https://pytorch.org/docs/stable/cuda.html#torch.cuda.memory_allocated advertises that it's possible to pass an int argument but it doesn't work. And even if I create a device argument it doesn't work correctly in multi-...
12、torch.cuda.max_memory_allocated(device=None)[SOURCE] Returns the maximum GPU memory occupied by tensors in bytes for a given device. By default, this returns the peak allocated memory since the beginning of this program.reset_max_memory_allocated()can be used to reset the starting point ...
例如,你可以使用以下代码来打印当前CUDA内存的使用情况: python import torch print(f"Allocated: {torch.cuda.memory_allocated(0) / 1024**2:.2f} MiB") print(f"Reserved: {torch.cuda.memory_reserved(0) / 1024**2:.2f} MiB") print(f"Max Allocated: {torch.cuda.max_memory_allocated(0) / ...
max_memory_allocated(device=None) 用途:返回给定设备上已分配的最大内存总量。 torch.cuda.memory_cached(device=None) 用途:返回给定设备上的缓存内存总量。 torch.cuda.max_memory_cached(device=None) 用途:返回给定设备上的最大缓存内存总量。 torch.cuda.empty_cache() 用途:释放缓存的内存,以便其他进程可以...
但是通常情况下直接在cmd中输入nvidia-smi是没有用的，那该怎么办呢 找路径 一般的路径为：C:\...
torch.cuda.is_available()[source] 返回一个bool,指示CUDA当前是否可用。 torch.cuda.max_memory_allocated(device=None)[source] 返回给定设备张量占用的最大GPU内存(以字节为单位)。默认情况下,这将返回自该程序开始以来分配的内存峰值。reset_max_memory_assigned()可用于重置跟踪此指标的起始点。例如,这两个函...
class torch.cuda.Event elapsed_time(end_event) ipc_handle() query() record(stream=None) synchronize() wait(stream=None) Memory management torch.cuda.empty_cache() torch.cuda.memory_allocated(device=None) torch.cuda.max_memory_allocated(device=None) ...
import torch 并检查是否可用的 cuda版本 python import torch torch.cuda.is_available() torch.__version__ 1. 2. 3. 4. CMD中: Anaconda Prompt 中: 二者是一致的。 此时:用pip install 方法安装了gpu版本的torch和torchvision,所以pip list 显示二者,但conda list 可能与之不一致,因为用的不是 conda in...
🚀 Feature Having multiple resettable torch.cuda.max_memory_allocated() counters Motivation With the help of torch.cuda's reset_max_memory_allocated and max_memory_allocated one can now measure peak memory usage. Which is very helpful. No...
就是从src中拷贝元素到self的tensor中,然后返回self。以gpu_tensor3 = cpu_tensor.copy_(gpu_tensor2)为例,就是把gpu中的gpu_tensor2拷贝到cpu中的cpu_tensor中。 2.直接在GPU上创建Tensor gpu_tensor1 = torch.tensor([[2,5,8],[1,4,7],[3,6,9]], device=torch.device("cuda:0")) ...