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 ...
torch.cuda.memory_allocated(device=None)[source] 返回给定设备的张量占用的当前GPU内存(以字节为单位)。 参数 device(torch.deviceorint, optional) – 选定的设备。返回当前设备的统计信息,由current_device()给出,如果设备为None(缺省值)。 注意 这可能比nvidia-smi中显示的要少,因为缓存分配器可以保存一些未...
torch.cuda.memory_allocated(device=None) 用途:返回给定设备上已分配的内存总量。 torch.cuda.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...
torch.cuda.device_count() class torch.cuda.device_of(obj) torch.cuda.empty_cache() torch.cuda.get_device_capability(device=None) torch.cuda.get_device_name(device=None) torch.cuda.init() torch.cuda.ipc_collect() torch.cuda.is_available() torch.cuda.max_memory_allocated(device=None) torch...
torch.cuda.max_memory_allocated(device=None)[source] Returns the maximum GPU memory usage by tensors in bytes for a given device. Parameters: device (torch.device or int, optional)– selected device. Returns statistic for the current device, given by current_device(), if device is None (...
🐛 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-...
可以在cmd中输入nvidia-smi，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...
defmax_memory_allocated(device:Union[Device,int]=None)->int: Expand All@@ -325,7 +327,7 @@ def max_memory_allocated(device: Union[Device, int] = None) -> int: See :ref:`cuda-memory-management` for more details about GPU memory ...
使用torch.cuda.max_memory_allocated()函数可以查看程序运行期间最大已分配的显存量。这可以帮助你了解程序在高峰时的显存使用情况。 python if torch.cuda.is_available(): max_allocated_memory = torch.cuda.max_memory_allocated(device=None) print(f"Max allocated GPU memory: {max_allocated_memory / (102...
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 172.00 MiB (GPU 0; 14.76 GiB total capacity; 6.07 GiB already allocated; 120.75 MiB free; 6.25 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentatio...