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.current_device()[source] 返回当前选定设备的索引。 torch.cuda.current_stream(device=None)[source] 返回给定设备当前选定的流。 参数 device(torch.deviceorint, optional) – 选定的设备。返回当前设备当前选择的流,如果设备为None(默认),则由current_device()给出。 torch.cuda.default_stream(devi...
set_device(device) 用途:设置当前默认的 GPU 设备索引。 torch.cuda.get_device_name(device=None) 用途:返回给定设备的名称。 torch.cuda.get_device_properties(device) 用途:返回给定设备的属性,包括最大共享内存、最大线程数等。 torch.cuda.memory_allocated(device=None) 用途:返回给定设备上已分配的内存...
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...
🐛 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-...
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 (...
returntorch._C._cuda_memoryStats(device) Expand DownExpand Up@@ -303,7 +305,7 @@ def memory_allocated(device: Union[Device, int] = None) -> int: needs to be created on GPU. See :ref:`cuda-memory-management` for more details about GPU memory management. ...
SUCCESS: raise CudaError(res) [docs]class device(object): r"""Context-manager that changes the selected device. Arguments: device (torch.device or int): device index to select. It's a no-op if this argument is a negative integer or ``None``. """ def __init__(self, device): ...
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...
这个错误是在执行 PyTorch CUDA 相关操作时出现的,比如在进行模型训练或推理时。错误中的 replica 0 on device 通常出现在使用分布式训练时,表示在第一个副本(replica)上,位于指定的 GPU 设备上发生了显存溢出。 2. 分析内存溢出原因 显存溢出可能由以下原因造成: ...