这个错误通常表明CUDA kernel的配置参数不正确。 CUDA kernel配置参数错误可能由多种原因引起,包括但不限于: 线程块数量或线程数量过大:如果指定的线程块数量或每个线程块中的线程数量超过了GPU的硬件限制,就会触发此错误。 共享内存分配过多:每个线程块可以使用的共享内存量是有限的,如果请求的共享内存量超过了限制,...
一. 错误原因 : 1、多GPU测试 2、PyTorch版本与显卡不兼容 二. 问题解决 :将 torch.backends.cudnn.benchmark = True(该句一般出现在主函数的开头几句) 改为 torch.backends.cudnn.benchmark = False 【补充】…
[rank7]: self.param_groups_fp16_flat_cpu_memory.append(get_accelerator().pin_memory( [rank7]: File "/root/anaconda3/envs/internX/lib/python3.10/site-packages/deepspeed/accelerator/cuda_accelerator.py", line 292, in pin_memory [rank7]: return tensor.pin_memory() [rank7]: RuntimeError...
RuntimeError: [address=0.0.0.0:43266, pid=897] CUDA error: invalid argument CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable devi...
也就是GPU0的时候，那么这个参数带进来的Location信息于你的台式机不兼容，就会发生找不到cuda device的...
Describe the bug When trying to run train_dreambooth.py with --enable_xformers_memory_efficient_attention the process exits with this error: RuntimeError: CUDA error: invalid argument CUDA kernel errors might be asynchronously reported a...
1,RuntimeError: CUDA error: device-side assert triggered 使用pytorch的时候报这个错误说明你label中有些指不在[0, num classes), 区间左闭右开。比如类别数num_class=3, 你的label出现了-1或者3, 4, 5等!!! 2.RuntimeError:invalid argument 5:k not in range for dimension at /pytorch/ate ......
Description I’m using tensorrt to run a mask-rcnn model, and using pytorch to postprocess the result. when the inference result contains more than 2 bounding boxes, and I print the result, a GPU tensor, it raises an e…
while running above code we are faing an problem [08/14/2024-11:58:45] [TRT] [E] 1: [defaultAllocator.cpp::deallocate::42] Error Code 1: Cuda Runtime (invalid argument) Segmentation fault (core dumped) please provide solution for the same...
RuntimeError: CUDA error: invalid configuration argument CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Compile withTORCH_USE_CUDA_DSAto enable device-side assertions. ...