网上解决方法(TransVG接口不长这样): device = torch.device('cuda:1') 如果是别的代码,可能把1改成0就行,看自己显卡卡号。 解决办法(举了个选两张卡的例子): CUDA_VISIBLE_DEVICES=0,3 # 表示选择0,3这2张显卡 python -m torch.distributed.launch --nproc_per_node=2 # 每个卡只能跑一个进程,所以...
RuntimeError: cuda runtime error (10) : invalid device ordinal at xxx 图1错误日志 原因分析 可以从以下角度排查: 请检查CUDA_VISIBLE_DEVICES设置的值是否与作业规格匹配。例如您选择4卡规格的作业,实际可用的卡ID为0、1、2、3,但是您在进行cuda相关的运算时,例如"tensor.to(device="cuda:7")",将张量...
设置可用的多个 gpu: os.environ['CUDA_VISIBLE_DEVICES'] ='4, 5' 3. 设置 device_ids model = torch.nn.DataParallel(model, device_ids=[4, 5]).cuda() 然后就可以顺利使用多个 gpu 来跑模型啦,如果不按照上述几步来做,会报以下错误: AssertionError: Invalid device id 如何kill 掉 vscode 仍然在...
#include "device_launch_parameters.h" #include <iostream> int main() { int deviceCount; cudaGetDeviceCount(&deviceCount); for(int i=0;i<deviceCount;i++) { cudaDeviceProp devProp; cudaGetDeviceProperties(&devProp, i); std::cout << "使用GPU device " << i << ": " << devProp.name ...
CUDA_VISIBLE_DEVICES这个环境变量可以影响CUDA能识别到的GPU,并影响它映射到的cuda设备编号。 首先我们知道使用nvidia-smi命令可以查询本机GPU的相关信息,如下所示。 $ nvidia-smi Sun May 12 22:13:43 2024 +---+ | NVIDIA-SMI 535.161.07 Driver Version: 535.161.07 CUDA Version: 12.2 | |---+---+...
To determine the device ID for the available hardware in your system, you can run NVIDIA’s deviceQuery executable included in the CUDA SDK. 什么意思呢?就是说可以通过CUDA_VISIBLE_DEVICES 环境变量来限制CUDA程序所能使用的GPU设备。CUDA应用运行时,CUDA将遍历当前可见的设备,并从零开始为可见设备编号。
use device id under CUDA_VISIBLE_DEVICES for get_device_capability e34748a youkaichao mentioned this pull request Jul 8, 2024 [Bug]: Using vllm as the inference engine, there is an incorrect recognition of GPU computing capabilities for different types. #6213 Open youkaichao requested a...
Device id that represents an invalid device #define cudaIpcMemLazyEnablePeerAccess 0x01 Automatically enable peer access between remote devices as needed #define cudaMemAttachGlobal 0x01 Memory can be accessed by any stream on any device #define cudaMemAttachHost 0x02 Memory cannot be acc...
RuntimeError: cuda runtime error (101) : invalid device ordinal at /opt/conda/conda-bld/pytorch_1595629427478/work/torch/csrc/cuda/Module.cpp:59 sorry. you should use --gpus=2,3 instead of CUDA_VISIBLE_DEVICES=2,3 with --gpus 2 ...
2. Internal: failed call to cuDevicePrimaryCtxRetain: CUDA_ERROR_INVALID_DEVICE 解决办法:指定运行GPU设备号 代码语言:javascript 复制 # x为gpu device id:0,1,2,3importos os.environ["CUDA_VISIBLE_DEVICES"]="x" 3. (interrupted by signal 11: SIGSEGV) ...