CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 # 表示选择0-7这8张显卡,根据自己的服务器选择卡号 python -m torch.distributed.launch --nproc_per_node=8 # 每个节点上跑的任务数 --use_env eval.py --batch_size 32 --num_workers 4 --bert_enc_num 12 --detr_enc_num 6 --backbone resnet50 -...
在使用os.environ['CUDA_VISIBLE_DEVICES']动态设置可见的 GPU 时,通常只能在程序开始运行之前进行设置,并且这种设置在程序运行后无法在一个进程中动态改变。这是因为深度学习框架(如 TensorFlow、PyTorch 等)在初始化时就会读取CUDA_VISIBLE_DEVICES环境变量,并在后续操作中使用这些设置。因此,尝试在运行时通过循环动态...
cuda_visible_devices设置cuda 0 cuda怎么配置 最近又捡起了CUDA,因为需要做一个任务,将某程序使用CUDA并行计算加快速度,希望能尽量达到实时性。CUDA的安装没有什么好讲,就是一路Next就可以。现在CUDA只有一个安装包,并且集成了Nsight,特别方便,不像CUDA 4,居然要安装3个包,烦死。 安装过后就开始对具体工程进行配置...
CUDA_VISIBLE_DEVICES=0,1,2,3,4 python3 -m vllm.entrypoints.openai.api_server --served-model-name Qwen1.5-72B-Chat --model /data/models/Qwen1.5-72B-Chat --host 0.0.0.0 --port 8089 出现问题: torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 768.00 MiB. GPU 0 has ...
To Reproduce Steps to reproduce the behavior: $ export CUDA_VISIBLE_DEVICES=0,1,6,7 $ python ./deepy.py ./train.py ./configs/125M.yml ./configs/local_setup.yml [2023-03-08 12:00:27,863] [INFO] [launch.py:82:main] WORLD INFO DICT: {'local...
device_string="" while read element; do nl=${element:0:1} trim_nl=$[nl - 1] device_string+="$trim_nl," done <<< "${cuda_array[*]}" IFS=$OLD_IFS export CUDA_VISIBLE_DEVICES="${device_string%,}" echo "set cuda devices to ${device_string%,}" echo $CUDA_VISIBLE_DEVICES }...
CUDA_VISIBLE_DEVICES=0,2,3Devices 0, 2, 3 will be visible; device 1 is masked CUDA will enumerate the visible devices starting at zero. In the last case, devices 0, 2, 3 will appear as devices 0, 1, 2. If you change the order of the string to “2,3,0”, devices 2,3,0 ...
一般项目的编译项目都在Tomcat的webapps下，项目的访问路径一般为：http://localhost:8080/项目虚拟路径。
CUDA_VISIBLE_DEVICES=1CUDA_HOME=/usr/local/cuda-8.0PATH="$PATH:/usr/local/cuda-8.0/bin"LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64:/usr/local/cuda-8.0/extras/CUPTI/lib64"#.bashrc文件 at /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2#define CUDNN_MAJOR...
Refer to the compatibility documentation for the supported hardware matrix or ensure that only supported hardware is visible during initialization via the CUDA_VISIBLE_DEVICES environment variable. cudaErrorMpsConnectionFailed = 805 This error indicates that the MPS client failed to connect to the MPS...