export CUDA_HOME="$CUDA_HOME:/usr/local/cuda" 通过echo $CUDA_HOME命令查看变量时发现开头多了一个冒号,:/usr/local/cuda:/usr/local/cuda这表示有一个空路径被追加到环境变量中了。空路径会导致问题,例如在查找 CUDA 工具时可能引发错误或行为异常。 修改成: export CUDA_HOME="/usr/local/cuda:$CUDA_...
2. conda 环境配置cudnn,下载以及更改指向 报错解决:RuntimeError:The detected CUDA version mismatches the version that was used to compile PyTorch.blog.csdn.net/weixin_43603658/article/details/130737155 3. 系统级的cuda路径更改指向 检查环境变量没有 echo $CUDA_HOME echo $PATH which nvcc 检查路径...
确保CUDA_HOME指向正确的CUDA安装目录,并且PATH环境变量包含了CUDA的bin目录。 如果发现环境变量配置有误(如路径前有多余的冒号或错误的路径),请进行修正。例如,如果你的.zshrc或.bashrc文件中CUDA_HOME配置错误,可以修改为: bash export CUDA_HOME="/usr/local/cuda" 修改后,不要忘记运行source ~/.zshrc或source...
in <module> import flash_attn_2_cuda as flash_attn_gpu ModuleNotFoundError: No module named 'flash_attn_2_cuda' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/home/ayes/IdeaProjects/Iona/.venv/lib/python3.12/site-packages/...
path.join(torch_dir, "include", "ATen", "CUDAGeneratorImpl.h")): 107 107 generator_flag = ["-DOLD_GENERATOR_PATH"] 108 108 109 - check_if_cuda_home_none("flash_attn") 109 + check_if_cuda_home_none(PACKAGE_NAME) 110 110 # Check, if CUDA11 is installed for compute ...
设置必要的 CUDA 环境变量 包含了常见的 GPU 架构支持 利用你的128核心进行并行编译 # 清理之前的安装 proxychains4 pip uninstall -y flash-attn # 获取 CUDA 路径 CUDA_PATH=$(dirname $(dirname $(which nvcc))) # 使用 proxychains4 安装 CUDA_HOME=$CUDA_PATH \ ...
报错信息: ImportError: /home/operationgpt/anaconda3/envs/lyj_py10_torch230/lib/python3.10/site-packages/flash_attn_2_cuda.cpython-310-x8...
Reminder I have read the README and searched the existing issues. System Info OS:wsl2 cuda-12.3 最新llamafactory,docker compose Reproduction 命令行: llamafactory-cli train --stage sft --do_train True --model_name_or_path /home/xx/.cache/model...
I found I was unable to import flash_attn_cuda after running python setup.py install. --- details --- I run python setup.py install with a prefix pointing to the root dir of flash-attention. I set PYTHONPATH=$PWD also with the absolute p...
32 + "CUDA_HOME": lambda: os.environ.get("CUDA_HOME", None), 40 33 # local rank of the process in the distributed setting, used to determine 41 34 # the GPU device id 42 - "LOCAL_RANK": 43 - lambda: int(os.environ.get("LOCAL_RANK", "0")), ...