Command "/usr/bin/python3 -u -c "import setuptools, tokenize;__file__='/tmp/pip-build-vl4m588q/llvmlite/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\r\n', '\n');f.close();exec(compile(code, __file__, 'exec'))" install --record /tmp...
(myenv) aiscuser@node-0:~/vllm$ pip install --user -e . # This may take 5-10 minutes. Obtaining file:///home/aiscuser/vllm Installing build dependencies ... done Checking if build backend supports build_editable ... done Getting requirements to build editable ... done Preparing edita...
我安装numba的时候出现 failed with error code 1 in /tmp/pip-build-sjiz2hmu/llvmlite/ 解决办法 python -m pip install --upgrade --force pip 1. pip install setuptools==33.1.1 1. 参考:
Docker容器的本质就是通过容器虚拟技术虚拟出的一台主机，就像虚拟机一样。可以将应用及其运行环境部署在这台虚拟的主机上运行。但容器与虚拟机又有着本质的不同。在
在弹出“Failed to create menus”后,选择忽略,让程序继续安装,我们看到安装程序有个“show detail” 的按钮, 点击“忽略” 后弹出框会消失,安装程序会停留在如下界面,等待用户点击“下一步” ,此时我们不要点“下一步”, 而是点击“show detail”,看看报错的详细信息 ...
imageio-ffmpeg-0.4.7importlib-resources-5.4.0kiwisolver-1.3.1llvmlite-0.36.0matplotlib-3.3.4more-itertools-8.7.0 moviepy-1.0.3networkx-2.5.1numba-0.53.1numpy-1.19.5proglog-0.1.10pymatting-1.1.8requests-2.27.1 scikit-image-0.17.2scipy-1.5.4six-1.16.0soupsieve-2.3.2.post1 tifffile-2020.9.3...
> numba>=0.49->umap-learn>=0.5.0->bertopic) (58.0.4) > Requirement already satisfied: llvmlite<0.38,>=0.37.0rc1 in c:\users\martin kunth\anaconda3\lib\site-packages (from > numba>=0.49->umap-learn>=0.5.0->bertopic) (0.37.0) ...
2,可以import cupy,但是执行extern C的情况下出现cupy.cuda.compiler.CompileException: nvrtc: error: failed to open nvrtc-builtins64_111.dll. Make sure that nvrtc-builtins64_111.dll is installed correctly.的问题 开始以为是numba把cupy玩坏了,但是没有安装numba的条件下依然会存在这个问题,首先假设: ...
=0.6.0itsdangerous==1.1.0Jinja2==2.11.3joblib==1.0.1kiwisolver==1.3.1llvmlite==0.36.0lxml==4.6.2MarkupSafe==1.1.1matplotlib==3.3.4nltk==3.5numba==0.53.1numpy==1.20.1onelogin==1.9.1pandas==1.2.3Pillow==8.1.2pprintpp==0.4.0pymongo==3.11.3pyparsing==2.4.7python-dateutil...
llvmlite 0.27.1-py36_0 --> 0.25.0-py36_2 numba 0.42.1-np116py36_0 --> 0.40.0-np115py36_3 numpy 1.16.1-py36h7b7c402_3 --> 1.15.4-py36_2 numpy-base 1.16.1-py36_3 --> 1.15.4-py36_2 scikit-learn 0.20.2-py36h7b7c402_2 --> 0.20.0-py36_17 scipy 1.2.0-py36_...