But when I look at the day-to-day work of application builders, there’s one additional bottleneck that I think is underappreciated: The time spent wrestling with version management is an inefficiency I hope we can reduce. A lot of AI software is written in the Python language, and so ...
found 1.3.8 in filename of file in /nix/store/2x2lvkgzvaqmk3jzgz52j1zc49rxcc50-python3.11-bottleneck-1.3.8 Rebuild report(if merged into master) (click to expand) 4 total rebuild path(s) 4 package rebuild(s) First fifty rebuilds by attrpath python311Packages.bottleneck python311Package...
<package_names>即安装在环境中的包名。名称两边不加尖括号“<>”。 如果要安装指定的版本号,则只需要在包名后面以=和版本号的形式执行。如:conda create --name python2 python=2.7,即创建一个名为“python2”的环境,环境中安装版本为2.7的python。 如果要在新创建的环境中创建多个包,则直接在<package_names>...
5. 复制“To install this package with conda run:”下方的命令,并粘贴在终端中执行。 6.完成安装 四、卸载包 1.卸载指定环境中的包 conda remove --name <env_name> <package_name> #<env_name> 即卸载包所在指定环境的名称。环境名两边不加尖括号“<>”。 #<package_name> 即要卸载包的名称。包名两...
$condainstall-ccondapandas$condauninstall-ccondapandasCollectingpackagemetadata(repodata.json):doneSolvingenvironment:done## Package Plan ##environmentlocation:/Users/khuyentran/miniconda3/envs/test-condaremovedspecs:-pandasThefollowingpackageswillbeREMOVED:blas-1.0-openblasbottleneck-1.3.5-py311ha0d4635_0...
conda install–channel https://conda .anaconda.ort/pandas bottleneck 通过pip命令来安装包 对于那些无法通过conda安装或者从Anaconda.org获得的包,我们通常可以用pip命令来安装包。 可以上pypi网 站查询要安装的包,查好以后输入pip install命令就可以安装这个包了。
bottleneck=1.2.1=py36_1-bzip2=1.0.6=1-ca-certificates=2019.3.9=hecc5488_0-cairo=1.14.10=0-cartopy=0.16.0=py36_0-certifi=2019.3.9=py36_0-cf_units=1.2.0=py36_0-cffi=1.11.4=py36h342bebf_0-chardet=3.0.4=py36h96c241c_1-click=6.7=py_1-click-plugins=1.0.3=py36_0-cloudpickle...
Collectingpackagemetadata(repodata.json):done Solving environment:done ## Package Plan ## environment location:/Users/khuyentran/miniconda3/envs/test-conda removed specs:-pandas The following packages will beREMOVED:blas-1.0-openblas bottleneck-1.3.5-py311ha0d4635_0 ...
Bottleneck1.3.5 Brotli1.0.9 brotlipy0.7.0 catalogue2.0.8 certifi2022.12.7 cffi1.15.1 chardet5.0.0 charset-normalizer2.1.1 单击8.1.3 cloudpickle2.2.1 colorama0.4.6 coloredlogs15.0.1 confection0.0.4 contourpy1.0.7 cycler0.11.0 cymem2.0.7 ...
Using this framework, biologists and engineers can opt for reproducible and extensible programmatic data analysis workflows, mediating a bottleneck limiting the throughput of microbial engineering. The Impact framework is available at https://github.com/lmse/impact....