Anaconda 中的依赖项和包冲突? [英] Dependencies and packages conflicts in Anaconda?
问题描述
我在 Windows 10 机器上使用 Anaconda 5.1 和 Python 3.6.
I'm using Anaconda 5.1 and Python 3.6 on a Windows 10 machine.
我遇到了很多问题；我尝试在我的 conda 环境中添加一些有用的工具，例如 lightGBM、tensorflow、keras、bokeh 等，但是一旦我使用过
I'm having quite a few problems ; I tried to add some useful tools such as lightGBM, tensorflow, keras, bokeh,... to my conda environment but once I've used
conda install -c conda-forge 包名
在所有这些中，我最终降级和升级了不同的软件包，这些软件包只会干扰我的安装，并且在这些安装之后我无法再使用任何东西.
on all of these, I end up having downgrading and upgrading of different packages that just mess with my installation and I can't use anything anymore after those installations.
我想知道是否有可能有多个版本的包 &依赖项彼此并存，这不会终止我的安装?
I wonder if it were possible to have multiples versions of packages & dependencies living alongside each other which won't kill my install?
抱歉，如果我的问题看起来很笨拙，感谢您的帮助，
Sorry if my question seems noobish and thanks for your help,
内特
推荐答案
好吧，通过四处搜索，我能够让一切正常运行，而且似乎不再有冲突，我不得不卸载 Anaconda，重新启动我的计算机，然后在我的安装损坏后重新安装它.
Alright by searching around I was able to have everything up and running and it doesn't seem to be in conflict anymore, I had to uninstall Anaconda, reboot my computer and then reinstall it after my installation was broken though.
只要软件包和依赖项没有相互干扰，我就可以安装 lightgbm、folium 和 catboost 在常规(基础)环境中并使用它们.这些是直接使用 conda install -c conda-forge packagename 安装的，除了我链接的 catboost.不要忘记检查可能影响您系统的不同版本的 conda、python 和 pip(wheel).
As long as packages and dependencies weren't messing around with each other, I was able to install lightgbm, folium and catboost in the regular (base) environment and use them. Those were installed straight with conda install -c conda-forge packagename, except for catboost which I linked. Do not forget to check for the different versions of conda, python and pip (wheel) which might affect your system.
此外，安装了 conda install nb_conda 以便能够立即在 Jupyter notebook 中选择不同的环境.
Also, conda install nb_conda was installed to be able to select different environments in the Jupyter notebook straight away.
然后，当我想安装 Tensorflow、Keras、theano 时，对我有用的是第二条评论中的说明 在这个线程中 尽管你不应该忘记在你创建的激活的新环境中再次安装 jupyter.
Then, when I wanted to install Tensorflow, Keras, theano what worked for me are the instructions in the second top comment in this thread though you should not forget to install jupyter again in the activated new environment you created.
之后，关闭所有内容，重新启动所有内容，在 Jupyter 的右上角，您应该能够选择不同的环境并从那里开始工作.
After that, close everything, re-launch everything and in the top right corner of the Jupyter you should be able to pick the different environments and work from there.
我希望这能帮助处于同样困境的其他人.
I hope this will help someone else in the same predicament.
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