带有最新Python版本的新Conda环境

Question

由于Python版本的更改非常少，所以我总是忘记了如何用最新的Python创建了一个新的Conda环境，用于朱庇特笔记本，所以我想下次我会把它列下来。在StackOverflow中，有一些不再起作用的答案，下面是我在StackOverflow上找到的对我有用的命令的编译(11月29日-2022年)。下面的这些说明适用于Windows，并使用Powershell (尽管它们也可以用于正常的命令行cmd.exe)

    # make sure you are in the base env

    # update conda
    conda update conda

    # to allow support for powershell
    conda init --all

    # The conda-forge repository seems to have at least the latest
    # stable Python version, so we will get Python from there.
    # add conda-forge to channels of conda.
    conda config --add channels conda-forge

    conda update jupyter
    # to fix 500 internal server error when trying to open a notebook later
    pip3 install --upgrade --user nbconvert

    # nb_conda_kernels enables a Jupyter Notebook or JupyterLab
    # application in one conda environment to access kernels for Python,
    # R, and other languages found in other environments.

    conda install nb_conda_kernels

    # I will now create a new conda env for Python 3.11 and name it as Python3.11
    conda create -n python3.11 python=3.11

    # check that it was created
    conda info --envs

    conda activate python3.11

    # Once installed, need to install ipykernel so Jupyter notebook can
    # see the new environment python3.11. 
    conda install -n python3.11 ipykernel

    # install ipywidgets as well for some useful functionalities 
    conda install -n python3.11 ipywidgets

    # Since I use R too, I'll also add a note here on R
    # To utilize an R environment, it must have the r-irkernel package; e.g.
    # conda install -n r_env r-irkernel

    # example to install a package in the new env, if desired
    # conda install --update-all --name python3.11 numpy

    #conda list will show the env's packages, versions, and where they came from too
    conda activate python3.11
    conda list
    conda deactivate

    # Now to check if the new environment can be selected in Jupyter
    # Notebook.  I change to the root directory first so jupyter 
    # notebook can see every folder.  Note that we are in base
    # environment, although no problem if in another environment 
    cd\
    jupyter notebook

    # If I open an existing notebook for example, I can tap on Kernel,
    # then Change kernel, and I should now be able to select the kernel
    # from the new environment I created, shown as "Python [conda env:python3.11]".
    #
    # There will also be another entry showing just the name of the env,
    # in this case, python3.11.  Just ignore this, select the entries
    # starting with "Python [conda env" ...   
    # 
    # If I tapped on New instead when Jupyter Notebook opened, it will
    # also show the list of envs. 

    # to check version, either use :
    !python --version

    # or

    from platform import python_version
    print(python_version()) 

    # both will show the Python version of whatever kernel is in use
    # by Jupyter notebook

    # to test Python 3.10 or 3.11 for example... from 3.10, an optional
    # strict parameter for zip has been added and can be used to
    # generate an error if lists' lengths are not the same

    a = [1,2,3,4]
    b = ['a', 'b', 'c']
    for val1, val2 in zip(a,b, strict = True):
        print(val1, val2)
    
    # this should appear - ValueError: zip() argument 2 is shorter than argument 1

还有别的办法吗？

Answer 1

1. 主要问题中的步骤是nb_conda_kernels way。在基本环境中安装nb_conda_kernels后，从基本环境运行的任何笔记本都会自动显示安装了ipykernel的任何其他环境中的内核。我们只需要一个jupyter笔记本，最好安装在基env.

中。

1. Not理想方法：“快速和肮脏的方法”是在每个env中安装jupyter笔记本。“如果在任何环境中安装jupyter，并在该环境中运行jupyter笔记本，笔记本将使用活动环境中的内核。内核将显示默认名称Python3，但我们可以通过执行以下操作来验证这一功能。”

导入操作系统

打印(os.environ‘’CONDA_DEFAULT_ENV‘)

1. “惯用的或简单的方法”是“单独注册要在内核列表中显示的每个环境”。不需要安装nb_conda_kernels.

在创建新的env之后，例如python3.11

`

conda activate python3.11  # make it active

conda install ipykernel    # needed for each env

conda install ipywidgets   # for additional jupyter functionalities

python -m ipykernel install --user --name python3.11 --display-name "Python 3.11 env"            # will install kernelspec python3.11

`

就是这样，当运行jupyter笔记本时，人们会看到“Python3.11env”作为从中选择的env之一。

注：

这种简单方法的问题是使用！像这样：

!python --version

!pip3 list

!conda list

将始终引用启动jupyter笔记本的env，而不管jupyter笔记本当前选择(正在使用的)什么内核版本。

所以如果我们这样做：

!pip3 install --upgrade numpy

numpy将在启动jupyter笔记本的env中安装或升级。这是一个问题，如果我们正在编程试图升级一个包在jupyter笔记本本身基于条件，例如。

如果我们使用nb_conda_kernels方式，那么上面的命令将始终在活动内核的env中安装/升级，而不管jupyter记事本是从哪个env启动的。

因此，这是值得注意的，如果一个人正在安装包在其他环境中，而不是基础。

就我个人而言，我同时使用了nb_conda_kernels方式(nb_conda_kernels)和通常/简单的方法。只需按照通常的方法执行所有步骤，那么在运行jupyter笔记本之前的最后一步是：

# make sure you are in base env
conda install nb_conda_kernels

因此，我在木星笔记本中的内核列表看起来应该是：

Python 3 (ipykernel)

Python3.11 env

python3.11:python3.11

等。

我可以选择任何我想要的内核，它将工作，同时记住我前面提到的行为。

如果我想要!pip3安装-升级来处理基础中的包，同时使用一个版本与基本版本不同的内核(例如Python3.8)，我将从基env启动笔记本，并选择内核“Python3.11env”。

如果我需要!pip3安装-升级来处理某个env中的包，我可以从任何env启动笔记本，并选择内核“python3.11:python3.11”。