我安装了Anaconda(使用Python 2.7),并在一个名为Tensorflow的环境中安装了Tensorflow。我可以在这个环境中成功导入Tensorflow。

问题是Jupyter Notebook无法识别我刚刚创建的新环境。无论我是从GUI Navigator还是tensorflow env中的命令行启动Jupyter Notebook,菜单中只有一个名为Python [Root]的内核,并且不能导入tensorflow。当然,我多次点击这个选项,保存文件,重新打开,但这些都没有帮助。

奇怪的是,当我打开Jupyter首页上的Conda标签时,我可以看到这两个环境。但是当我打开文件选项卡,并尝试新建一个笔记本时,我仍然只有一个内核。

我看了这个问题: 连接Conda环境与Jupyter Notebook 但是在我的电脑上没有~/Library/Jupyter/kernels这样的目录!这个Jupyter目录只有一个称为runtime的子目录。

我真的很困惑。Conda环境应该自动成为内核吗?(我在https://ipython.readthedocs.io/en/stable/install/kernel_install.html上手动设置了内核,但被告知没有找到ipykernel。)


当前回答

这适用于我在windows 10和最新解决方案:

1)进入conda环境(激活your_env_name)

2) conda install -n your_env_name ipykernel

3) python -m ipykernel install——user——name build_central——display name" your_env_name"

(注意:在步骤3中,“your_env_name”周围包含引号)

其他回答

我不得不运行前3个答案中提到的所有命令来让它工作:

conda install jupyter
conda install nb_conda
conda install ipykernel
python -m ipykernel install --user --name mykernel

This has been so frustrating, My problem was that within a newly constructed conda python36 environment, jupyter refused to load “seaborn” - even though seaborn was installed within that environment. It seemed to be able to import plenty of other files from the same environment — for example numpy and pandas but just not seaborn. I tried many of the fixes suggested here and on other threads without success. Until I realised that Jupyter was not running kernel python from within that environment but running the system python as kernel. Even though a decent looking kernel and kernel.json were already present in the environment. It was only after reading this part of the ipython documentation: https://ipython.readthedocs.io/en/latest/install/kernel_install.html#kernels-for-different-environments and using these commands:

source activate other-env
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"

我能让一切顺利进行。(我实际上没有使用-user变量)。

我还没有想到的一件事是如何将默认的python设置为“python (other-env)”。目前,从主屏幕打开的现有.ipynb文件将使用系统python。我必须使用内核菜单“更改内核”来选择环境python。

仅使用环境变量:

python -m ipykernel install --user --name $(basename $VIRTUAL_ENV)

在我的例子中,使用Windows 10和conda 4.6.11,通过运行这些命令

conda install nb_conda

conda install -c conda-forge nb_conda_kernels

在我使用conda Jupyter笔记本从同一命令行打开Jupyter后,从终端同时有环境活动并没有做这项工作。

显然,解决方案是从Anaconda Navigator打开Jupyter,进入我的环境:打开Anaconda Navigator,在Environments中选择环境,按下所选环境的“播放”按钮,并选择“用Jupyter Notebook打开”。

Anaconda Navigator中的环境从选定的环境中运行Jupyter

nb_conda_kernels包是在conda中使用jupyter的最佳方式。通过最小的依赖关系和配置,它允许您使用运行在不同环境中的jupyter笔记本上的其他conda环境。引用其文件:

Installation This package is designed to be managed solely using conda. It should be installed in the environment from which you run Jupyter Notebook or JupyterLab. This might be your base conda environment, but it need not be. For instance, if the environment notebook_env contains the notebook package, then you would run conda install -n notebook_env nb_conda_kernels Any other environments you wish to access in your notebooks must have an appropriate kernel package installed. For instance, to access a Python environment, it must have the ipykernel package; e.g. conda install -n python_env ipykernel To utilize an R environment, it must have the r-irkernel package; e.g. conda install -n r_env r-irkernel For other languages, their corresponding kernels must be installed.

然后你需要做的就是启动jupyter笔记本服务器:

conda activate notebook_env  # only needed if you are not using the base environment for the server
# conda install jupyter # in case you have not installed it already
jupyter


尽管有太多的答案,@merv也在努力改进,但仍然很难找到一个好的答案。我做了这个CW,所以请投票给它的顶部或改进它!