blocks|key|14675|text|令人讨厌的是，在您的tensorflow环境中，您可以运行jupyter+notebook+，而无需在该环境中安装。快跑|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|BOLD|entityRanges|data|14676|(tensorflow)+$+conda+install+jupyter|code-block|syntax|javascript|14677|tensorflow环境现在应该可以在木星笔记本中看到，在您的任何conda环境中都是以类似Python+[conda+env:tensorflow]的方式开始的。|14678|entityMap^0|A|A|T|G|1A|8|1L|3|0|0|0|2A|0|A|X|5|1A|T|0^^$0|@$1|2|3|4|5|6|7|P|8|@$9|Q|A|R|B|C]|$9|S|A|T|B|C]|$9|U|A|V|B|D]|$9|W|A|X|B|D]]|E|@]|F|$]]|$1|G|3|H|5|I|7|Y|8|@]|E|@]|F|$J|K]]|$1|L|3|M|5|6|7|Z|8|@$9|10|A|11|B|D]|$9|12|A|13|B|C]|$9|14|A|15|B|C]|$9|16|A|17|B|C]]|E|@]|F|$]]|$1|N|3|-4|5|6|7|18|8|@]|E|@]|F|$]]]|O|$]]

The annoying thing is that in your <code>tensorflow</code> environment, you can run <code>jupyter notebook</code> without installing <code>jupyter</code> in that environment. Just run

<pre><code>(tensorflow) $ conda install jupyter
</code></pre>

and the <code>tensorflow</code> environment should now be visible in Jupyter Notebooks started in any of your <code>conda</code> environments as something like <code>Python [conda env:tensorflow]</code>.

blocks|key|206500|text|如果您的环境未显示，请确保已安装|type|unstyled|depth|inlineStyleRanges|entityRanges|data|206501|木星环境中的nb_conda_kernels|unordered-list-item|offset|length|style|CODE|206502|在您想要访问的Python环境中，ipykernel和ipywidgets+(请注意，ipywidgets是为了启用某些木星功能，而不是环境可见性，参见相关文档)。|206503|Anaconda文件说|206504|nb_conda_kernels应该安装在运行朱庇特笔记本或JupyterLab的环境中。这可能是您的基本conda环境，但不必是。例如，如果环境notebook_env包含笔记本包，那么您将运行
安装-n+notebook_env+nb_conda_kernels
您希望在笔记本中访问的任何其他环境都必须安装适当的内核包。例如，要访问Python环境，它必须有ipykernel包；
conda安装-n+python_env+ipykernel
要利用R环境，它必须具有r-irkernel包。
安装-n+r_env+r-irkernel
对于其他语言，必须安装它们对应的核。|blockquote|206505|除了Python之外，通过安装专用的*kernel包，木星可以访问来自一吨其他语言的内核，包括R、朱莉娅、斯卡拉/火花、JavaScript、bash、八度，甚至MATLAB。|206506|请注意，在最初发布这篇文章时，可能是还不支持Python3.6环境造成的。|206507|如果其他解决方案无法让木星识别其他conda环境，则始终可以在特定环境中安装和运行jupyter。不过，你可能无法从木星内部看到或切换到其他环境。|206508|$+conda+create+-n+py36_test+-y+python=3.6+jupyter
$+source+activate+py36_test
(py36_test)+$+which+jupyter
/home/schowell/anaconda3/envs/py36_test/bin/jupyter
(py36_test)+$+jupyter+notebook|code-block|syntax|javascript|206509|请注意，我正在此笔记本中运行Python3.6.1：|206510|📷|atomic|206511|​|206512|请注意，如果您在许多环境中都这样做，将木星安装到每个环境中所增加的存储空间可能是不可取的(取决于您的系统)。|206513|entityMap|0|LINK|mutability|MUTABLE|url|https://ipywidgets.readthedocs.io/en/stable/user_install.html#installing-in-jupyterlab-3-0|1|https://github.com/Anaconda-Platform/nb_conda_kernels#installation|2|https://github.com/jupyter/jupyter/wiki/Jupyter-kernels|3|4|https://irkernel.github.io/|5|https://github.com/JuliaLang/IJulia.jl|6|https://github.com/apache/incubator-toree|7|https://github.com/n-riesco/ijavascript|8|https://github.com/calysto/octave_kernel|9|https://github.com/calysto/matlab_kernel|10|https://github.com/Anaconda-Platform/nb_conda/issues/45|11|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/365e487a9c180dca8d0136605e19320b.png|imageAlt^0|0|6|G|0|H|9|R|A|17|A|24|4|0|0|0|A|1|0|0|G|7V|6|2|0|I|7|Z|6|3|1B|1|4|1D|3|5|1H|6|6|1O|A|7|24|2|8|29|6|9|0|I|F|A|0|15|7|0|0|0|0|1|B|0|0|0^^$0|@$1|2|3|4|5|6|7|26|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|27|8|@$E|28|F|29|G|H]]|9|@]|A|$]]|$1|I|3|J|5|D|7|2A|8|@$E|2B|F|2C|G|H]|$E|2D|F|2E|G|H]|$E|2F|F|2G|G|H]]|9|@$E|2H|F|2I|1|2J]]|A|$]]|$1|K|3|L|5|6|7|2K|8|@]|9|@$E|2L|F|2M|1|2N]]|A|$]]|$1|M|3|N|5|O|7|2O|8|@$E|2P|F|2Q|G|H]]|9|@$E|2R|F|2S|1|2T]]|A|$]]|$1|P|3|Q|5|6|7|2U|8|@$E|2V|F|2W|G|H]]|9|@$E|2X|F|2Y|1|2Z]|$E|30|F|31|1|32]|$E|33|F|34|1|35]|$E|36|F|37|1|38]|$E|39|F|3A|1|3B]|$E|3C|F|3D|1|3E]|$E|3F|F|3G|1|3H]]|A|$]]|$1|R|3|S|5|6|7|3I|8|@]|9|@$E|3J|F|3K|1|3L]]|A|$]]|$1|T|3|U|5|6|7|3M|8|@$E|3N|F|3O|G|H]]|9|@]|A|$]]|$1|V|3|W|5|X|7|3P|8|@]|9|@]|A|$Y|Z]]|$1|10|3|11|5|6|7|3Q|8|@]|9|@]|A|$]]|$1|12|3|13|5|14|7|3R|8|@]|9|@$E|3S|F|3T|1|3U]]|A|$]]|$1|15|3|16|5|6|7|3V|8|@]|9|@]|A|$]]|$1|17|3|18|5|6|7|3W|8|@]|9|@]|A|$]]|$1|19|3|-4|5|6|7|3X|8|@]|9|@]|A|$]]]|1A|$1B|$5|1C|1D|1E|A|$1F|1G]]|1H|$5|1C|1D|1E|A|$1F|1I]]|1J|$5|1C|1D|1E|A|$1F|1K]]|1L|$5|1C|1D|1E|A|$1F|1K]]|1M|$5|1C|1D|1E|A|$1F|1N]]|1O|$5|1C|1D|1E|A|$1F|1P]]|1Q|$5|1C|1D|1E|A|$1F|1R]]|1S|$5|1C|1D|1E|A|$1F|1T]]|1U|$5|1C|1D|1E|A|$1F|1V]]|1W|$5|1C|1D|1E|A|$1F|1X]]|1Y|$5|1C|1D|1E|A|$1F|1Z]]|20|$5|21|1D|22|A|$23|24|25|-4]]]]

If your environments are not showing up, make sure you have installed
<ul>
<li><code>nb_conda_kernels</code> in the environment with Jupyter</li>
<li><code>ipykernel</code> and <code>ipywidgets</code> in the Python environment you want to access (note that <code>ipywidgets</code> is to enable some Juptyer functionality, not environment visibility, see <a href="https://ipywidgets.readthedocs.io/en/stable/user_install.html#installing-in-jupyterlab-3-0" rel="noreferrer">related docs</a>).</li>
</ul>
<a href="https://github.com/Anaconda-Platform/nb_conda_kernels#installation" rel="noreferrer">Anaconda's documentation</a> states that
<blockquote>
<code>nb_conda_kernels</code> 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
<pre><code>conda install -n notebook_env nb_conda_kernels
</code></pre>
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.
<pre><code>conda install -n python_env ipykernel
</code></pre>
To utilize an R environment, it must have the r-irkernel package; e.g.
<pre><code>conda install -n r_env r-irkernel
</code></pre>
For other languages, <a href="https://github.com/jupyter/jupyter/wiki/Jupyter-kernels" rel="noreferrer">their corresponding kernels</a> must be installed.
</blockquote>
In addition to Python, by installing the appropriatel <code>*kernel</code> package, Jupyter can access kernels from <a href="https://github.com/jupyter/jupyter/wiki/Jupyter-kernels" rel="noreferrer">a ton of other languages</a> including <a href="https://irkernel.github.io/" rel="noreferrer">R</a>, <a href="https://github.com/JuliaLang/IJulia.jl" rel="noreferrer">Julia</a>, <a href="https://github.com/apache/incubator-toree" rel="noreferrer">Scala/Spark</a>, <a href="https://github.com/n-riesco/ijavascript" rel="noreferrer">JavaScript</a>, bash, <a href="https://github.com/calysto/octave_kernel" rel="noreferrer">Octave</a>, and even <a href="https://github.com/calysto/matlab_kernel" rel="noreferrer">MATLAB</a>.
<hr />
Note that at the time originally posting this, there was a possible cause from <a href="https://github.com/Anaconda-Platform/nb_conda/issues/45" rel="noreferrer"><code>nb_conda</code> not yet supporting Python 3.6 environments</a>.
If other solutions fail to get Jupyter to recognize other conda environments, you can always install and run <code>jupyter</code> from within a specific environment. You may not be able to see or switch to other environments from within Jupyter though.
<pre><code>$ conda create -n py36_test -y python=3.6 jupyter
$ source activate py36_test
(py36_test) $ which jupyter
/home/schowell/anaconda3/envs/py36_test/bin/jupyter
(py36_test) $ jupyter notebook
</code></pre>
Notice that I am running Python 3.6.1 in this notebook:
<a href="https://i.stack.imgur.com/YJDq0.png" rel="noreferrer"><img src="https://i.stack.imgur.com/YJDq0.png" alt="enter image description here" /></a>
Note that if you do this with many environments, the added storage space from installing Jupyter into every environment may be undesirable (depending on your system).

blocks|key|1934918|text|只需在您的新环境中运行conda+install+ipykernel，只有这样，您才能获得这个env的内核。即使您在每个env中安装了不同的版本，并且不再安装jupyter笔记本，这也是可行的。您可以启动您的笔记本从任何env，您将能够看到新添加的内核。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1934919|entityMap^0|B|N|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|K|8|@]|D|@]|E|$]]]|G|$]]

Just run <code>conda install ipykernel</code> in your new environment, only then you will get a kernel with this env. This works even if you have different versions installed in each envs and it doesn't install jupyter notebook again. You can start youe notebook from any env you will be able to see newly added kernels.

blocks|key|14890|text|我遇到了同样的问题，我的新conda环境myenv无法被选择为内核或新笔记本。并且在env中运行jupter+notebook也得到了同样的结果。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|14891|我的解决方案，以及我对木星笔记本如何识别conda-env和内核的了解：|14892|使用conda将jupyter和ipython安装到myenv：|14893|conda+install+-n+myenv+ipython+jupyter|code-block|syntax|javascript|14894|之后，在任何env之外运行jupter+notebook都会将myenv和我以前的环境一起列为内核。|14895|Python+[conda+env:old]
Python+[conda+env:myenv]|14896|一旦激活环境，就运行笔记本：|14897|source+activate+myenv
jupyter+notebook|14898|隐藏所有其他环境--内核，只显示语言内核：|14899|python+2
python+3
R|14900|entityMap^0|K|5|1C|F|0|0|Q|5|0|0|D|F|V|5|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|17|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|18|8|@$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|J|3|K|5|L|7|1B|8|@]|D|@]|E|$M|N]]|$1|O|3|P|5|6|7|1C|8|@$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|L|7|1H|8|@]|D|@]|E|$M|N]]|$1|S|3|T|5|6|7|1I|8|@]|D|@]|E|$]]|$1|U|3|V|5|L|7|1J|8|@]|D|@]|E|$M|N]]|$1|W|3|X|5|6|7|1K|8|@]|D|@]|E|$]]|$1|Y|3|Z|5|L|7|1L|8|@]|D|@]|E|$M|N]]|$1|10|3|-4|5|6|7|1M|8|@]|D|@]|E|$]]]|11|$]]

I ran into this same problem where my new conda environment, <code>myenv</code>, couldn't be selected as a kernel or a new notebook. And running <code>jupter notebook</code> from within the env gave the same result.

My solution, and what I learned about how Jupyter notebooks recognizes conda-envs and kernels:

Installing jupyter and ipython to <code>myenv</code> with conda:

<pre><code>conda install -n myenv ipython jupyter
</code></pre>

After that, running <code>jupter notebook</code> outside any env listed <code>myenv</code> as a kernel along with my previous environments.

<pre><code>Python [conda env:old]
Python [conda env:myenv]
</code></pre>

Running the notebook once I activated the environment:

<pre><code>source activate myenv
jupyter notebook
</code></pre>

hides all my other environment-kernels and only shows my language kernels:

<pre><code>python 2
python 3
R
</code></pre>

blocks|key|1934968|text|我认为其他答案不再有效，因为conda停止自动将环境设置为jupyter内核。您需要以以下方式手动为每个环境添加内核：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1934969|source+activate+myenv
python+-m+ipykernel+install+--user+--name+myenv+--display-name+"Python+(myenv)"|code-block|syntax|javascript|1934970|如本文所述：install.html#kernels-for-different-environments也参见本期。|offset|length|1934971|增编:您应该能够安装带有conda+install+nb_conda_kernels的conda+install+nb_conda_kernels包，以自动添加所有环境，请参阅核粒|style|CODE|1934972|entityMap|0|LINK|mutability|MUTABLE|url|http://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments|1|https://github.com/jupyter/jupyter/issues/245|2|https://github.com/Anaconda-Platform/nb_conda_kernels^0|0|0|6|1B|0|1K|2|1|0|C|U|17|U|2G|2|2|0^^$0|@$1|2|3|4|5|6|7|10|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|11|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|12|8|@]|9|@$I|13|J|14|1|15]|$I|16|J|17|1|18]]|A|$]]|$1|K|3|L|5|6|7|19|8|@$I|1A|J|1B|M|N]|$I|1C|J|1D|M|N]]|9|@$I|1E|J|1F|1|1G]]|A|$]]|$1|O|3|-4|5|6|7|1H|8|@]|9|@]|A|$]]]|P|$Q|$5|R|S|T|A|$U|V]]|W|$5|R|S|T|A|$U|X]]|Y|$5|R|S|T|A|$U|Z]]]]

I don't think the other answers are working any more, as conda stopped automatically setting environments up as jupyter kernels. You need to manually add kernels for each environment in the following way:

<pre><code>source activate myenv
python -m ipykernel install --user --name myenv --display-name "Python (myenv)"
</code></pre>

As documented here:<a href="http://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments" rel="noreferrer">http://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments</a>
Also see <a href="https://github.com/jupyter/jupyter/issues/245" rel="noreferrer">this issue</a>.

Addendum:
You should be able to install the <code>nb_conda_kernels</code> package with <code>conda install nb_conda_kernels</code> to add all environments automatically, see <a href="https://github.com/Anaconda-Platform/nb_conda_kernels" rel="noreferrer">https://github.com/Anaconda-Platform/nb_conda_kernels</a>

blocks|key|206679|text|我们在这个问题上做了很多斗争，下面是对我们有用的方法。如果使用conda锻造通道，确保使用来自conda-forge的更新包非常重要，即使在Miniconda根环境中也是如此。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|206680|所以请安装米尼康达，然后执行以下操作：|206681|conda+config+--add+channels+conda-forge+--force
conda+update+--all++-y
conda+install+nb_conda_kernels+-y
conda+env+create+-f+custom_env.yml+-q+--force
jupyter+notebook|code-block|syntax|javascript|206682|而且您的自定义环境将以可用内核的形式出现在木星中，只要在您的ipykernel文件中列出要安装的custom_env.yml，如下例所示：|206683|name:+bqplot
channels:
-+conda-forge
-+defaults
dependencies:
-+python>=3.6
-+bqplot
-+ipykernel|206684|为了证明它适用于一系列定制环境，这里有一个Windows屏幕抓取：|206685|​|206686|📷|atomic|206687|206688|entityMap|0|LINK|mutability|MUTABLE|url|https://anaconda.org/conda-forge|1|https://conda.io/miniconda.html|2|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/d7ee487ca0e0697daf64b5b8746ea1f7.png|imageAlt^0|1B|B|1Y|9|V|9|0|0|5|4|1|0|0|U|9|1C|E|0|0|0|0|0|1|2|0|0^^$0|@$1|2|3|4|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]]|D|@$9|1J|A|1K|1|1L]]|E|$]]|$1|F|3|G|5|6|7|1M|8|@]|D|@$9|1N|A|1O|1|1P]]|E|$]]|$1|H|3|I|5|J|7|1Q|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1R|8|@$9|1S|A|1T|B|C]|$9|1U|A|1V|B|C]]|D|@]|E|$]]|$1|O|3|P|5|J|7|1W|8|@]|D|@]|E|$K|L]]|$1|Q|3|R|5|6|7|1X|8|@]|D|@]|E|$]]|$1|S|3|T|5|6|7|1Y|8|@]|D|@]|E|$]]|$1|U|3|V|5|W|7|1Z|8|@]|D|@$9|20|A|21|1|22]]|E|$]]|$1|X|3|T|5|6|7|23|8|@]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|24|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15]]|16|$5|11|12|13|E|$14|17]]|18|$5|19|12|1A|E|$1B|1C|1D|-4]]]]

We have struggle a lot with this issue, and here's what works for us. If you use the <a href="https://anaconda.org/conda-forge" rel="noreferrer">conda-forge channel</a>, it's important to make sure you are using updated packages from <code>conda-forge</code>, even in your <code>Miniconda</code> root environment. 

So install <a href="https://conda.io/miniconda.html" rel="noreferrer">Miniconda</a>, and then do:

<pre class="lang-bash prettyprint-override"><code>conda config --add channels conda-forge --force
conda update --all -y
conda install nb_conda_kernels -y
conda env create -f custom_env.yml -q --force
jupyter notebook
</code></pre>

and your custom environment will show up in Jupyter as an available kernel, as long as <code>ipykernel</code> was listed for installation in your <code>custom_env.yml</code> file, like this example:

<pre><code>name: bqplot
channels:
- conda-forge
- defaults
dependencies:
- python&gt;=3.6
- bqplot
- ipykernel
</code></pre>

Just to prove it working with a bunch of custom environments, here's a screen grab from Windows: 

<a href="https://i.stack.imgur.com/hw7d9.png" rel="noreferrer"><img src="https://i.stack.imgur.com/hw7d9.png" alt="enter image description here"></a>

blocks|key|15100|text|我必须运行前3条答案中提到的所有命令才能使此工作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15101|conda+install+jupyter
conda+install+nb_conda
conda+install+ipykernel
python+-m+ipykernel+install+--user+--name+mykernel|code-block|syntax|javascript|15102|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

I had to run all the commands mentioned in the top 3 answers to get this working:

<pre><code>conda install jupyter
conda install nb_conda
conda install ipykernel
python -m ipykernel install --user --name mykernel
</code></pre>

blocks|key|15123|text|这是如此令人沮丧，我的问题是，在一个新构建的conda+python36环境中，jupyter拒绝加载“海运”-尽管海运是在该环境中安装的。它似乎能够从相同的环境中导入大量其他文件--比如numpy和熊猫，但不能从海上进口。我在这里和其他线程上尝试了许多修补程序，但都没有成功。直到我意识到，木星并不是在这个环境中运行内核python，而是将系统python作为内核运行。尽管环境中已经出现了一个好看的内核和kernel.json。只是在阅读了ipython文档的这一部分之后：install.html#kernels-for-different-environments并使用了以下命令：|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|15124|source+activate+other-env
python+-m+ipykernel+install+--user+--name+other-env+--display-name+"Python+(other-env)"|code-block|syntax|javascript|15125|我能让一切顺利。(我实际上没有使用-user变量)。|15126|我还没有想到的一件事是如何将默认的python设置为"Python+(其他-env)“之一。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。|15127|entityMap|0|LINK|mutability|MUTABLE|url|https://ipython.readthedocs.io/en/latest/install/kernel_install.html#kernels-for-different-environments^0|6N|1B|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@]|9|@$A|V|B|W|1|X]]|C|$]]|$1|D|3|E|5|F|7|Y|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|Z|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|10|8|@]|9|@]|C|$]]|$1|M|3|-4|5|6|7|11|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]]]

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:
<a href="https://ipython.readthedocs.io/en/latest/install/kernel_install.html#kernels-for-different-environments" rel="noreferrer">https://ipython.readthedocs.io/en/latest/install/kernel_install.html#kernels-for-different-environments</a>
and using these commands:

<pre><code>source activate other-env
python -m ipykernel install --user --name other-env --display-name "Python (other-env)"
</code></pre>

I was able to get everything going nicely. (I didn’t actually use the —user variable).

One thing I have not yet figured is how to set the default python to be the "Python (other-env)" one. At present an existing .ipynb file opened from the Home screen will use the system python. I have to use the Kernel menu “Change kernel” to select the environment python.

blocks|key|1935140|text|++++$+conda+install+nb_conda_kernels|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1935141|(在运行jupyter记事本的conda环境中)将使所有conda+env自动可用。为了访问其他环境，必须安装相应的内核。这是参考。|unstyled|offset|length|1935142|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/Anaconda-Platform/nb_conda_kernels^0|0|1R|2|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$B|C]]|$1|D|3|E|5|F|7|R|8|@]|9|@$G|S|H|T|1|U]]|A|$]]|$1|I|3|-4|5|F|7|V|8|@]|9|@]|A|$]]]|J|$K|$5|L|M|N|A|$O|P]]]]

<pre><code> $ conda install nb_conda_kernels
</code></pre>

(in the conda environment where you run jupyter notebook) will make all conda envs available automatically. For access to other environments, the respective kernels must be installed. Here's the <a href="https://github.com/Anaconda-Platform/nb_conda_kernels" rel="noreferrer">ref</a>.

blocks|key|1935188|text|虽然@酷科学家的回答为我工作，但也有一种方法不会使内核环境与完整的jupyter+package%2Bdeps相混淆。它是在ipython文档中描述的，并且(我怀疑)只有在非基本环境中运行笔记本服务器时才是必要的。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1935189|conda+activate+name_of_your_kernel_env
conda+install+ipykernel
python+-m+ipykernel+install+--prefix=/home/your_username/.conda/envs/name_of_your_jupyter_server_env+--name+'name_of_your_kernel_env'|code-block|syntax|javascript|1935190|您可以检查它是否可以使用|1935191|conda+activate+name_of_your_jupyter_server_env+
jupyter+kernelspec+list|1935192|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/a/50958537/1870254|1|https://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments^0|2|8|0|1O|9|1|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]|$A|10|B|11|1|12]]|C|$]]|$1|D|3|E|5|F|7|13|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|14|8|@]|9|@]|C|$]]|$1|K|3|L|5|F|7|15|8|@]|9|@]|C|$G|H]]|$1|M|3|-4|5|6|7|16|8|@]|9|@]|C|$]]]|N|$O|$5|P|Q|R|C|$S|T]]|U|$5|P|Q|R|C|$S|V]]]]

While <a href="https://stackoverflow.com/a/50958537/1870254">@coolscitist's answer</a> worked for me, there is also a way that does not clutter your kernel environment with the complete jupyter package+deps.
It is described in the <a href="https://ipython.readthedocs.io/en/stable/install/kernel_install.html#kernels-for-different-environments" rel="nofollow noreferrer">ipython docs</a> and is (I suspect) only necessary if you run the notebook server in a non-base environment.

<pre class="lang-sh prettyprint-override"><code>conda activate name_of_your_kernel_env
conda install ipykernel
python -m ipykernel install --prefix=/home/your_username/.conda/envs/name_of_your_jupyter_server_env --name 'name_of_your_kernel_env'
</code></pre>

You can check if it works using

<pre class="lang-sh prettyprint-override"><code>conda activate name_of_your_jupyter_server_env 
jupyter kernelspec list
</code></pre>

blocks|key|15235|text|摘要(tldr)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15236|如果您希望'python3‘内核总是从启动它的环境中运行，请删除用户'python3’内核，它优先于当前环境中的任何内容：|15237|jupyter+kernelspec+remove+python3|code-block|syntax|javascript|15238|全解|15239|我将为以下情况发布另一种更简单的解决方案：|15240|您已经创建了一个conda环境|unordered-list-item|15241|此环境安装了jupyter+(也安装了ipykernel)。|15242|当您运行命令jupyter+notebook并单击'+new‘下拉菜单中的'python3’创建一个新笔记本时，该笔记本将从基本环境而不是从当前环境执行python。|offset|length|style|CODE|15243|您希望它能够在任何环境中启动一个带有“python3”的新笔记本，这样就可以从该环境而不是基础上执行Python。|15244|我将为解决方案的其余部分使用环境名称“test_env”。另外，请注意'python3‘是内核的名称。|15245|目前投票最多的答案确实有效，但还有一种选择。它说要做以下工作：|15246|python+-m+ipykernel+install+--user+--name+test_env+--display-name+"Python+(test_env)"|15247|这将使您可以选择使用test_env环境，而不管您从哪个环境启动jupyter+notebook。但是，启动带有“python3”的笔记本电脑仍将使用基本环境中的Python安装。|15248|可能发生的是存在一个用户python3内核。运行命令jupyter+kernelspec+list列出所有的环境。例如，如果您有一个mac，您将被返回如下(我的用户名是Ted)。|15249|python3+++++++/Users/Ted/Library/Jupyter/kernels/python3|15250|木星在这里所做的是通过三条不同的路径寻找内核。它从用户到Env，再到System。有关它搜索每个操作系统的路径的更多详细信息，请参见本文件。|BOLD|15251|上面的两个内核都在用户路径中，这意味着无论您从哪个环境启动jupyter笔记本，它们都是可用的。这也意味着，如果在环境级别上存在另一个“python3”内核，那么您将永远无法访问它。|15252|对我来说，从启动笔记本的环境中选择'python3‘内核应该从该环境执行Python更有意义。|15253|您可以通过查看操作系统的Env搜索路径来检查是否有另一个“python3”环境(请参阅上面的文档链接)。对于我(在我的mac上)，我发出了以下命令：|15254|+ls+/Users/Ted/anaconda3/envs/test_env/share/jupyter/kernels|15255|我确实在那里列出了一个'python3‘内核。|15256|多亏了这个GitHub问题的评论+(查看第一个响应)，您可以使用以下命令删除用户'python3‘环境：|15257|jupyter+kernelspec+remove+python3|15258|现在，当您运行jupyter+kernelspec+list时，假设test_env仍然处于活动状态，您将得到以下内容：|15259|python3+++++++/Users/Ted/anaconda3/envs/test_env/share/jupyter/kernels/python3|15260|注意，此路径位于test_env目录中。如果您创建了一个新环境，安装jupyter，激活它，并列出内核，您将得到位于其环境路径中的另一个“python3”内核。|15261|用户'python3‘内核优先于任何Env+'python3’内核。通过删除它，可以公开活动环境'python3‘内核，并且每次都可以选择它。这样就不需要手动创建内核了。在软件开发方面，这样做也更有意义，因为在软件开发中，人们希望将自己隔离到一个单一的环境中。运行与主机环境不同的内核似乎不太自然。|15262|在默认情况下，这个用户'python3‘并不是为每个人安装的，所以并不是每个人都会遇到这个问题。|15263|entityMap|0|LINK|mutability|MUTABLE|url|https://jupyter-client.readthedocs.io/en/stable/kernels.html#kernel-specs|1|https://github.com/jupyter/notebook/issues/2898^0|0|0|0|0|0|0|0|6|G|0|0|0|0|0|W|G|0|Q|N|0|0|P|2|S|3|Y|6|1U|3|0|0|0|0|0|0|0|3|D|1|0|0|7|N|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|2C|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|2D|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|2E|8|@]|9|@]|A|$G|H]]|$1|I|3|J|5|6|7|2F|8|@]|9|@]|A|$]]|$1|K|3|L|5|6|7|2G|8|@]|9|@]|A|$]]|$1|M|3|N|5|O|7|2H|8|@]|9|@]|A|$]]|$1|P|3|Q|5|O|7|2I|8|@]|9|@]|A|$]]|$1|R|3|S|5|O|7|2J|8|@$T|2K|U|2L|V|W]]|9|@]|A|$]]|$1|X|3|Y|5|O|7|2M|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|2N|8|@]|9|@]|A|$]]|$1|11|3|12|5|6|7|2O|8|@]|9|@]|A|$]]|$1|13|3|14|5|F|7|2P|8|@]|9|@]|A|$G|H]]|$1|15|3|16|5|6|7|2Q|8|@$T|2R|U|2S|V|W]]|9|@]|A|$]]|$1|17|3|18|5|6|7|2T|8|@$T|2U|U|2V|V|W]]|9|@]|A|$]]|$1|19|3|1A|5|F|7|2W|8|@]|9|@]|A|$G|H]]|$1|1B|3|1C|5|6|7|2X|8|@$T|2Y|U|2Z|V|1D]|$T|30|U|31|V|1D]|$T|32|U|33|V|1D]]|9|@$T|34|U|35|1|36]]|A|$]]|$1|1E|3|1F|5|6|7|37|8|@]|9|@]|A|$]]|$1|1G|3|1H|5|6|7|38|8|@]|9|@]|A|$]]|$1|1I|3|1J|5|6|7|39|8|@]|9|@]|A|$]]|$1|1K|3|1L|5|F|7|3A|8|@]|9|@]|A|$G|H]]|$1|1M|3|1N|5|6|7|3B|8|@]|9|@]|A|$]]|$1|1O|3|1P|5|6|7|3C|8|@]|9|@$T|3D|U|3E|1|3F]]|A|$]]|$1|1Q|3|1R|5|F|7|3G|8|@]|9|@]|A|$G|H]]|$1|1S|3|1T|5|6|7|3H|8|@$T|3I|U|3J|V|W]]|9|@]|A|$]]|$1|1U|3|1V|5|F|7|3K|8|@]|9|@]|A|$G|H]]|$1|1W|3|1X|5|6|7|3L|8|@]|9|@]|A|$]]|$1|1Y|3|1Z|5|6|7|3M|8|@]|9|@]|A|$]]|$1|20|3|21|5|6|7|3N|8|@]|9|@]|A|$]]|$1|22|3|-4|5|6|7|3O|8|@]|9|@]|A|$]]]|23|$24|$5|25|26|27|A|$28|29]]|2A|$5|25|26|27|A|$28|2B]]]]

<h3>Summary (tldr)</h3>

If you want the 'python3' kernel to always run the Python installation from the environment where it is launched, delete the User 'python3' kernel, which is taking precedence over whatever the current environment is with:

<pre><code>jupyter kernelspec remove python3
</code></pre>

<h3>Full Solution</h3>

I am going to post an alternative and simpler solution for the following case:

<ul>
<li>You have created a conda environment</li>
<li>This environment has jupyter installed (which also installs ipykernel)</li>
<li>When you run the command <code>jupyter notebook</code> and create a new notebook by clicking 'python3' in the 'New' dropdown menu, that notebook executes python from the base environment and not from the current environment.</li>
<li>You would like it so that launching a new notebook with 'python3' within any environment executes the Python version from that environment and NOT the base</li>
</ul>

I am going to use the name 'test_env' for the environment for the rest of the solution. Also, note that 'python3' is the name of the kernel.

The currently top-voted answer does work, but there is an alternative. It says to do the following:

<pre><code>python -m ipykernel install --user --name test_env --display-name "Python (test_env)"
</code></pre>

This will give you the option of using the test_env environment regardless of what environment you launch <code>jupyter notebook</code> from. But, launching a notebook with 'python3' will still use the Python installation from the base environment.

What likely is happening is that there is a user python3 kernel that exists. Run the command <code>jupyter kernelspec list</code> to list all of your environments. For instance, if you have a mac you will be returned the following (my user name is Ted).

<pre><code>python3 /Users/Ted/Library/Jupyter/kernels/python3
</code></pre>

What Jupyter is doing here is searching through three different paths looking for kernels. It goes from User, to Env, to System. See <a href="https://jupyter-client.readthedocs.io/en/stable/kernels.html#kernel-specs" rel="noreferrer">this document</a> for more details on the paths it searches for each operating system.

The two kernels above are both in the User path, meaning they will be available regardless of the environment that you launch a jupyter notebook from. This also means that if there is another 'python3' kernel at the environment level, then you will never be able to access it.

To me, it makes more sense that choosing the 'python3' kernel from the environment you launched the notebook from should execute Python from that environment.

You can check to see if you have another 'python3' environment by looking in the Env search path for your OS (see the link to the docs above). For me (on my mac), I issued the following command:

<pre><code> ls /Users/Ted/anaconda3/envs/test_env/share/jupyter/kernels
</code></pre>

And I indeed had a 'python3' kernel listed there. 

Thanks to <a href="https://github.com/jupyter/notebook/issues/2898" rel="noreferrer">this GitHub issue comment</a> (look at the first response), you can remove the User 'python3' environment with the following command:

<pre><code>jupyter kernelspec remove python3
</code></pre>

Now when you run <code>jupyter kernelspec list</code>, assuming the test_env is still active, you will get the following:

<pre><code>python3 /Users/Ted/anaconda3/envs/test_env/share/jupyter/kernels/python3
</code></pre>

Notice that this path is located within the test_env directory. If you create a new environment, install jupyter, activate it, and list the kernels, you will get another 'python3' kernel located in its environment path.

The User 'python3' kernel was taking precedence over any of the Env 'python3' kernels. By removing it, the active environment 'python3' kernel was exposed and able to be chosen every time. This eliminates the need to manually create kernels. It also makes more sense in terms of software development where one would want to isolate themselves into a single environment. Running a kernel that is different from the host environment doesn't seem natural. 

It also seems that this User 'python3' is not installed for everyone by default, so not everyone is confronted by this issue.

blocks|key|15303|text|按照iPython文档中的说明将不同的conda环境添加到朱庇特笔记本中要选择的内核列表中。总之，在安装ipykernel之后，必须在终端中逐个激活每个conda环境，并运行命令python+-m+ipykernel+install+--user+--name+myenv+--display-name+"Python+(myenv)"，其中myenv是要添加的环境(内核)。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|15304|entityMap|0|LINK|mutability|MUTABLE|url|https://ipython.readthedocs.io/en/stable/install/kernel_install.html^0|1G|9|2H|27|4R|5|2|D|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]|$9|Q|A|R|B|C]|$9|S|A|T|B|C]]|D|@$9|U|A|V|1|W]]|E|$]]|$1|F|3|-4|5|6|7|X|8|@]|D|@]|E|$]]]|G|$H|$5|I|J|K|E|$L|M]]]]

Follow the <a href="https://ipython.readthedocs.io/en/stable/install/kernel_install.html" rel="nofollow noreferrer">instructions in the iPython documentation</a> for adding different conda environments to the list of kernels to choose from in Jupyter Notebook. In summary, after installing <code>ipykernel</code>, you must activate each conda environment one by one in a terminal and run the command <code>python -m ipykernel install --user --name myenv --display-name "Python (myenv)"</code>, where <code>myenv</code> is the environment (kernel) you want to add.

blocks|key|15360|text|在我的例子中，通过运行命令使用Windows+10和conda+4.6.11|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15361|conda+install+nb_conda

conda+install+-c+conda-forge+nb_conda_kernels|code-block|syntax|javascript|15362|在我使用conda+jupyter+notebook从同一个命令行打开木星之后，激活的环境在终端上却没有做好工作。|offset|length|style|CODE|15363|解决方案显然是从Anaconda+Navigator打开木星，在环境中打开我的环境:打开Anaconda+Navigator，选择环境中的环境，按下所选环境上的"play“按钮，然后选择‘用木星打开笔记本’。|15364|Anaconda+Navigator从选定环境运行木星的环境|15365|​|15366|📷|atomic|15367|15368|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/156891569e4cc328f39693c33ff459e4.png|imageAlt^0|0|0|4|M|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|15|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|16|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|17|8|@$I|18|J|19|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|1A|8|@]|9|@]|A|$]]|$1|O|3|P|5|6|7|1B|8|@]|9|@]|A|$]]|$1|Q|3|R|5|6|7|1C|8|@]|9|@]|A|$]]|$1|S|3|T|5|U|7|1D|8|@]|9|@$I|1E|J|1F|1|1G]]|A|$]]|$1|V|3|R|5|6|7|1H|8|@]|9|@]|A|$]]|$1|W|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]]|X|$Y|$5|Z|10|11|A|$12|13|14|-4]]]]

In my case, using Windows 10 and conda 4.6.11, by running the commands 

<pre><code>conda install nb_conda

conda install -c conda-forge nb_conda_kernels
</code></pre>

from the terminal while having the environment active didn't do the job after I opened Jupyter from the same command line using <code>conda jupyter notebook</code>. 

The solution was apparently to opened Jupyter from the Anaconda Navigator by going to my environment in Environments: Open Anaconda Navigator, select the environment in Environments, press on the "play" button on the chosen environment, and select 'open with Jupyter Notebook'.

Environments in Anaconda Navigator to run Jupyter from the selected environment

<img src="https://i.stack.imgur.com/ZmFZv.png" alt="">

blocks|key|15437|text|nb_conda_kernels包是将jupyter与conda结合使用的最佳方法。凭借最小的依赖关系和配置，它允许您从运行在不同环境中的jupyter笔记本中使用其他conda环境。引用其文档|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|15438|安装
此包设计为仅使用conda进行管理。它应该安装在运行木星笔记本或JupyterLab的环境中。这可能是您的base+conda环境，但不必是。例如，如果环境notebook_env包含笔记本包，那么您将运行
conda+install+-n+notebook_env+nb_conda_kernels
您希望在笔记本中访问的任何其他环境都必须安装适当的内核包。例如，要访问Python环境，它必须有ipykernel包；
conda+install+-n+python_env+ipykernel
要利用R环境，它必须具有r-irkernel包。
conda+install+-n+r_env+r-irkernel
对于其他语言，必须安装它们的对应核。|blockquote|BOLD|15439|然后，您需要做的就是启动jupyter笔记本服务器：|15440|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|code-block|syntax|javascript|15441|​|15442|📷|atomic|15443|15444|尽管答案太多，@merv也在努力改进它们，但仍然很难找到一个好答案。我做了这个CW，所以请投票到顶端或改进它！|15445|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/Anaconda-Platform/nb_conda_kernels#installation|1|https://github.com/jupyter/jupyter/wiki/Jupyter-kernels|2|IMAGE|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/2cf3f6e1129bb449d21eb1639a39991e.png|imageAlt^0|0|G|J|7|R|5|2N|2|0|0|0|2|1K|4|29|C|2Z|1A|5M|9|5Y|11|7P|X|91|3|1|0|0|0|0|0|1|2|0|0|0^^$0|@$1|2|3|4|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]]|D|@$9|1L|A|1M|1|1N]]|E|$]]|$1|F|3|G|5|H|7|1O|8|@$9|1P|A|1Q|B|I]|$9|1R|A|1S|B|C]|$9|1T|A|1U|B|C]|$9|1V|A|1W|B|C]|$9|1X|A|1Y|B|C]|$9|1Z|A|20|B|C]|$9|21|A|22|B|C]]|D|@$9|23|A|24|1|25]]|E|$]]|$1|J|3|K|5|6|7|26|8|@]|D|@]|E|$]]|$1|L|3|M|5|N|7|27|8|@]|D|@]|E|$O|P]]|$1|Q|3|R|5|6|7|28|8|@]|D|@]|E|$]]|$1|S|3|T|5|U|7|29|8|@]|D|@$9|2A|A|2B|1|2C]]|E|$]]|$1|V|3|R|5|6|7|2D|8|@]|D|@]|E|$]]|$1|W|3|X|5|6|7|2E|8|@]|D|@]|E|$]]|$1|Y|3|-4|5|6|7|2F|8|@]|D|@]|E|$]]]|Z|$10|$5|11|12|13|E|$14|15]]|16|$5|11|12|13|E|$14|17]]|18|$5|19|12|1A|E|$1B|1C|1D|-4]]]]

The <code>nb_conda_kernels</code> package is the best way to use <code>jupyter</code> with <code>conda</code>. With minimal dependencies and configuration, it allows you to use other conda environments from a jupyter notebook running in a different environment. Quoting its <a href="https://github.com/Anaconda-Platform/nb_conda_kernels#installation" rel="noreferrer">documentation</a>:

<blockquote>
 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 <code>base</code> conda environment, but it need not be. For instance, if the environment <code>notebook_env</code> contains the notebook package, then you would run
 
 <code>conda install -n notebook_env nb_conda_kernels</code> 
 
 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 <code>ipykernel</code> package; e.g.
 
 <code>conda install -n python_env ipykernel</code>
 
 To utilize an R environment, it
 must have the r-irkernel package; e.g.
 
 <code>conda install -n r_env r-irkernel</code> 
 
 For other languages, their <a href="https://github.com/jupyter/jupyter/wiki/Jupyter-kernels" rel="noreferrer">corresponding kernels</a> must be installed.
</blockquote>

Then all you need to do is start the jupyter notebook server:

<pre><code>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
</code></pre>

<a href="https://i.stack.imgur.com/L0KD9.png" rel="noreferrer"><img src="https://i.stack.imgur.com/L0KD9.png" alt="enter image description here"></a>

<hr>

Despite the plethora of answers and @merv's efforts to improve them, it still hard to find a good one. I made this one CW, so please vote it to the top or improve it!

blocks|key|207220|text|可能与频道有关的问题|type|unstyled|depth|inlineStyleRanges|entityRanges|data|207221|我有这个问题(再次)，结果是我安装了conda伪造通道；移除它并从anaconda通道重新安装，而不是为我修复它。|offset|length|style|BOLD|207222|Update：我在新的env上再次遇到了同样的问题，这一次我确实从anaconda通道安装了nb_conda_kernels，但是我的jupyter_client来自conda-forge通道。卸载nb_conda_kernels并将其重新安装到更高优先级的通道。|CODE|207223|因此，请确保您已从正确的通道安装:)|207224|entityMap^0|0|I|7|X|8|0|0|6|X|8|2B|B|1A|G|1V|E|2R|G|0|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|P|8|@$D|Q|E|R|F|G]|$D|S|E|T|F|G]]|9|@]|A|$]]|$1|H|3|I|5|6|7|U|8|@$D|V|E|W|F|G]|$D|X|E|Y|F|G]|$D|Z|E|10|F|G]|$D|11|E|12|F|J]|$D|13|E|14|F|J]|$D|15|E|16|F|J]]|9|@]|A|$]]|$1|K|3|L|5|6|7|17|8|@]|9|@]|A|$]]|$1|M|3|-4|5|6|7|18|8|@]|9|@]|A|$]]]|N|$]]

<h2>Possible Channel-Specific Issue</h2>

I had this issue (again) and it turned out I installed from the conda-forge channel; removing it and reinstalling from anaconda channel instead fixed it for me.

Update: I again had the same problem with a new env, this time I did install <code>nb_conda_kernels</code> from anaconda channel, but my <code>jupyter_client</code> was from the conda-forge channel. Uninstalling <code>nb_conda_kernels</code> and reinstalling updated that to a higher-priority channel.

So make sure you've installed from the correct channels :)

blocks|key|207299|text|我也遇到了类似的问题，我找到了一个适用于Mac、Windows和Linux的解决方案。上面的答案中所包含的关键要素不多：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|207300|为了能够在木星笔记本中看到conda+env，您需要：|207301|基本env中的以下包：
conda+install+nb_conda|unordered-list-item|offset|length|style|CODE|207302|所创建的每个env中的以下包：
conda+install+ipykernel|207303|检查jupyter_notebook_config.py的配置
首先检查jupyter_notebook_config.py是否位于jupyter+--paths给出的位置之一
如果它不存在，则通过运行jupyter+notebook+--generate-config创建它
添加或确保您有以下内容：c.NotebookApp.kernel_spec_manager_class='nb_conda_kernels.manager.CondaKernelSpecManager'|207304|您可以在终端中看到的env：|207305|📷|atomic|207306|​|207307|在木星实验室中，您可以看到与上面的笔记本和控制台相同的env：|207308|207309|207310|当你打开笔记本时，你可以选择你的env：|207311|207312|207313|安全的方法是创建一个特定的env，从中运行env实例jupyter+lab命令。启动你的env。然后添加jupyter实验室扩展示例jupyter实验室扩展。然后您可以运行jupyter+lab|207314|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/16a74264f475afa3bead3f47eed1a922.png|imageAlt|1|https://developer.qcloudimg.com/http-save/yehe-900000/c559736b276a0e3609c80ef2f067e13e.png|2|https://developer.qcloudimg.com/http-save/yehe-900000/304f933652a3f6951ecf00d6693b458f.png|3|LINK|MUTABLE|url|https://github.com/tarrade/proj_custom_ai_platform_notebook/blob/master/env/jupyter-notebook.yaml|4|https://github.com/tarrade/proj_custom_ai_platform_notebook/blob/master/script/jupyter_lab/install_jupyterlab_extension.sh^0|0|0|C|M|0|G|N|0|2|Q|10|Q|1U|F|2T|Y|47|2H|0|0|0|1|0|0|0|0|0|1|1|0|0|0|0|1|2|0|0|Q|B|2E|B|L|5|3|1S|E|4|0^^$0|@$1|2|3|4|5|6|7|1P|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|1Q|8|@]|9|@]|A|$]]|$1|D|3|E|5|F|7|1R|8|@$G|1S|H|1T|I|J]]|9|@]|A|$]]|$1|K|3|L|5|F|7|1U|8|@$G|1V|H|1W|I|J]]|9|@]|A|$]]|$1|M|3|N|5|F|7|1X|8|@$G|1Y|H|1Z|I|J]|$G|20|H|21|I|J]|$G|22|H|23|I|J]|$G|24|H|25|I|J]|$G|26|H|27|I|J]]|9|@]|A|$]]|$1|O|3|P|5|6|7|28|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|29|8|@]|9|@$G|2A|H|2B|1|2C]]|A|$]]|$1|T|3|U|5|6|7|2D|8|@]|9|@]|A|$]]|$1|V|3|W|5|6|7|2E|8|@]|9|@]|A|$]]|$1|X|3|R|5|S|7|2F|8|@]|9|@$G|2G|H|2H|1|2I]]|A|$]]|$1|Y|3|U|5|6|7|2J|8|@]|9|@]|A|$]]|$1|Z|3|10|5|6|7|2K|8|@]|9|@]|A|$]]|$1|11|3|R|5|S|7|2L|8|@]|9|@$G|2M|H|2N|1|2O]]|A|$]]|$1|12|3|U|5|6|7|2P|8|@]|9|@]|A|$]]|$1|13|3|14|5|6|7|2Q|8|@$G|2R|H|2S|I|J]|$G|2T|H|2U|I|J]]|9|@$G|2V|H|2W|1|2X]|$G|2Y|H|2Z|1|30]]|A|$]]|$1|15|3|-4|5|6|7|31|8|@]|9|@]|A|$]]]|16|$17|$5|18|19|1A|A|$1B|1C|1D|-4]]|1E|$5|18|19|1A|A|$1B|1F|1D|-4]]|1G|$5|18|19|1A|A|$1B|1H|1D|-4]]|1I|$5|1J|19|1K|A|$1L|1M]]|1N|$5|1J|19|1K|A|$1L|1O]]]]

I had similar issue and I found a solution that is working for Mac, Windows and Linux. It takes few key ingredients that are in the answer above:

To be able to see conda env in Jupyter notebook, you need: 

<ul>
<li>the following package in you base env: 
<code>conda install nb_conda</code> </li>
<li>the following package in each env you create: 
<code>conda install ipykernel</code> </li>
<li>check the configurationn of <code>jupyter_notebook_config.py</code> 
 first check if you have a <code>jupyter_notebook_config.py</code> in one of the location given by <code>jupyter --paths</code> 
 if it doesn't exist, create it by running <code>jupyter notebook --generate-config</code> 
 add or be sure you have the following: <code>c.NotebookApp.kernel_spec_manager_class='nb_conda_kernels.manager.CondaKernelSpecManager'</code></li>
</ul>

The env you can see in your terminal:
<a href="https://i.stack.imgur.com/Ojrp6.png" rel="noreferrer"><img src="https://i.stack.imgur.com/Ojrp6.png" alt="enter image description here"></a>

On Jupyter Lab you can see the same env as above both the Notebook and Console:
<a href="https://i.stack.imgur.com/w9FoI.png" rel="noreferrer"><img src="https://i.stack.imgur.com/w9FoI.png" alt="enter image description here"></a>

And you can choose your env when have a notebook open:
<a href="https://i.stack.imgur.com/FPNcp.png" rel="noreferrer"><img src="https://i.stack.imgur.com/FPNcp.png" alt="enter image description here"></a>

The safe way is to create a specific env from which you will run your <a href="https://github.com/tarrade/proj_custom_ai_platform_notebook/blob/master/env/jupyter-notebook.yaml" rel="noreferrer">example of env</a><code>jupyter lab</code> command. Activate your env. Then add jupyter lab extension <a href="https://github.com/tarrade/proj_custom_ai_platform_notebook/blob/master/script/jupyter_lab/install_jupyterlab_extension.sh" rel="noreferrer">example jupyter lab extension</a>. Then you can run <code>jupyter lab</code>

blocks|key|1935659|text|首先，你需要激活你的环境。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1935660|pip+install+ipykernel|code-block|syntax|javascript|1935661|接下来，您可以通过键入以下命令将虚拟环境添加到木星中：|1935662|python+-m+ipykernel+install+--name+=+my_env|1935663|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

First you need to activate your environment .
<pre><code>pip install ipykernel
</code></pre>
Next you can add your virtual environment to Jupyter by typing:
<pre><code>python -m ipykernel install --name = my_env
</code></pre>

blocks|key|15650|text|仅使用环境变量：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15651|python+-m+ipykernel+install+--user+--name+$(basename+$VIRTUAL_ENV)|code-block|syntax|javascript|15652|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Using only environment variables:
<pre><code>python -m ipykernel install --user --name $(basename $VIRTUAL_ENV)
</code></pre>

blocks|key|15658|text|我在使用vscode服务器时遇到了这个问题。在名为"base“的conda环境中，我安装了opennmt2的1.2.0版本，但我想在conda环境"opennmt2”中运行jupyter笔记本，其中包含使用opennmt2+2.0的代码。我通过在conda(opennmt2)中重新安装jupyter来解决这个问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15659|conda+install+jupyter|code-block|syntax|javascript|15660|重新安装后，在opennmt2环境中执行jupyter记事本将执行新安装的jupyter。|15661|where+jupyter+
/root/miniconda3/envs/opennmt2/bin/jupyter
/root/miniconda3/bin/jupyter|15662|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

I encountered this problem when using vscode server.
In the conda environment named &quot;base&quot;, I installed the 1.2.0 version of opennmt-py, but I want to run jupyter notebook in the conda environment &quot;opennmt2&quot;, which contains code using opennmt-py 2.0.
I solved the problem by reinstalling jupyter in conda(opennmt2).
<pre><code>conda install jupyter
</code></pre>
After reinstalling, executing jupyter notebook in the opennmt2 environment will execute the newly installed jupyter
<pre><code>where jupyter 
/root/miniconda3/envs/opennmt2/bin/jupyter
/root/miniconda3/bin/jupyter
</code></pre>

blocks|key|15716|text|这是一个旧线程，但是在Anaconda提示符中运行它，在我感兴趣的环境中，为我工作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|15717|ipython+kernel+install+--name+"myenvname"+--user|code-block|syntax|javascript|15718|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

This is an old thread, but running this in Anaconda prompt, in my environment of interest, worked for me:
<pre><code>ipython kernel install --name &quot;myenvname&quot; --user
</code></pre>

blocks|key|207424|text|我只想补充一下前面的答案:万一安装nb_conda_kernels、ipywidgets和ipekernel不起作用，请确保您的木星版本是最新的。一段时间后，我的env突然停止出现，在我通过anaconda导航器更新jupyter之后，它恢复了工作。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|207425|entityMap^0|H|G|Y|A|19|9|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|O|8|@]|D|@]|E|$]]]|G|$]]

I just wanted to add to the previous answers: in case installing <code>nb_conda_kernels</code>, <code>ipywidgets</code> and <code>ipekernel</code> dosen't work, make sure your version of Jupyter is up to date. My envs suddenly stopped showing up after a period of everything working fine, and it resumed working after I simply updated jupyter through the anaconda navigator.

blocks|key|1935775|text|对于conda+4.5.12，对我有用的是(我的虚拟env称为nwt)|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1935776|conda+create+--name+nwt+python=3|code-block|syntax|javascript|1935777|之后，我需要激活虚拟环境并安装ipykernel。|1935778|activate+nwt
pip+install+ipykernel|1935779|那么对我有用的是：|1935780|python+-m+ipykernel+install+--user+--name+env_name+--display-name+"name+of+your+choosing."|1935781|例如，我使用'nwt‘作为虚拟env的显示名称。在运行上面的命令之后。再次在Anaconda提示符中运行“jupyter记事本”。我得到的是：|1935782|📷|atomic|offset|length|1935783|​|1935784|entityMap|0|IMAGE|mutability|IMMUTABLE|imageUrl|https://developer.qcloudimg.com/http-save/yehe-900000/1e856ea986a27b58d496e4859c54b1f1.png|imageAlt^0|0|0|0|0|0|0|0|0|1|0|0|0^^$0|@$1|2|3|4|5|6|7|16|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|17|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|18|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|19|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|1A|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|1B|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|1C|8|@]|9|@]|A|$]]|$1|Q|3|R|5|S|7|1D|8|@]|9|@$T|1E|U|1F|1|1G]]|A|$]]|$1|V|3|W|5|6|7|1H|8|@]|9|@]|A|$]]|$1|X|3|-4|5|6|7|1I|8|@]|9|@]|A|$]]]|Y|$Z|$5|10|11|12|A|$13|14|15|-4]]]]

For conda 4.5.12, what works for me is (my virtual env is called nwt)
<pre><code>conda create --name nwt python=3
</code></pre>
after that I need to activate the virtual environment and install the ipykernel
<pre><code>activate nwt
pip install ipykernel
</code></pre>
then what works for me is:
<pre><code>python -m ipykernel install --user --name env_name --display-name &quot;name of your choosing.&quot;
</code></pre>
As an example, I am using 'nwt' as the display name for the virtual env. And after running the commands above. Run 'jupyter notebook&quot; in Anaconda Prompt again. What I get is:
<a href="https://i.stack.imgur.com/VJBb6.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/VJBb6.png" alt="new kernel" /></a>

I installed Anaconda (with Python 2.7), and installed Tensorflow in an environment called <code>tensorflow</code>. I can import Tensorflow successfully in that environment.

The problem is that Jupyter Notebook does not recognize the new environment I just created. No matter I start Jupyter Notebook from the GUI Navigator or from the command line within the <code>tensorflow</code> env, there is only one kernel in the menu called <code>Python [Root]</code>, and Tensorflow cannot be imported. Of course, I clicked on that option multiple times, saved file, re-opened, but these did not help.

Strangely, I can see the two environments when I open the <code>Conda</code> tab on the front page of Jupyter. But when I open the <code>Files</code> tab, and try to <code>new</code> a notebook, I still end up with only one kernel.

I looked at this question:
<a href="https://stackoverflow.com/questions/37433363/link-conda-environment-with-jupyter-notebook">Link Conda environment with Jupyter Notebook</a>
But there isn't such a directory as <code>~/Library/Jupyter/kernels</code> on my computer! This Jupyter directory only has one sub-directory called <code>runtime</code>.

I am really confused. Are Conda environments supposed to become kernels automatically? (I followed <a href="https://ipython.readthedocs.io/en/stable/install/kernel_install.html">https://ipython.readthedocs.io/en/stable/install/kernel_install.html</a> to manually set up the kernels, but was told that <code>ipykernel</code> was not found.)

Conda environments not showing up in Jupyter Notebook

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

我安装了Anaconda (使用Python2.7)，并在一个名为tensorflow的环境中安装了Tensorflow。我可以在那个环境中成功地导入Tensorflow。问题是，朱庇特笔记本不承认我刚刚创造的新环境。无论我从GUI启动朱庇特笔记本还是从命令行启动tensorflow env，菜单中只有一个名为Pyth...

问Conda环境未出现在木星笔记本中
EN

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Conda环境未出现在木星笔记本中EN

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问Conda环境未出现在木星笔记本中
EN