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updated python env description and added short docu for user envs

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feichtinger
2019-09-04 17:18:22 +02:00
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pages/merlin6/04 jupyterhub/jupyterhub.md
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@ -40,7 +40,7 @@ The service is available inside of PSI (or through a VPN connection) at
Your notebooks can run within different software environments which are offered by a number of available **Jupyter kernels**.
E.g. in this test installation we provide two environments targeted at data science
* **tensorflow-1.13.1_py36**: contains Tensorflow, Keras, scikit-learn, Pandas, numby, and dependencies. Stable
* **tensorflow-1.13.1_py37**: contains Tensorflow, Keras, scikit-learn, Pandas, numpy, dask, and dependencies. Stable
* **talos_py36**: also contains the Talos package. This
environment is experimental and subject to updates and changes.
@ -60,6 +60,21 @@ When the `anaconda` module has been loaded, you can list the available environme
conda info -e
```
You can get more info on the use of the `conda` package management tool at its official [https://conda.io/projects/conda/en/latest/commands.html](documentation site).
## Using your own custom made environments with jupyterhub
Python environments can take up a lot of space due to the many dependencies that will be installed. You should always install your extra environments to the data area belonging to your account, e.g. `/data/user/${YOUR-USERNAME}/conda-envs`
In order for jupyterhub (and jupyter in general) to recognize the provided environment as a valid kernel, make sure that you include the `nb_conda_kernels` package in your environment. This package provides the necessary activation and the dependencies.
Example:
```
conda create -c conda-forge -p /data/user/${USER}/conda-envs/my-test-env python=3.7 nb_conda_kernels
```
After this, your new kernel will be visible as `my-test-env` inside of your jupyterhub session.
## Using nbextensions for adding features to your notebook