Posts in Jupyterhub

How to re-enable the deprecated python kernels?

As you propably know, we will rename/remove some unused/outdated/ python modules, please see the details here. Since the jupyterhub kernels are based on modules, the deprecated kernels will no longer be available as default kernels in jupyter notebooks/labs.

NO PANIC, if you have been working with those deprecated kernels and want to continue using them in your notebooks, please follow the steps below.

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Deprecated Python environments

Original post.

Since several years, we are offering Python environments on Mistral. Many of them are not updated any more and should not be used for new development. However, older scripts may rely on these environments and the versions of their installed packages.

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How to install R packages in different locations?

The default location for R packages is not writeble and you can not install new packages. On demand we install new packages system-wide and for all users. However, it possible to install packages in different locations than root and here are the steps:

create a directory in $HOME e.g. ~/R/libs

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How to install jupyter kernel for Matlab

In this tutorial, I will describe the steps I followed to get the matlab_kernel working in Jupyterhub on Mistral. Most of the instructions are based on these posts 1, 2.

Unfortunately, it is assumed that you have Matlab on your local computer. If you just follow their steps, you will end up with:

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Requested MovieWriter (ffmpeg) not available

Do you want to create videos / animations with ffmpeg from your jupyter notebook? you need ffmpeg-python (conda) which requires ffmpeg software on Mistral (module)

conda env with ffmpeg-python and ipykernel

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How to containerIze your jupyter kernel?

We have seen in this blog post how to encapsulate a jupyter notebook (server) in a singularity container . In this tutorial, I am going to describe how you can run a jupyter kernel in a container and make it available in the jupyter*.

Possible use case for this is to install a supported PyTorch version and work with jupyter notebooks (see GLIBC and the container-based workaround).

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Create a kernel from your own Julia installation

If you are using your own Julia installation following (correctly) the instructions described here:

you can use it in Jupyterhub.

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Connect Spyder IDE to a remote kernel on Mistral

I am just describing spontaneously what worked for me to connect my local Spyder instance to a remote node on Mistral THAT YOU CAN CONNECT TO VIA SSH FROM YOUR LOCAL MACHINE!!!!

This is just a draft tutorial that will be updated/optimized afterwards.


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Python environment locations

Kernels are based on python environments created with conda, virtualenv or other package manager. In some cases, the size of the environment can tremendously grow depending on the installed packages. The default location for python files is the $HOME directory. In this case, it will quickly fill your quota. In order to avoid this, we suggest that you create/store python files in other directories of the filesystem on Mistral.

The following are two alternative locations where you can create your Python environment:

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How to quickly create a test kernel

This is a follow up on Kernels. In some cases, the process of publishing new Python modules can take long. In the meantime, you can create a test kernel to use it in Jupyterhub. Creating new conda environments and using them as kernels has been already described here. In this example, we are not going to create a new conda env but only the kernel configuration files.

in this tutorial, I will take the module python3/2021-01. as an example.

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CF Python package added to the software tree

According to this link:

The Python cf package is an Earth Science data analysis library that is built on a complete implementation of the CF data model. The cf package implements the CF data model 1 for its internal data structures and so is able to process any CF-compliant dataset. It is not strict about CF-compliance, however, so that partially conformant datasets may be ingested from existing datasets and written to new datasets. This is so that datasets that are partially conformant may nonetheless be modified in memory.

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SLURM update / Memory use

Slurm config on Mistral has been updated to fix an issue related to memory use.

Prior the update, some Slurm jobs continue consuming the available memory (and even swap) of the allocated node and exceed the allocated memory (set in sbatch or srun). If this occurs, it also affect other jobs/users.

error message

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