Ipyparallel establishes a cluster consisting of one controller and multiple engines. If you’re not familiar with this plugin, it is a child module of iPython, which has grown into Jupyter notebooks. For that, we chose an old trusted friend from the Python and HPC community: ipyparallel. So we decided to go for another, more “code interactive” approach. Approach two: parallelizable with more interactivity This can be overcome, but was not within the scope of the project. For example, it is certainly possible that a user forgets to shut down their notebook and upholds a portion of the resources on the HPC unknowingly. Additionally, an HPC system administrator will always require users to do certain operations within the infrastructure themselves, as there are risks involved with spawning these notebooks directly on an HPC. However, the starting time of these notebooks could take up quite some time. We encapsulated every notebook within a container such as Docker or Singularity, to capture content like environmental variables, and this container was than spawned across several nodes on an HPC. Our first approach was to run every single notebook as a separate HPC job, assuming that HPC provides more computational power than the end-user’s machine, so the limits of the analytics that you can do within a notebook expand dramatically. So what approaches did we try and what have we learned from them Approach one: just run it (on a more powerful machine)
#Jupyterhub vs jupyterlab how to
Through the integration with an HPC, more powerful analytics can be done, and users do not have to lose significant time learning how to run analyses on the HPC. JupyterHub provides an easy, interactive, experimental, almost playful way of analysing data. Another application - which is rapidly becoming indispensable - is JupyterHub. HPCs have become an essential addition to the resources of a bioinformatician when it comes to tackling the issue of extra computing power for big datasets analysis. In this blogpost I want to share with you the knowledge we gained throughout the execution of this project. At the time when we began this project, JupyterLab was still in alpha state, therefore we sadly could not trust it enough to deliver a reliable solution for our customer and had to limit ourselves to JupyterHub. So why am I telling you this? For a recent project we looked into installing, integrating and configuring JupyterHub and JupyterLab for one of our customers.
or you will end up with a serious coffee addiction before it is finished. I can almost guarantee you it will not go well. Try doing some calculation on that with your own laptop. Genomics data nowadays can easily take up more than 1TB. HPCs are becoming more necessary every day as the amount of data which scientists, analysts and statisticians use is growing rapidly. HPC? This abbreviation stands for High Performance Computing, for some also known as a “supercomputer”, although a supercomputer is only one type of HPC.įor the non technical readers, you can compare a HPC to a lot of computers connected to each other by a tool that distributes the jobs and workload over these computers. What made this project so interesting to us, was the fact that we needed to integrate Jupyter notebooks with an HPC.
#Jupyterhub vs jupyterlab install
The benefits are obvious: users don’t need to install Jupyter locally and can access it from any computer. To overcome these limitations, Jupyter community has created JupyterHub, a multi-user web-based hosting solution for Jupyter. Also, the computational power of a locally installed Jupyter is inherently limited and the user’s notebooks and data are tied to a certain machine. Although it is possible to install Jupyter on an end-user’s machine, its installation and configuration require a lot of expertise. Jupyter is an interactive computational notebook, actually, a programming environment, which allows you to see the results of the computation nearly as soon as you type (think about Excel but with extra power, like Excel on steroids). Even though JupyterLab is unfortunately still in beta version, it already looks very promising: it promises to be more flexible, extensive and have better “app” support, so you can adjust it to your specific needs more easily. And the researchers who are using Jupyter a lot may know that there is a new edition coming, JupyterLab.
Others may already know the server version of this application: JupyterHub. Some of you may already know the popular notebook application Jupyter. To improve the analysis power for one of our customers, we decided to bring together the following two components: JupyterHub and HPC.
I am not talking about storing the data but about having the ability to perform computationally intensive analysis on these large data sets.
These days, computers need to be able to cope with the vast amounts of data that the Life Science sector is constantly generating.
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