![]() This feature enables you to have multiple sessions running at the same time. We have recently deployed an upgrade that enables Jupyterhub Named Servers. It is still good practice to shut down your session(s) (see step 7 above) when you are done to make those resources available to other users. Note that with the new Named Servers feature (see below), you no longer need to exit your interactive session to start the second shorter-lived session. ![]() A good approach is to use a general interactive session to develop and test your workflow, and then start a more resource-heavy session if needed to run your workflow. The lower options request more scarce resources - larger amounts of memory or CPU cores, or GPU accelerators - and as such request a shorter time limit of only four hours. The first session options in the list are appropriate for starting a long-lived session that you will use all day for any type of computational work. K40 GPU session - 12 cores, 60 GB, 4 hour, 100 GB local scratch, 1 K40 GPU.Agate A40 GPU session - 16 cores, 60 GB, 4 hour, 80 GB local scratch, 1 A40 GPU.Mangi AMD wide - 24 cores, 45 GB, 4 hours, 75 GB local scratch.This session and the ones below are limited to 4 hours to help the scheduler quickly meet these larger resource requests. This reserves one half of a ram256g node. Mesabi High-mem - 12 cores, 128 GB, 4 hours, 180 GB local scratch.Agate Interactive - 4 cores, 32 GB, 12 hours, 64 GB local scratchĪ basic interactive session on an Agate node also appropriate for all-day computing, but requests a shorter time limit as this newer hardware is in higher demand.With a long time limit and small resource request, it is appropriate for all-day general computing tasks. This is the default session type it runs on a standard Mesabi node and will generally start up quickly. Basic Interactive - 3 cores, 8 GB, 24 hours, 48 GB local scratch.The "Server Options" form allows you to select the resources requested for that job. Behind the scenes a job is created and scheduled to run for you. Your session is provided by a Jupyter Notebook server running on your behalf on a scheduled HPC resource. Unless you end your session, your server will continue running and consuming resources until the requested time runs out, even if you log out or close your web browser. From the "File" menu at the top of the work area, select "Hub Control Panel" and press "Stop My Server". When you are done working, you should end your session to release the compute resources for other users.You can reconnect to your running session by logging in to (including from another computer or a different location). Your session is hosted by a running job, and will continue running if you close your web browser or even log out.You can browse, open, and manage files via the directory view in the left sidebar. You can start new notebooks, command line terminals, and other resources by clicking icons in the launcher. Once your session starts, you will be redirected to the JupyterLab interface.Usually your Jupyter Notebooks server will start in under a minute, but during busy periods you may have to wait up to several minutes, especially if you have selected one of the larger job types. On the "Server Options" page, select a job type (see below for additional information) and click "Start" to start your session.If your Jupyter Notebooks server is already running, you will be redirected directly to your running session. If you do not already have a running Jupyter session, you will be prompted to "Start My Server".You will be asked to select your group if you belong to more than one group. You will be prompted to log in with your University InternetID. If you are off-campus you will need to connect through the University VPN. You do not need to install any special software. You can connect to the Jupyter Notebooks service using any modern web browser. There are many excellent tutorials and videos online that explain how to use Jupyter Notebooks. MSI Python tutorial materials provide a useful overview. Notebooks currently supports both the JupyterLab and Classic Notebook interfaces, computation with the Python (version 2 and 3) and R languages, and can also interoperate with user-installed Jupyter environments. This interactive computing environment requires only a web browser, and enables data analysis and visualization on our HPC resources in a shareable, reproducible notebook format. Jupyter Notebooks are available to all users at.
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