Warning

Please make sure to save your work frequently in case a shutdown happens.

JupyterHub on Triton User Menu

Introduction

JupyterHub provides Jupyter Notebook for multiple users.

Through JupyterHub on Triton, you can request and start a Jupyter Notebook server on one of Triton’s compute nodes (using LSF job scheduler behind the scenes). In this way, you can interactively test your Python or R programs through the Notebook with the supercomputer resources.

Currently all requested Notebook servers are running in only one compute node. It is recommended to use the Notebook as a testing tool and submit formal jobs via LSF.

Using JupyterHub on Triton

Login

Starting your Jupyter Notebook server

  • Press the Start My Notebook Server button to launch the resource request page.
  • Choose the memory, number of CPU cores, time you want to run the Notebook server and whether or not you want to use a GPU.
  • Press the Request button to request and start a Notebook server.

Logout

When using the JupyterHub, you need to be clear that there are three things you need to turn off:

  1. Close Notebook File - After saving, press File in the menu bar and choose Close and Halt.
  2. Stop Notebook Server - Click the Control Panel button at the top-right corner and press Stop My Notebook Server.
  3. Logout from JupyterHub - Click the Logout from JupyterHub button at the top-right corner.

Warning

If you only logout from JupyterHub without stopping the Notebook Server first, the Notebook Server will run until the time you set up when starting it. This could result in unintended increased SU usage.

Using Jupyter Notebook

After the notebook server starts, you will see the interface page showing your home directory.

You can create notebook files, text files and folders, or open terminals using the New button at the top-right corner under the menu bar.

Details can be found at the official Jupyter Notebook User Documentation.

Creating Your Python Kernel

  • $ ssh <caneid>@triton.ccs.miami.edu to login to Triton
  • $ ml anaconda3 or ml wml_anaconda3 if you need to install deep learning packages
  • $ conda create -n <your environment> python=<version> <package1> <package2> ...
  • $ conda activate <your environment>
  • (your environment)$ conda install ipykernel
  • (your environment)$ ipython kernel install --user --name <kernel name> --display-name "<the displayed name for the kernel>"

Here is an example:

(Please press y on your keyboard when you see Proceed ([y]/n)?)

$ ml anaconda3
$ conda create -n myenv python=3.7 numpy scipy
$ conda activate myenv
(myenv)$ conda install ipykernel
(myenv)$ ipython kernel install --user --name my_py37_kernel --display-name "My Python 3.7 with NumPy and SciPy"

Later on, you can still install new packages to the kernel using conda install <package> after activating the environment.

Note

If the package could not be found, you can search Anaconda Cloud and choose Platform linux-ppc64le

If Anaconda Cloud does not have the package neither, you could try pip install

Warning

Issues may arise when using pip and conda together. Only after conda has been used to install as many packages as possible should pip be used to install any remaining software. If modifications are needed to the environment, it is best to create a new environment rather than running conda after pip.

After a package is installed, you can use it in your notebook by running import <package name> in a cell.

Creating Your R kernel

  • $ ml anaconda3
  • $ conda create -n <your r environemnt> -c conda-forge r-base
  • $ conda activate <your r environemnt>
  • $ conda install -c conda-forge jupyter_client
  • (<your r environemnt>)$ R
  • (inside R) > install.packages(c('repr', 'IRdisplay', 'IRkernel'))
  • (inside R) > IRkernel::installspec(name='<your r kernel name>', displayname='<display name of your kernel>')

Later on, you can still install new R packages to the kernel by activating the environment, entering R and running install.packages('<package name>') (The pacakge will be installed at /~/.conda/envs/<your r environment>/lib/R/library)

After a R package is installed, you can use it in your notebook by running library('<package name>') in a cell.

Removing Personal Kernels

You can view a list of all your kernels at the following path:

/home/<your_caneid>/.local/share/jupyter/kernels

From this directory you can delete kernels using Linux rm kernel_name command.

Using Pre-installed Kernels

Several kernels has been pre-installed on Triton. You can use them to test your code if you do not need additional packages. On the Notebook Dashboard page, you can create a new notebook file (.ipynb) with a selected kernel by clicking on the New button at the top-right corner under the menu bar. On the Notebook Editor page, you can change kernel by clicking Kernel in the menubar and choosing Change kernel.

  • Python 2.7 and Python 3.7 kernels are the Anaconda2 2019.07 and Anaconda3 2019.07 base environments. Each of them has over 150 packages automatically installed.
  • WML CE kernels have the IBM Watson Machine Learning Community Edition packages. (You can check different versions by changing the Releases version in the Filters bar on the website.)
  • R kernel includes the R Base Package.

Switching to JupyterLab

After the Jupyter Notebook server starts, you can switch to JupyterLab by changing the url from .../tree to .../lab. If you want to stop the server from JupyterLab, choose File >> Hub Control Panel in the menu bar, then press Stop My Notebook Server button in the panel.