http://node_name.int.cluster.computecanada.ca:8888/lab?token=101c3688298e78ab554ef86d93a196deaf5bcd2728fad4eb

1. Once JupyterLab is launched on a compute node (see [[#Starting_JupyterLab|Starting JupyterLab]]), you can extract the hostname:port and the token from the first provided HTTP address. For example:

   http://node_name.int.cluster.computecanada.ca:8888/lab?token=101c368829...2728fad4eb
          └────────────────────┬────────────────────┘           └──────────┬──────────┘
                         hostname:port                                   token
   

2. Open a new Terminal tab in MobaXTerm. In the following command, replace with its corresponding value (refer to the above figure), replace with your Alliance account username, and replace with the cluster on which you have launched JupyterLab:{{Command2 |prompt=[name@local ~]$ |ssh -L 8888: @.computecanada.ca }}
3. Open your web browser and go to the following address where must be replaced with the alphanumerical value extracted from the above figure:

   http://localhost:8888/?token=
   

1. Load the [[Julia]] module:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |module load julia }}
2. Install IJulia:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |echo -e 'using Pkg\nPkg.add("IJulia")' {{!}} julia }}
3. Important: start or restart a new JupyterLab session before using the Julia kernel.

1. Make sure the same Julia module is loaded:{{Command2 |module load julia }}
2. Install any required package. For example with Glob:{{Command2 |echo -e 'using Pkg\nPkg.add("Glob")' {{!}} julia }}
3. The newly installed Julia packages should already be usable in a notebook executed by the Julia kernel.

1. If you do not have a Python virtual environment, create one. Then, activate it:
1. Start from a clean Bash environment (this is only required if you are using the Jupyter Terminal via [[JupyterHub]] for the creation and configuration of the Python kernel):{{Command2 |env -i HOME{{=}}$HOME bash -l }}
2. Load a Python module:{{Command2 |module load python }}
3. Create a new Python virtual environment:{{Command2 |virtualenv --no-download $HOME/jupyter_py3 }}
4. Activate your newly created Python virtual environment:{{Command2 |source $HOME/jupyter_py3/bin/activate }}
2. Create the common kernels folder, which is used by all kernels you want to install:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |mkdir -p ~/.local/share/jupyter/kernels }}
3. Finally, install the Python kernel:
   1. Install the ipykernel library:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |pip install --no-index ipykernel }}
   2. Generate the kernel spec file. Replace with a name that will uniquely identify your kernel:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |python -m ipykernel install --user --name --display-name "Python 3.x Kernel" }}
4. Important: start or restart a new JupyterLab session before using the Python kernel.

1. If you are using the Jupyter Terminal via [[JupyterHub]], make sure the activated Python virtual environment is running in a clean Bash environment. See the above section for details.
2. Install any required library. For example, numpy:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |pip install --no-index numpy }}
3. The newly installed Python libraries can now be imported in any notebook using the Python 3.x Kernel.

1. Load an R module:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |module load r/4.1 }}
2. Install the R kernel dependencies (crayon, pbdZMQ, devtools) - this will take up to 10 minutes, and packages should be installed in a local directory like ~/R/x86_64-pc-linux-gnu-library/4.1:{{Command2 |prompt=(jupyter_py3) [name@server ~]$ |R --no-save |result=> install.packages(c('crayon', 'pbdZMQ', 'devtools'), repos{{=}}'http://cran.us.r-project.org') }}
3. Install the R kernel.{{Command2 |prompt=> |devtools::install_github(paste0('IRkernel/', c('repr', 'IRdisplay', 'IRkernel'))) }}
4. Install the R kernel spec file.{{Command2 |prompt=> |IRkernel::installspec() }}
5. Important: Start or restart a new JupyterLab session before using the R kernel.

1. Make sure the same R module is loaded:{{Command2 |module load r/4.1 }}
2. Start the R shell and install any required package. For example with doParallel:{{Command2 |R --no-save |result=> install.packages('doParallel', repos{{=}}'http://cran.us.r-project.org') }}
3. The newly installed R packages should already be usable in a notebook executed by the R kernel.